diff --git a/.veda/ui b/.veda/ui
index 774d08621d..e4573ea439 160000
--- a/.veda/ui
+++ b/.veda/ui
@@ -1 +1 @@
-Subproject commit 774d08621dcc5d787859d61c5fbad3f5562fa9cd
+Subproject commit e4573ea43986c0ac61d2d831ec512a8e69a4a78a
diff --git a/custom-pages/wildfire-explorer/index.css b/custom-pages/wildfire-explorer/index.css
new file mode 100644
index 0000000000..6b388a25e4
--- /dev/null
+++ b/custom-pages/wildfire-explorer/index.css
@@ -0,0 +1,3 @@
+.usa-site-alert {
+ margin-top: 2.5rem;
+}
\ No newline at end of file
diff --git a/custom-pages/wildfire-explorer/index.mdx b/custom-pages/wildfire-explorer/index.mdx
index 6f69d554e0..1f107bf904 100644
--- a/custom-pages/wildfire-explorer/index.mdx
+++ b/custom-pages/wildfire-explorer/index.mdx
@@ -3,7 +3,7 @@ hideNav: true
hideHero: true
hideFooter: true
---
-
+import './index.css';
import Component from './component.tsx';
diff --git a/datasets/CMIP-winter-median-precip-temp.data.mdx b/datasets/CMIP-winter-median-precip-temp.data.mdx
deleted file mode 100644
index d6709e43b4..0000000000
--- a/datasets/CMIP-winter-median-precip-temp.data.mdx
+++ /dev/null
@@ -1,281 +0,0 @@
----
-id: CMIP-winter-median-pr
-name: 'Projections of Western United States Winter Conditions'
-description: "CMIP6 projections of changes to air temperature and winter cumulative precipitation"
-media:
- src: ::file media/CMIP-winter-median.jpeg
- alt: Photo of Nisqually glacier
- author:
- name: Justin Pflug
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Snow
- - Precipitation
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA EIS
- - CMIP6
-infoDescription: |
- ::markdown
- Future changes to air temperature are expected to influence the phase of winter precipitation (snowfall or rainfall) and the timing and amount of snowmelt and streamflow, while changes to precipitation are expected to alter the volume and timing of snow water resources. Here, we present the projected percent-change to Western US air tempeature and cumulative winter precipitation at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2095. Projections are averaged from an ensemble of 23 downscaled climate models from the CMIP6 NASA Earth Exchange Global Daily Downscaled Projections.
-layers:
- - id: CMIP245-winter-median-pr
- stacCol: CMIP245-winter-median-pr
- name: 'Percent-change to winter cumulative precipitation, SSP2-4.5'
- type: raster
- description: 'Percent difference in projected winter (January, February, March) cumulative precipitation, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 2-4.5) with downscaling performed by NASA Earth Exchange'
- sourceParams:
- resampling: bilinear
- bidx: 1
- nodata: nan
- colormap_name: rdbu
- rescale:
- - -60
- - 60
- compare:
- datasetId: CMIP-winter-median-pr
- layerId: CMIP245-winter-median-pr
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- label: Precipitation percent-difference
- min: "-60%"
- max: "+60%"
- stops:
- - "#CA171C"
- - "#DE6158"
- - "#F2B089"
- - "#F5D5C3"
- - "#F8F8F8"
- - "#CDE2EF"
- - "#A0CBE4"
- - "#5EA4D1"
- - "#207BBD"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
- - id: CMIP585-winter-median-pr
- stacCol: CMIP585-winter-median-pr
- name: 'Percent-change to winter cumulative precipitation, SSP5-8.5'
- type: raster
- description: 'Percent difference in projected winter (January, February, March) cumulative precipitation, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 5-8.5) with downscaling performed by NASA Earth Exchange'
- sourceParams:
- resampling: bilinear
- bidx: 1
- nodata: nan
- colormap_name: rdbu
- rescale:
- - -60
- - 60
- compare:
- datasetId: CMIP-winter-median-pr
- layerId: CMIP585-winter-median-pr
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- label: Precipitation percent-difference
- min: "-60%"
- max: "+60%"
- stops:
- - "#CA171C"
- - "#DE6158"
- - "#F2B089"
- - "#F5D5C3"
- - "#F8F8F8"
- - "#CDE2EF"
- - "#A0CBE4"
- - "#5EA4D1"
- - "#207BBD"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
-
- - id: CMIP245-winter-median-ta
- stacCol: CMIP245-winter-median-ta
- name: 'SSP2-4.5, Change to winter average air temperature'
- type: raster
- description: 'Difference in projected winter (January, February, March) average air temperature, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 2-4.5) with downscaling performed by NASA Earth Exchange'
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: rdbu_r
- rescale:
- - -5.5
- - 5.5
- compare:
- datasetId: CMIP-winter-median-ta
- layerId: CMIP245-winter-median-ta
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: °C
- type: gradient
- label: Air temperature difference [C]
- min: "-5.5"
- max: "+5.5"
- stops:
- - "#207BBD"
- - "#5EA4D1"
- - "#A0CBE4"
- - "#CDE2EF"
- - "#F8F8F8"
- - "#F5D5C3"
- - "#F2B089"
- - "#DE6158"
- - "#CA171C"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
-
- - id: CMIP585-winter-median-ta
- stacCol: CMIP585-winter-median-ta
- name: 'SSP5-8.5, Change to winter average air temperature'
- type: raster
- description: 'Difference in projected winter (January, February, March) average air temperature, relative to a historical timeframe between 1995 and 2014. Outputs represent the median of 23 member ensembles from CMIP6 (SSP 5-8.5) with downscaling performed by NASA Earth Exchange'
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: rdbu_r
- rescale:
- - -5.5
- - 5.5
- compare:
- datasetId: CMIP-winter-median-ta
- layerId: CMIP585-winter-median-ta
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: °C
- type: gradient
- label: Air temperature difference [C]
- min: "-5.5"
- max: "+5.5"
- stops:
- - "#207BBD"
- - "#5EA4D1"
- - "#A0CBE4"
- - "#CDE2EF"
- - "#F8F8F8"
- - "#F5D5C3"
- - "#F2B089"
- - "#DE6158"
- - "#CA171C"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2025-2085
- - **Spatial Extent:** Western United States
- - **Spatial Resolution:** 25 kilometers
- - **Data Units:** Percentage (%)
- - **Data Type:** Research
-
-
-
-
- Median projected percent-changes to end-of-century total winter precipitation from a 23-member ensemble of CMIP6 models (SSP2-4.5).
-
-
-
-
-
-
-
- ## About
- Future changes to air temperature are expected to influence the phase of winter precipitation (snowfall or rainfall) and the timing and amount of snowmelt and streamflow. Future changes to precipitation are expected to alter the volume and timing of snow water resources. Here, we present the projected percent-change to Western US air temperature and cumulative winter precipitation at quarter-degree spatial resoutions across 20-year time periods between 2016 and 2085. Projections are averaged from an ensemble of 23 downscaled climate models from the [CMIP6 NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).
-
- We have examined two different Shared Socioeconomic Pathways (SSPs) which describe potential future scenarios based on decisions in climate policies and actions as well as human infrastructure and governance. We have gathered the SSP2-4.5 which represents an intermediate radiative forcing pathway and the SSP5-8.5 representing a future with a high radiative forcing.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Future Projections of Western US Montane Snowpack**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional Resources
-
- [EIS Freshwater](https://freshwater.eis.smce.nasa.gov/)
- [Global Daily Downscaled Projections](https://www.nasa.gov/nex/gddp)
- [Land Information System](https://lis.gsfc.nasa.gov/)
- [Shared Socioeconomic Pathways](https://earth.gov/sealevel/faq/124/what-are-shared-socioeconomic-pathways-or-ssps/#:~:text=Shared%20Socioeconomic%20Pathways%20(SSPs)%20are,gas%20emissions%20and%20climate%20change.)
-
-
-
diff --git a/datasets/CMIP_days_above_threshold_ssp.data.mdx b/datasets/CMIP_days_above_threshold_ssp.data.mdx
deleted file mode 100644
index 630f1ecc23..0000000000
--- a/datasets/CMIP_days_above_threshold_ssp.data.mdx
+++ /dev/null
@@ -1,121 +0,0 @@
----
-id: climdex-tmaxxf-access-cm2
-isHidden: false
-name: 'CMIP Days above 90 degrees climdex-tmaxxf-access-cm2'
-description: "CMIP Days above 90 degrees F climdex-tmaxxf-access-cm2"
-media:
- src: ::file media/CMIP-winter-median.jpeg
- alt: Photo of Nisqually glacier
- author:
- name: Justin Pflug
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Snow
- - Precipitation
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA EIS
- - CMIP6
-infoDescription: |
- ::markdown
- CMIP Days above 90 degrees climdex-tmaxxf-access-cm2
-layers:
-
- - id: climdex-tmaxxf-access-cm2-585
- stacCol: climdex-tmaxxf-access-cm2-ssp585
- name: 'Days above 90 Degrees F SSP585'
- type: raster
- description: 'Days above 90 degrees F'
- sourceParams:
- assets: tmax_above_90
- resampling: bilinear
- bidx: 1
- nodata: nan
- colormap_name: ylorrd
- rescale:
- - 0
- - 365
- legend:
- unit:
- label: Days
- type: gradient
- min: 0
- max: 365
- stops:
- - "#E4FF7A"
- - "#FAED2D"
- - "#FFCE0A"
- - "#FFB100"
- - "#FE9900"
- - "#FC7F00"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Annual
- unit: Days
-
-
- - id: climdex-tmaxxf-access-cm2-126
- stacCol: climdex-tmaxxf-access-cm2-ssp126
- name: 'Days above 90 Degrees F SSP126'
- type: raster
- description: 'Days above 90 degrees F'
- sourceParams:
- assets: tmax_above_90
- resampling: bilinear
- bidx: 1
- nodata: nan
- colormap_name: ylorrd
- rescale:
- - 0
- - 365
- legend:
- unit:
- label: Days
- type: gradient
- min: 0
- max: 365
- stops:
- - "#E4FF7A"
- - "#FAED2D"
- - "#FFCE0A"
- - "#FFB100"
- - "#FE9900"
- - "#FC7F00"
- compare:
- datasetId: climdex-tmaxxf-access-cm2
- layerId: climdex-tmaxxf-access-cm2-585
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
-
-
----
-
-
-
-
-
-
- Comparison of days above 90 degrees Fahrenheit in CMIP6 models between SSP-126 (left) and SSP-585 (right).
-
-
-
\ No newline at end of file
diff --git a/datasets/FLDAS-soilmoisture-anomalies.data.mdx b/datasets/FLDAS-soilmoisture-anomalies.data.mdx
deleted file mode 100644
index ba3601a282..0000000000
--- a/datasets/FLDAS-soilmoisture-anomalies.data.mdx
+++ /dev/null
@@ -1,175 +0,0 @@
----
-id: fldas-soil-moisture-anomalies
-name: "FLDAS Surface Soil Moisture Anomalies"
-description: "A 10 km global data product with 40 years of monthly soil moisture anomalies for food and water security monitoring from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System"
-media:
- src: ::file ./media/FLDAS_Dataset_Cover.jpg
- alt: Landscape in Gondar, Ethiopia
- author:
- name: Amy McNally
-taxonomy:
- - name: Topics
- values:
- - Agriculture
- - Water Resources
- - name: Subtopics
- values:
- - Soil Moisture
- - Drought
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA GES DISC
-infoDescription: |
- ::markdown
- - **Temporal Extent:** January 1982 - June 2023
- - **Temporal Resolution:** Monthly
- - **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0)
- - **Spatial Resolution:** 10 km x 10 km
- - **Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean
- - **Data Type:** Research
- - **Data Latency:** Monthly
-layers:
- - id: SoilMoi00_10cm_tavg
- stacCol: fldas-soil-moisture-anomalies
- name: FLDAS Surface Soil Moisture Anomalies
- type: raster
- description: "Surface soil moisture 0-10cm anomaly"
- zoomExtent:
- - 0
- - 14
- sourceParams:
- colormap_name: rdbu
- rescale: -0.3, 0.3
- resampling: bilinear
- bidx: 1
- nodata: -9999
- compare:
- datasetId: fldas-soil-moisture-anomalies
- layerId: SoilMoi00_10cm_tavg
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: kg mm3/mm3
- type: gradient
- min: "-0.3"
- max: "0.3"
- stops:
- - "#67001f"
- - "#d6604d"
- - "#fddbc7"
- - "#d1e5f0"
- - "#4393c3"
- - "#053061"
- info:
- source: NASA
- spatialExtent: Quasi-Global
- temporalResolution: Monthly
- unit: mm3/mm3
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 1982 - June 2023
- - **Temporal Resolution:** Monthly
- - **Spatial Extent:** Quasi-Global ( -180.0,-60.0,180.0,90.0)
- - **Spatial Resolution:** 10 km x 10 km
- - **Data Units:** Fraction Soil moisture anomaly (mm3/mm3) difference from 1982-2016 monthly mean
- - **Data Type:** Research
- - **Data Latency:** Monthly
-
-
-
-
- Comparison of 0-10cm soil moisture anomalies across Africa between June 1, 2020 and June 1, 2022.
-
-
-
-
-
-
-
- ## Scientific Details
- The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) contains a series of land surface parameters simulated from the Noah 3.6.1 model. The data are in 0.10 degree resolution and range from January 1982 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall data that has been temporally downscaled using the NASA Land Data Toolkit. The simulation was initialized on January 1, 1982 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year. Soil moisture anomalies are computed based on monthly averages from 1982-2016.
-
-
-
-
-
-
- ## Source Data Product Citation
-
- Amy McNally, NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly Anomaly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], [10.5067/GNKZZBAYDF4W](https://doi.org/10.5067/GNKZZBAYDF4W)
-
-
-
-
-
-
- ## Dataset Accuracy
-
- This dataset uses CHIRPS precipitation inputs and MERRA-2 reanalysis. While regional, relative, comparisons to remotely sensed estimates and other model products are favorable, users should verify that the data accuracy meets the requirements of their specific application, and interpret results accordingly.
-
-
-
-
-
-
- ## Key Publications
-
- This dataset uses CHIRPS precipitation inputs and MERRA-2 reanalysis. While regional, relative, comparisons to remotely sensed estimates and other model products are favorable, users should verify that the data accuracy meets the requirements of their specific application, and interpret results accordingly.
-
-
-
-
-
-
-
- ## Acknowledgment
-
- We gratefully acknowledge the financial support from the NASA Earth Science Applications: Water Resources program award 13-WATER13-0010, and USAID FEWS NET and NASA Participating Agency Program Agreement and NASA Harvest. Computing was supported by the resources at the NASA Center for Climate Simulation (NCCS). Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD).
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0).
-
-
diff --git a/datasets/GPM_3IMERGDF.data.mdx b/datasets/GPM_3IMERGDF.data.mdx
deleted file mode 100644
index 771a155936..0000000000
--- a/datasets/GPM_3IMERGDF.data.mdx
+++ /dev/null
@@ -1,165 +0,0 @@
----
-id: GPM_3IMERGDF.v07
-name: "GPM IMERG Daily Precipitation"
-description: "GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree"
-media:
- src: ::file ./media/gpmimergdaily.png
- alt: GPM IMERG Final Precipitation
- author:
- name: NASA
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Precipitation
- - Snow
- - Floods
- - Drought
-layers:
- - id: GPM_3IMERGDF.v07
- type: cmr
- stacCol: GPM_3IMERGDF
- tileApiEndpoint: "https://staging.openveda.cloud/api/titiler-cmr/WebMercatorQuad/tilejson.json"
- name: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree
- description: "GPM Level 3 IMERG Final Daily 10 x 10 km (GPM_3IMERGDF) accumulated precipitation"
- time_density: day
- zoomExtent:
- - 0
- - 20
- sourceParams:
- tile_scale: 3
- resampling: bilinear
- variable: precipitation
- colormap_name: gnbu
- rescale:
- - 0
- - 46
- maxzoom: 12
- concept_id: C2723754864-GES_DISC
- backend: xarray
- legend:
- unit:
- label: "mm/hr"
- type: gradient
- min: 0
- max: 46
- stops:
- [
- "#f7fcf0",
- "#e6f5e1",
- "#d7efd1",
- "#c5e8c2",
- "#abdeb6",
- "#8bd2bf",
- "#6bc3c9",
- "#4bafd1",
- "#3193c2",
- "#1878b4",
- "#085da0",
- "#084081",
- ]
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2000-2024
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 0.1 x 0.1 degree
- - **Data Units:** mm/hr
- - **Data Type:** Research
-
-
-
-
- GPM IMERG peak rainfall rate over southeast Texas during Hurricane Harvey on August 29, 2017.
-
-
-
-
-
-
-
- ### About
-
- This dataset is the GPM Level 3 IMERG _Final_ Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24.
-
- As the "Final" product, it undergoes rigorous post-processing, including gauge calibration and quality checks, making it ideal for scientific research and operational applications. The dataset integrates observations from the GPM Core Observatory and partner satellites, leveraging advanced algorithms to provide reliable and consistent data.
-
- Applications of this dataset include flood and drought monitoring, climate studies, water resource management, and environmental analysis. Its high spatial resolution, global coverage, and inclusion of retrospective corrections make it a critical resource for understanding precipitation patterns and dynamics.
-
-
-
-
-
-
- ### Access the Data
-
- Visit the [NASA GSFC](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary) page to explore all of the options that GPM IMERG offers.
-
- The files represented are NetCDF files in GES DISC's Earthdata Cloud bucket and discovered and tiled via a dynamic tiling service using CMR.
-
-
-
-
-
-
- ### Citing This Dataset
- G. Huffman, D. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, P. Xie, 2014: Integrated Multi-satellitE Retrievals for GPM (IMERG), version 4.4. NASA's Precipitation Processing Center.
-
-
-
-
-
-
-
- ### Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Huffman, G., Bolvin, D., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E., Sorooshian, S., Tan, J., and P. Xie (2020). NASA Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version 06. https://gpm.nasa.gov/sites/default/files/2020-05/IMERG_ATBD_V06.3.pdf
-
- [GPM & TRMM Data Products DOIs](https://pps.gsfc.nasa.gov/Documents/Master_List_of_PPS_Data_Products.html)
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/Tornado_Hail_2011.data.mdx b/datasets/Tornado_Hail_2011.data.mdx
deleted file mode 100644
index 63a03ed30c..0000000000
--- a/datasets/Tornado_Hail_2011.data.mdx
+++ /dev/null
@@ -1,154 +0,0 @@
----
-id: Tornado_Hail_2011
-isHidden: true
-name: 'NCEI Interpolated Hail Reports for the April 27th, 2011 Super Outbreak'
-description: "NCEI Storm Events Database Hail Reports that are Interpolated for the Super Outbreak of April 27th, 2011"
-media:
- src: ::file ./media/tornado_2011_NDVI_Background.jpg
- alt: Aerial View of Scope of 2011 Tuscaloosa Tornado Damage Path
- author:
- name: NWS BMX
- url: https://www.weather.gov/bmx/event_04272011tuscbirm
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - name: Source
- values:
- - NCEI
-infoDescription: |
- ::markdown
- - **Temporal Extent:** April 27, 2011
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Northern and Central Alabama
- - **Spatial Resolution:** N/A
- - **Data Units:** Inches (In)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: Tornado_Hail_2011
- stacCol: Tornado_Hail_2011
- name: Interpolated NCEI Hail Size Reports for the Super Outbreak of April 27th, 2011
- type: raster
- description: "NCEI Storm Events Database Hail reports that are Interpolated for the Super Outbreak of April 27th, 2011"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: greens
- rescale:
- - 1
- - 2.75
- legend:
- type: gradient
- unit:
- label: In
- min: 1
- max: 2.75
- stops:
- - "#f7fcf5" # Very Pale Green
- - "#c7e9c0" # Light Mint Green
- - "#74c476" # Medium Green
- - "#238b45" # Dark Green
- - "#00441b" # Deep Forest Green
- info:
- source: UAH
- spatialExtent: Northern and Central Alabama
- temporalResolution: Daily
- unit: Inches (In)
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** April 27, 2011
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Northern and Central Alabama
- - **Spatial Resolution:** N/A
- - **Data Units:** Inches (In)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- NCEI Storm Events Database hail size reports that are interpolated for the Super Outbreak of April 27th, 2011 within Northern and Central Alabama.
-
-
-
-
-
-
-
- ### About
-
- The National Centers for Environmental Information (NCEI) Storm Events Database is a comprehensive archive of severe weather reports across the United States, including wind gust data collected from various sources. This dataset compiles observations from the National Weather Service (NWS), trained storm spotters, and automated surface observing systems (ASOS), providing detailed records of wind events, including peak gust speeds, locations, and associated impacts.
-
- By analyzing wind gust reports from the NCEI Storm Events Database, researchers can assess the spatial and temporal distribution of high-wind events, identify trends in extreme wind occurrences, and evaluate their impacts on infrastructure, transportation, and public safety. This dataset is particularly valuable for studying severe convective wind events, derechos, and other high-impact wind phenomena, helping to inform mitigation strategies and improve forecasting efforts.
-
-
-
-
-
-
- ### Access the Data
-
- This dataset was interpolated from confirmed derecho reports from the [Storm Events Database](https://www.ncdc.noaa.gov/stormevents/), on May 12th, 2022.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Li, J., Geiss, A., Feng, Z., Leung, L. R., Qian, Y., and Cui, W., 2024: A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes. Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-112.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **How the 2011 Tornadoes Transformed Alabama’s Vegetation**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/Tornado_Tracks_2011.data.mdx b/datasets/Tornado_Tracks_2011.data.mdx
deleted file mode 100644
index eab319ca1e..0000000000
--- a/datasets/Tornado_Tracks_2011.data.mdx
+++ /dev/null
@@ -1,200 +0,0 @@
----
-id: Tornado_Tracks_2011
-isHidden: true
-name: "April 27th, 2011 Tornado Tracks"
-description: "Utilizing NWS tornado track data to highlight the damage of the 04/27/2011 tornadoes"
-media:
- src: ::file ./media/tornado_2011_NDVI_Background.jpg
- alt: Aerial View of Scope of 2011 Tuscaloosa Tornado Damage Path
- author:
- name: NWS BMX
- url: https://www.weather.gov/bmx/event_04272011tuscbirm
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - name: Source
- values:
- - NWS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** April 27, 2011
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 50 meters
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: Tornado_Tracks_2011
- stacCol: Tornado_Tracks_2011
- name: April 27th 2011 Tornadoes (Paths)
- type: raster
- description: "This dataset shows official NWS tornado center path lines categorized by maximum EF rating."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: tornado_ef_scale
- nodata: 0
- rescale:
- - 0
- - 255
- legend:
- type: categorical
- stops:
- - color: "#b3bcc9" # Grey for EFUNK
- label: EFUNK
- - color: "#add8e6" # Light blue for EF0
- label: EF0
- - color: "#90ee90" # Green for EF1
- label: EF1
- - color: "#ffe71f" # Yellow for EF2
- label: EF2
- - color: "#ffa500" # Orange for EF3
- label: EF3
- - color: "#ff0000" # Red for EF4
- label: EF4
- - color: "#ff00ff" # Pink for EF5
- label: EF5
- info:
- source: National Weather Service (NWS)
- spatialExtent: CONUS
- temporalResolution: N/A
- unit: N/A
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** April 27th 2011
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 50 meters
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- The Hackleburg EF-5 Tornado and Tuscaloosa-Birmingham EF-4 Tornado, produced from the Super Outbreak on April 27th, 2011.
-
-
-
-
-
-
-
- ### About
-
- The National Weather Service’s (NWS) Damage Assessment Toolkit (DAT) is a pivotal geographic information system (GIS)-hosted dataset designed to support post-storm damage surveys conducted by meteorologists. This toolkit plays a crucial role in documenting and analyzing tornado and significant straight-line wind damage across affected areas. Ground-based surveys are carried out to capture this information, which is then geospatially referenced and uploaded to the DAT database.
-
- This dataset encompasses comprehensive elements such as tornado track centerlines, polygons depicting Enhanced Fujita (EF) scale ratings along tornado paths, and detailed descriptions with meteorological statistics for each logged damage location. In some cases, it also includes imagery collected by survey teams, adding further context to damage assessments. The comprehensive information in this dataset makes it invaluable for researchers, planners, and emergency responders.
-
-
-
-
-
-
-
-
- ### What the DAT Offers
-
- * Tornado Track Centerlines: Geospatial data capturing the precise paths of tornadoes, providing insights into their trajectory and extent.
-
- * Enhanced Fujita (EF) Scale Polygons: Detailed polygons of the EF rating at each location along a tornado’s path, offering a better understanding of the severity of the storm across different points.
-
- * Location-Specific Damage Descriptions: Comprehensive descriptions of damage at each surveyed point, paired with relevant meteorological statistics to offer deeper insight into storm impacts.
-
- * Damage Imagery: When available, surveyor-captured images provide visual context to logged damage points, further enhancing data interpretation and analysis.
-
-
-
-
-
-
- ### Access the Data
-
- Visit the [Storm Damage Viewer](https://apps.dat.noaa.gov/StormDamage/DamageViewer/) page to explore a GIS-hosted page that contains the DAT dataset.
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- J. Parks Camp, NWSFO, Tallahassee, FL; and P. Kirkwood, J. G. LaDue, L. A. Schultz, and N. Parikh., National Weather Service Damage Assessment Toolkit: Transitioning to Operations, Abstract 9.1 presented at 2017 Annual Meeting, AMS, Seattle, Washington, 26 Jan.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Leonardo, D., 2011: Damage Assessment Toolkit business case analysis: NWS OSIP Project 08-024. NWS Rep., 16 pp., https://osip.nws.noaa.gov/osip/projectDetail.php?document=23295.
-
- Stellman, K., T. Brice, D. Hansing, A. Foster, C. Pieper, and K. Lander, 2009: How geographic information system software is improving the effectiveness of the National Weather Service. 89th Annual Meeting, New Orleans, LA, Amer. Meteor. Soc., 5A.11, http://ams.confex.com/ams/89annual/webprogram/Paper148642.html.
-
-
-
-
-
-
-
- ### Other Publications
-
- National Wind Institute, 2006: A recommendation for an enhanced Fujita scale (EF-scale). Texas Tech University Wind Science and Engineering Center Rep., 111 pp., www.depts.ttu.edu/nwi/Pubs/EnhancedFujitaScale/EFScale.pdf.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **How the 2011 Tornadoes Transformed Alabama’s Vegetation**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/Tornado_Wind_2011.data.mdx b/datasets/Tornado_Wind_2011.data.mdx
deleted file mode 100644
index 9fbf19d5ce..0000000000
--- a/datasets/Tornado_Wind_2011.data.mdx
+++ /dev/null
@@ -1,94 +0,0 @@
----
-id: Tornado_Wind_2011
-isHidden: true
-name: 'NCEI Interpolated Wind Gusts for the Super Outbreak of April 27, 2011'
-description: "NCEI Storm Events Database Wind Gusts that are Interpolated for the Super Outbreak of April 27th, 2011"
-media:
- src: ::file ./media/tornado_2011_NDVI_Background.jpg
- alt: Aerial View of Scope of 2011 Tuscaloosa Tornado Damage Path
- author:
- name: NWS BMX
- url: https://www.weather.gov/bmx/event_04272011tuscbirm
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - Air Quality
- - name: Source
- values:
- - NCEI
-infoDescription: |
- ::markdown
- - **Temporal Extent:** April 27, 2011
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Northern and Central Alabama
- - **Spatial Resolution:** N/A
- - **Data Units:** Miles Per Hour (mph)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: Tornado_Wind_2011
- stacCol: Tornado_Wind_2011
- name: Interpolated NCEI Wind Gust Reports for the Super Outbreak of April 27th, 2011
- type: raster
- description: "NCEI Storm Events Database Wind Gusts that are Interpolated for the Super Outbreak of April 27th, 2011"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bupu
- rescale:
- - 60
- - 75
- legend:
- type: gradient
- unit:
- label: mph
- min: 60
- max: 75
- stops:
- - "#f7fcfd" #Very Pale Cyan
- - "#bfd3e6" #Pastel Blue
- - "#8c95c6" #Muted Lavender Blue
- - "#88409c" #Deep Violet
- - "#4d004b" #Dark Purple
- info:
- source: UAH
- spatialExtent: Northern and Central Alabama
- temporalResolution: Daily
- unit: Miles Per Hour (mph)
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** May 12, 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Northern Plains region
- - **Spatial Resolution:** N/A
- - **Data Units:** Miles Per Hour (mph)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- NCEI Storm Events Database wind gusts that are interpolated for the Derecho of May 12th, 2022 within the Northern Plains region.
-
-
-
\ No newline at end of file
diff --git a/datasets/bangladesh-landcover-2001-2020.data.mdx b/datasets/bangladesh-landcover-2001-2020.data.mdx
deleted file mode 100644
index df6d3f5cca..0000000000
--- a/datasets/bangladesh-landcover-2001-2020.data.mdx
+++ /dev/null
@@ -1,152 +0,0 @@
----
-id: bangladesh-landcover-2001-2020
-isHidden: true
-name: 'Land Cover - Bangladesh'
-description: 'Annual land cover maps for 2001 and 2020 (Bangladesh)'
-media:
- src: ::file media/bangladesh-landcover-2001-2020--dataset-cover.jpg
- alt: 'Annual land cover maps for 2001 and 2020 (Bangladesh)'
- author:
- name: USGS
- url: https://unsplash.com/photos/d59NHNtT_Ss
-taxonomy:
- - name: Topics
- values:
- - Biodiversity
- - name: Subtopics
- values:
- - Land Use
- - name: Source
- values:
- - MODIS
-infoDescription: |
- ::markdown
- The annual land use - land cover maps for 2001 and 2021 were captured using the combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset ([MCD12Q1 V6](https://lpdaac.usgs.gov/products/mcd12q1v006/)). The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country.
-layers:
- - id: bangladesh-landcover-2001-2020
- stacCol: bangladesh-landcover-2001-2020
- name: 'Land cover maps 2001 and 2020'
- type: raster
- description: 'Annual land cover maps for 2001 and 2020 (Bangladesh)'
- zoomExtent:
- - 3
- - 20
- sourceParams:
- resampling: nearest
- colormap: '{"0":[0,0,0,128],"100":[0,130,0,255],"200":[17,131,226,255],"300":[199,43,32,255],"400":[98,234,37,255]}'
- bidx: 1
- return_mask: true
- legend:
- type: categorical
- stops:
- - color: "#000000"
- label: No Data
- - color: "#008200"
- label: Native Vegetation
- - color: "#1183e2"
- label: Open water
- - color: "#c72b20"
- label: Urban areas
- - color: "#62ea25"
- label: Cropland
- compare:
- datasetId: bangladesh-landcover-2001-2020
- layerId: bangladesh-landcover-2001-2020
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- info:
- source: NASA
- spatialExtent: Bangladesh
- temporalResolution: Annual
- unit: Categorical
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2001 and 2020
- - **Spatial Extent:** Bangladesh, Asia
- - **Spatial Resolution:** 500 meters
- - **Data Type:** Research
-
-
-
-
- Comparison of MODIS-derived land cover in Bangladesh between 2001 and 2020.
-
-
-
-
-
-
-
- ## About
- The annual land cover maps of 2001 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Annual Land Cover Type dataset (MCD12Q1 V6, dataset link: [https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)). The actual data product provides global land cover types at yearly intervals (2001-2020) at 500 meters with six different types of land cover classification. Among six different schemes, The International Geosphere–Biosphere Programme (IGBP) land cover classification selected and further simplified to dominant land cover classes (water, urban, cropland, native vegetation) for two different years to illustrate the changes in land use and land cover of the country.
-
-
-
-
-
-
-
- ## Key Publications
-
- Friedl, M., Sulla-Menashe, D. (2019). MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-03-05 from https://doi.org/10.5067/MODIS/MCD12Q1.006
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Unraveling the Components of Coastal Risk**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional Resources
- [MODIS Land Cover Data Quality Guide](https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf)
-
-
diff --git a/datasets/blizzard-count-1950-2021.data.mdx b/datasets/blizzard-count-1950-2021.data.mdx
deleted file mode 100644
index 21a5ef149c..0000000000
--- a/datasets/blizzard-count-1950-2021.data.mdx
+++ /dev/null
@@ -1,145 +0,0 @@
----
-id: blizzard-count
-name: 'Blizzard Count 1950-2021'
-description: "Nearest neighbor interpolation of NCEI Storm Events Database confirmed blizzard reports from 1950-2021 in the United States, clipped to the Northern Plains, Midwest, and Northeast."
-
-media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Snow
- - Climate
- - Hydrology
- - name: Source
- values:
- - NCEI
-layers:
- - id: blizzard-count
- stacCol: blizzard-count
- name: Blizzard Count 1950-2021
- type: raster
- description: "Nearest neighbor interpolation of NCEI Storm Events Database confirmed blizzard reports from 1950-2021 in the United States, clipped to the Northern Plains, Midwest, and Northeast."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: turbo
- nodata: -999
- rescale:
- - 1
- - 50
- legend:
- type: gradient
- min: 1
- max: 50
- stops:
- - "#30123b"
- - "#4146d1"
- - "#23d775"
- - "#f9f510"
- - "#fa6b02"
- - "#900c00"
- info:
- source: NCEI
- spatialExtent: Northern and Eastern United States
- temporalResolution: 1950-2021 Climatology
- unit: Count
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** 1950-2021
- - **Temporal Resolution:** Single Climatology
- - **Spatial Extent:** Northern and Eastern United States
- - **Data Type:** Research
-
-
-
-
- Number of confirmed blizzards from 1950-2021 in the northern Plains.
-
-
-
-
-
-
-
- ## About
-
- In the United States, blizzards occur most frequently in the northern Great Plains, but they have been reported in every state except Hawaii. While blizzards are traditionally associated with winter, records show they can happen any time between October and June. Advances in weather monitoring and forecasting have improved our ability to detect and report these events, which has led to an increase in reported blizzard activity over the past several decades. On average, 13 blizzards occur annually across CONUS, but the past few decades have seen this number inflate to 19. The geographic spread of these storms has also widened, with more blizzards occurring outside their historical hotspots. Although the intensity of impact by individual storms has generally decreased due to better forecasting, their economic and societal impacts have risen, with associated losses now exceeding $1 billion per year. Understanding blizzards and preparing for their impacts is critical for communities in regions prone to extreme winter weather.
-
-
-
-
-
-
- ## Access the Data
-
- This dataset was interpolated from confirmed blizzard reports from the [Storm Events Database](https://www.ncdc.noaa.gov/stormevents/), between 1950-2021.
-
-
-
-
-
-
- ### Key Publications
-
- Coleman, J. S. M., and R. M. Schwartz, 2017: An Updated Blizzard Climatology of the Contiguous United States (1959–2014): An Examination of Spatiotemporal Trends. J. Appl. Meteor. Climatol., 56, 173–187, https://doi.org/10.1175/JAMC-D-15-0350.1.
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **When Winter Rages**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/blizzard-era5.data.mdx b/datasets/blizzard-era5.data.mdx
deleted file mode 100644
index 962c234c59..0000000000
--- a/datasets/blizzard-era5.data.mdx
+++ /dev/null
@@ -1,280 +0,0 @@
----
-id: blizzard-era5
-name: 'ERA5 Reanalysis (Select Events)'
-description: "ERA5 reanalysis data of surface temperature, wind, mean sea level pressure, and cloud fraction for two select blizzard events."
-
-media:
- src: ::file ./media/CERES_NETFLUX_M_2006-07.jpeg
- alt: Net radiation across the globe.
- author:
- name: NASA Earth Observatory
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Snow
- - Weather
- - Hydrology
- - name: Source
- values:
- - ECMWF
-layers:
- - id: blizzard-era5-2m-temp
- stacCol: blizzard-era5-2m-temp
- name: ERA5 2 Meter Temperature
- type: raster
- description: "ERA5 2-meter temperature reanalysis data for two select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: surface_temperature
- nodata: -999
- rescale:
- - 240
- - 315
- legend:
- type: gradient
- unit:
- label: K
- min: 240
- max: 315
- stops:
- - "#3CCBCE"
- - "#C5F8FF"
- - "#FEC5FF"
- - "#E079FB"
- - "#094FC9"
- - "#009FFF"
- - "#44E2FF"
- - "#147F4F"
- - "#79B32C"
- - "#FDFE00"
- - "#FF8700"
- - "#FF0F00"
- - "#9D0F2B"
- - "#4D0000"
- - "#BA2E6D"
- info:
- source: ECMWF
- spatialExtent: Global
- temporalResolution: 1950-1978; Inconsistent
- unit: Kelvin
- media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-
- - id: blizzard-era5-10m-wind
- stacCol: blizzard-era5-10m-wind
- name: ERA5 10 Meter Wind
- type: raster
- description: "ERA5 10-meter wind reanalysis data for two select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: ylorrd
- nodata: -999
- rescale:
- - 0
- - 25
- legend:
- type: gradient
- unit:
- label: m/s
- min: 0
- max: 25
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: ECMWF
- spatialExtent: Global
- temporalResolution: 1950-1978; Inconsistent
- unit: m/s
- media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-
- - id: blizzard-era5-cfrac
- stacCol: blizzard-era5-cfrac
- name: ERA5 Cloud Fraction
- type: raster
- description: "ERA5 cloud fraction reanalysis data for two select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: greys_r
- nodata: -999
- rescale:
- - 0
- - 1
- legend:
- type: gradient
- min: 0
- max: 1
- stops:
- - "#000000"
- - "#404040"
- - "#7a7a7a"
- - "#b5b5b5"
- - "#e2e2e2"
- - "#ffffff"
- info:
- source: ECMWF
- spatialExtent: Global
- temporalResolution: 1950-1978; Inconsistent
- unit: fraction; 1 = 100% coverage
- media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-
- - id: blizzard-era5-mslp
- stacCol: blizzard-era5-mslp
- name: ERA5 Mean Sea Level Pressure
- type: raster
- description: "ERA5 mean sea level pressure (MSLP) reanalysis data for two select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: nipy_spectral
- nodata: -999
- rescale:
- - 972
- - 1040
- legend:
- unit:
- label: hPa
- type: gradient
- min: 972
- max: 1040
- stops:
- - "#000000"
- - "#870098"
- - "#0078dd"
- - "#00aa88"
- - "#00dc00"
- - "#efed00"
- - "#fe0000"
- - "#cccccc"
- info:
- source: ECMWF
- spatialExtent: Global
- temporalResolution: 1950-1978; Inconsistent
- unit: hPa
- media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
----
-
-
- ## Dataset Details
- - **Temporal Extent:** 1950-1978
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 0.25 degree
- - **Data Units:** Temperature - Kelvin; Wind - m/s; MSLP - hPa; Cloud Fraction - fraction
- - **Data Type:** Research
-
-
-
-
- ERA5 reanalysis mean sea level pressure from the strongest timeframe of the Great Blizzard of 1978 in the Great Lakes region of the USA.
-
-
-
-
-
-
- ## About
-
- ERA5 reanalysis, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), is a high-resolution global atmospheric dataset that provides historical climate and weather information from 1940 to the present. It combines observations from satellites, weather stations, and other sources with advanced numerical weather prediction models to generate a consistent, spatially complete representation of past atmospheric conditions. ERA5 offers hourly data at a 0.25 degree spatial resolution with multiple atmospheric variables, including temperature, wind speed, cloud coverage, and pressure, along with uncertainty estimates. It is widely used in climate research, meteorology, and environmental studies.
-
-
-
-
-
-
-
- ## Access the Data
-
- Visit ECMWF's [Climate Data Store](https://cds.climate.copernicus.eu/datasets?q=era5&kw=Variable+domain%3A+Atmosphere+%28surface%29&kw=Variable+domain%3A+Atmosphere+%28upper+air%29&kw=Variable+domain%3A+Atmosphere+%28upper+level%29&kw=Variable+domain%3A+Ocean+%28physics%29) to explore options for data access.
-
-
-
-
-
-
- ### Key Publications
-
- Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **When Winter Rages**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/blizzard-footprints.data.mdx b/datasets/blizzard-footprints.data.mdx
deleted file mode 100644
index 64be0f69b3..0000000000
--- a/datasets/blizzard-footprints.data.mdx
+++ /dev/null
@@ -1,418 +0,0 @@
----
-id: blizzard-snowfall-footprint
-name: 'Blizzard and Snowfall Footprint based on Climatology'
-description: "Raster land masks displaying the general snowfall footprint for various mid-latitude cyclones."
-
-media:
- src: ::file ./media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Snow
- - Climate
- - Hydrology
- - name: Source
- values:
- - UAH
-layers:
- - id: blizzard-clipper
- stacCol: blizzard-clipper
- name: AB/SK/MB Clipper Snowfall Footprint
- type: raster
- description: "Climatologically favored region of the USA that experiences snowfall from Alberta Clipper, Saskatchewan Screamer, and Manitoba Mauler cyclones."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"0":[22,158,242,255]}'
- legend:
- type: categorical
- stops:
- - color: "#169ef2"
- label: Clipper Snowfall
- info:
- source: UAH
- spatialExtent: Southern Canada/Northern United States
- temporalResolution: 1950-2021 Climatology
- unit: Binary
-
- - id: blizzard-alley
- stacCol: blizzard-alley
- name: Blizzard Alley Snowfall Footprint
- type: raster
- description: "Climatologically favored region of the Central USA that experiences regular blizzards, known as Blizzard Alley."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"0":[22,158,242,255]}'
- legend:
- type: categorical
- stops:
- - color: "#169ef2"
- label: Blizzard Alley
- info:
- source: NCEI
- spatialExtent: Northern Plains
- temporalResolution: 1950-2021 Climatology
- unit: Binary
-
- - id: blizzard-co-low
- stacCol: blizzard-co-low
- name: Colorado Low Snowfall Footprint
- type: raster
- description: "Climatologically favored region of the USA that experiences snowfall from Colorado Low cyclones."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"0":[22,158,242,255]}'
- legend:
- type: categorical
- stops:
- - color: "#169ef2"
- label: CO Low Snowfall
- info:
- source: UAH
- spatialExtent: Central United States
- temporalResolution: 1950-2021 Climatology
- unit: Binary
-
- - id: blizzard-millera
- stacCol: blizzard-millera
- name: Miller A Snowfall Footprint
- type: raster
- description: "Climatologically favored region of the USA that experiences snowfall from Miller A cyclones."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"0":[22,158,242,255]}'
- legend:
- type: categorical
- stops:
- - color: "#169ef2"
- label: Miller A Snowfall
- info:
- source: UAH
- spatialExtent: Eastern United States
- temporalResolution: 1950-2021 Climatology
- unit: Binary
-
- - id: blizzard-millerb
- stacCol: blizzard-millerb
- name: Miller B Snowfall Footprint
- type: raster
- description: "Climatologically favored region of the USA that experiences snowfall from Miller B cyclones."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"0":[22,158,242,255]}'
- legend:
- type: categorical
- stops:
- - color: "#169ef2"
- label: Miller B Snowfall
- info:
- source: UAH
- spatialExtent: Eastern United States
- temporalResolution: 1950-2021 Climatology
- unit: Binary
-
- - id: blizzard-noreaster
- stacCol: blizzard-noreaster
- name: Noreaster Snowfall Footprint
- type: raster
- description: "Climatologically favored region of the USA that experiences snowfall from Nor'easter cyclones."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"0":[22,158,242,255]}'
- legend:
- type: categorical
- stops:
- - color: "#169ef2"
- label: Noreaster Snowfall
- info:
- source: UAH
- spatialExtent: Eastern United States
- temporalResolution: 1950-2021 Climatology
- unit: Binary
-
- - id: blizzard-panhandle-hooker
- stacCol: blizzard-panhandle-hooker
- name: Panhandle Hooker Snowfall Footprint
- type: raster
- description: "Climatologically favored region of the USA that experiences snowfall from Panhandle Hooker cyclones."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"0":[22,158,242,255]}'
- legend:
- type: categorical
- stops:
- - color: "#169ef2"
- label: Panhandle Hooker Snowfall
- info:
- source: UAH
- spatialExtent: Central United States
- temporalResolution: 1950-2021 Climatology
- unit: Binary
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 1950-2021
- - **Temporal Resolution:** Single Climatology
- - **Data Units:** Binary
- - **Data Type:** Research
-
-
-
-
-
- ## Alberta Clipper, Saskatchewan Screamer, and Manitoba Mauler
- - **Spatial Extent:** Southern Canada/Northern United States
- Alberta Clippers, Saskatchewan Screamers, and Manitoba Maulers (AC/SK/MB) are fast-moving low pressure systems that originate in the Canadian Prairies and bring cold, dry air and light to moderate snowfall to parts of the United States. These systems are named for their origins in Alberta, Saskatchewan, and Manitoba, respectively, and are distinguished by their speed and limited moisture content. Alberta Clippers are the most common of the three and typically travel southeastward across the northern Plains, Midwest, and Great Lakes region, often bringing light amounts of snow, though localized areas can receive higher totals. Saskatchewan Screamers and Manitoba Maulers follow similar tracks but are less frequent. Due to their rapid movement, these storms rarely bring heavy snowfall, but their strong winds can create blowing snow and reduced visibility, occasionally leading to blizzard conditions. Additionally, they often usher in frigid Arctic air, leading to significant temperature drops that can be anomalous for impacted regions. These systems are a defining feature of winter weather across the northern United States and contribute heavily to the region's cold and snowy climatology, as in more active years handfuls of these systems can occur in quick succession.
-
-
-
-
- Climatologically favored region of the USA that experiences snowfall from Alberta Clipper, Saskatchewan Screamer, and Manitoba Mauler cyclones.
-
-
-
-
-
-
- ## Blizzard Alley
- - **Spatial Extent:** Northern Plains
- Blizzard Alley is a term used to describe a region in the north-central United States, particularly the eastern Dakotas, western Minnesota, and parts of Nebraska and Iowa, that experiences blizzards regularly. This region is among the most blizzard-prone areas in North America. This specific dataset shows the Blizzard Alley region as interpolated from the National Centers for Environmental Information's (NCEI's) Storm Events Database. The Storm Events Database contains reports of officially confirmed blizzard conditions across the United States, beginning in 1950.
-
-
-
-
-
- Climatologically favored region of the Central USA that experiences regular blizzards, known as Blizzard Alley.
-
-
-
-
-
-
-
- ## Denver Cyclones
- - **Spatial Extent:** Central United States
- Denver Cyclones, or Colorado Lows, are low pressure systems that form on the lee side of the Rocky Mountains, often along the Colorado Front Range. These systems form when moist air from the Gulf of America converges with cold Arctic air descending from Canada, creating sharp temperature gradients. The interaction between these air masses and the region’s complex terrain promotes the development of cyclonic systems that can bring heavy snowfall or blizzard conditions to the Colorado Front Range, including Denver, as well as parts of the Central and Northern Great Plains and Midwest. As these cyclones intensify and move eastward, they can bring severe winter weather to Blizzard Alley, making them a key contributor to the high frequency of blizzards in this region. The open terrain of the Great Plains amplifies wind speeds, further reducing visibility and exacerbating blizzard conditions.
-
-
-
-
- Climatologically favored region of the USA that experiences snowfall from Colorado Low cyclones.
-
-
-
-
-
-
-
- ## Miller A
- - **Spatial Extent:** Eastern United States
- Miller Type A cyclones form along the Gulf Coast or in the southern United States, where warm, moist air from the Gulf of America meets colder Arctic air descending from the north. These storms then travel along the East Coast, often intensifying as they near the Northeast U.S., as they tap into additional moisture from the Atlantic Ocean. Type A cyclones tend to bring widespread, heavy snowfall to the densely populated I-95 corridor or interior Northeast. Their direct development along the Gulf provides an abundant moisture source, resulting in potentially significant impacts for the Southeastern U.S., the Mid-Atlantic, and the Northeast.
-
-
-
-
-
- Climatologically favored region of the USA that experiences snowfall from Miller A cyclones.
-
-
-
-
-
-
-
- ## Miller B
- - **Spatial Extent:** Eastern United States
- Miller Type B cyclones are more complex, involving the interaction of two distinct low pressure systems. A primary low forms over the Midwest or Great Lakes regions, often producing light to moderate snowfall. As the primary low weakens, energy transfers to a secondary low-pressure system that develops near the Mid-Atlantic coast. This secondary low rapidly intensifies as it moves up the East Coast, often producing heavy snow for the Northeast and Mid-Atlantic regions. Type B cyclones tend to bring mixed precipitation and ice to areas farther inland before transitioning to snow along the coast.
-
-
-
-
-
- Climatologically favored region of the USA that experiences snowfall from Miller B cyclones.
-
-
-
-
-
-
-
- ## Nor'easter
- - **Spatial Extent:** Eastern United States
- Nor’easters are perhaps the most notorious system that can bring blizzard conditions. Nor’easters frequent blizzard conditions to the I-95 corridor, can be either Miller Type A or B cyclones (but are more commonly Type B), and are often tracked using a coordinate benchmark known as the "40°N, 70°W rule." This location, just southeast of Cape Cod, Massachusetts, serves as a critical reference point for meteorologists. If the center of a nor’easter passes near or over this benchmark as it moves to the northeast, it frequently results in significant snow and wind impacts for the densely populated I-95 corridor from Washington, D.C., to Boston. This position aligns the storm to draw in cold Arctic air from the northwest while tapping into moist Atlantic air, creating the perfect setup for heavy snowfall and blizzard conditions.
-
-
-
-
-
- Climatologically favored region of the USA that experiences snowfall from Nor'easter cyclones.
-
-
-
-
-
-
-
- ## Panhandle Hooker
- - **Spatial Extent:** Central United States
- Panhandle Hookers are another type of low pressure system that can bring blizzard conditions to portions of CONUS. These cyclones originate in the Texas and Oklahoma Panhandles and are known for their distinctive ‘hooking’ storm track - hence their unique name. These systems typically form when a strong blast of cold Arctic air from the north (known as Blue Northers) collides with warm, moist air from the Gulf of America in the southern Plains. As the storm intensifies, it takes a northeastward "hook-shaped" trajectory, moving across the central Plains, the Midwest, and into the Great Lakes region. Panhandle Hookers most often bring heavy snow and blizzard conditions to parts of the central Great Plains, Upper Midwest, and Great Lakes, while the southern Plains and Ohio Valley may experience mixed precipitation or severe weather on the warmer side of the storm. These systems are a major source of snow for the central United States and, depending on their exact track, can also more uncommonly impact the northeastern U.S. with snow or ice. The strong winds and rapid intensification of these storms make them a hallmark of winter weather in the Plains and Midwest.
-
-
-
-
-
- Climatologically favored region of the USA that experiences snowfall from Panhandle Hooker cyclones.
-
-
-
-
-
-
-
-
- ## Access the Data
-
- These data products were interpolated from confirmed blizzard reports from the [Storm Events Database](https://www.ncdc.noaa.gov/stormevents/), between 1950-2021.
-
-
-
-
-
-
-
- ## Key Publications
-
- Coleman, J. S. M., and R. M. Schwartz, 2017: An Updated Blizzard Climatology of the Contiguous United States (1959–2014): An Examination of Spatiotemporal Trends. J. Appl. Meteor. Climatol., 56, 173–187, https://doi.org/10.1175/JAMC-D-15-0350.1.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **When Winter Rages**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/blizzard-goes-bombogenesis.data.mdx b/datasets/blizzard-goes-bombogenesis.data.mdx
deleted file mode 100644
index 7b97e9edd1..0000000000
--- a/datasets/blizzard-goes-bombogenesis.data.mdx
+++ /dev/null
@@ -1,147 +0,0 @@
----
-id: blizzard-goes-bombogenesis
-isHidden: true
-name: 'GOES Imagery - Bombogenesis (Select Event)'
-description: "Single GOES TrueColor three-band image of a mid-latitude cyclone undergoing bomb cyclogenesis and producing blizzard conditions on January 4, 2018."
-
-media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Snow
- - Weather
- - Hydrology
- - name: Source
- values:
- - NOAA
-layers:
- - id: blizzard-goes-bombogenesis
- stacCol: blizzard-goes-bombogenesis
- name: GOES Imagery - Bombogenesis (Jan 4 2018)
- type: raster
- description: "Single GOES TrueColor three-band image of a mid-latitude cyclone undergoing bomb cyclogenesis and producing blizzard conditions on January 4, 2018."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,15
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: categorical
- stops:
- - color: "rgba(0, 0, 0, 0)" # using transparent for now to fix the issues with scrollytelling
- label: "Imagery"
- info:
- source: NOAA
- spatialExtent: Eastern USA / Northwest Atlantic Ocean
- temporalResolution: January 4, 2018
- unit: N/A
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** 2018-01-04 1345 UTC
- - **Temporal Resolution:** Single Time Step
- - **Spatial Extent:** Eastern USA / Northwest Atlantic Ocean
- - **Spatial Resolution:** 0.5 km
- - **Data Type:** Research
-
-
-
-
- Single GOES TrueColor three-band image of a mid-latitude cyclone undergoing bomb cyclogenesis and producing blizzard conditions on January 4, 2018.
-
-
-
-
-
-
-
- ## About
-
- A defining feature of many nor’easters is bomb cyclogenesis, or **"bombogenesis"**, which occurs when a storm's central low pressure drops by at least 24 millibars in 24 hours, signifying rapid intensification of the system. The process is driven by the clash of cold air from the north and warm, humid air over the Atlantic. As the storm deepens, powerful winds spiral inward towards the central low pressure, enhancing snowfall rates and creating whiteout conditions. Bomb cyclogenesis often results in some of the most impactful nor’easters, which have been historically responsible for bringing record-breaking snowfall and hurricane-force winds to parts of the Northeast. The dataset is GOES satellite imagery of an example of a Noreaster undergoing bomb cyclogenesis off the East Coast of the USA on January 4, 2018. This TrueColor three-band composite satellite imagery is a combination of the red, green, and blue bands of GOES16 ABI. The green band was derived from the raw red, veggie, and blue bands.
-
-
-
-
-
-
- ## Access the Data
-
- Visit the Archived GOES Satellite [AWS S3 Bucket](https://registry.opendata.aws/noaa-goes/) to explore options for data access.
-
-
-
-
-
-
-
-
- ### Key Publications
-
- Manobianco, J., 1989: Explosive East Coast Cyclogenesis: Numerical Experimentation and Model-Based Diagnostics. Mon. Wea. Rev., 117, 2384–2405.
-
- Manobianco, J., 1989: Explosive East Coast Cyclogenesis over the West-Central North Atlantic Ocean: A Composite Study Derived from ECMWF Operational Analyses. Mon. Wea. Rev., 117, 2365–2383.
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **When Winter Rages**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/blizzard-merra2.data.mdx b/datasets/blizzard-merra2.data.mdx
deleted file mode 100644
index cd7d09cdad..0000000000
--- a/datasets/blizzard-merra2.data.mdx
+++ /dev/null
@@ -1,264 +0,0 @@
----
-id: blizzard-merra2
-name: 'MERRA2 Reanalysis (Select Events)'
-description: "MERRA2 reanalysis data of surface temperature, wind, mean sea level pressure, and cloud fraction for three select blizzard events."
-media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Snow
- - Weather
- - Hydrology
- - name: Source
- values:
- - NASA GMAO
-layers:
- - id: blizzard-merra2-2m-temp
- stacCol: blizzard-merra2-2m-temp
- name: MERRA2 2 Meter Temperature
- type: raster
- description: "MERRA2 2-meter temperature reanalysis data for three select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: surface_temperature
- nodata: -999
- rescale:
- - 240
- - 315
- legend:
- type: gradient
- unit:
- label: K
- min: 240
- max: 315
- stops:
- - "#3CCBCE"
- - "#C5F8FF"
- - "#FEC5FF"
- - "#E079FB"
- - "#094FC9"
- - "#009FFF"
- - "#44E2FF"
- - "#147F4F"
- - "#79B32C"
- - "#FDFE00"
- - "#FF8700"
- - "#FF0F00"
- - "#9D0F2B"
- - "#4D0000"
- - "#BA2E6D"
- info:
- source: NASA GMAO
- spatialExtent: Global
- temporalResolution: 1991-2016; Inconsistent
- unit: Kelvin
- - id: blizzard-merra2-10m-wind
- stacCol: blizzard-merra2-10m-wind
- name: MERRA2 10 Meter Wind
- type: raster
- description: "MERRA2 10-meter wind reanalysis data for three select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: ylorrd
- nodata: -999
- rescale:
- - 0
- - 25
- legend:
- type: gradient
- unit:
- label: m/s
- min: 0
- max: 25
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: NASA GMAO
- spatialExtent: Global
- temporalResolution: 1991-2016; Inconsistent
- unit: m/s
- - id: blizzard-merra2-cfrac
- stacCol: blizzard-merra2-cfrac
- name: MERRA2 Cloud Fraction
- type: raster
- description: "MERRA2 cloud fraction reanalysis data for three select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: greys_r
- nodata: -999
- rescale:
- - 0
- - 1
- legend:
- type: gradient
- min: 0
- max: 1
- stops:
- - "#000000"
- - "#404040"
- - "#7a7a7a"
- - "#b5b5b5"
- - "#e2e2e2"
- - "#ffffff"
-
- info:
- source: NASA GMAO
- spatialExtent: Global
- temporalResolution: 1991-2016; Inconsistent
- unit: fraction; 1 = 100% coverage
-
- - id: blizzard-merra2-mslp
- stacCol: blizzard-merra2-mslp
- name: MERRA2 Mean Sea Level Pressure
- type: raster
- description: "MERRA2 mean sea level pressure (MSLP) reanalysis data for three select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: nipy_spectral
- nodata: -999
- rescale:
- - 972
- - 1040
- legend:
- unit:
- label: hPa
- type: gradient
- min: 972
- max: 1040
- stops:
- - "#000000"
- - "#870098"
- - "#0078dd"
- - "#00aa88"
- - "#00dc00"
- - "#efed00"
- - "#fe0000"
- - "#cccccc"
- info:
- source: NASA GMAO
- spatialExtent: Global
- temporalResolution: 1991-2016; Inconsistent
- unit: hPa
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** 1991-2016
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 0.625x0.5 degree
- - **Data Units:** Temperature - Kelvin; Wind - m/s; MSLP - hPa; Cloud Fraction - fraction
- - **Data Type:** Research
-
-
-
-
- ERA5 reanalysis 10 meter wind speeds from the strongest timeframe of the 1993 Storm of the Century along the Eastern Seaboard of the USA.
-
-
-
-
-
-
-
- ### About
-
- The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is NASA’s atmospheric reanalysis dataset that spans from 1980 to the present. Produced by the *lobal Modeling and Assimilation Office (GMAO), MERRA-2 improves upon its predecessor by incorporating aerosol interactions, bias corrections for satellite radiances, and observed precipitation to improve the representation of the hydrological cycle within the dataset. It provides global atmospheric, land surface, and oceanic variables such as temperature, wind speed, cloud cover, and pressure at 0.5° x 0.625° spatial resolution with hourly to monthly temporal coverage. MERRA-2 is widely used for climate studies, weather analysis, and Earth system research, particularly for investigating long-term atmospheric trends and variability.
-
-
-
-
-
-
- ### Access the Data
-
- Visit NASA's [Global Modeling and Assimilation Office page](https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/) to explore options for data access.
-
-
-
-
-
-
-
-
- ### Key Publications
-
- Gelaro, R., McCarty, W., Suárez, M. J., et al. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **When Winter Rages**
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/blizzard-snowfall.data.mdx b/datasets/blizzard-snowfall.data.mdx
deleted file mode 100644
index 8b3d810730..0000000000
--- a/datasets/blizzard-snowfall.data.mdx
+++ /dev/null
@@ -1,155 +0,0 @@
----
-id: blizzard-snowfall
-name: 'Regional Snowfall Index Accumulated Snowfall (Select Events)'
-description: "Regional Snowfall Index (RSI) accumulated snowfall reports interpolated for five select blizzard events."
-
-media:
- src: ::file media/blizzards-cover.png
- alt: Near-blizzard conditions in Maria Stein, Ohio on December 23, 2022.
- author:
- name: Andrew Blackford
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Snow
- - Weather
- - Hydrology
- - name: Source
- values:
- - NCEI
-layers:
- - id: blizzard-snowfall
- stacCol: blizzard-snowfall
- name: Regional Snowfall Index (RSI) Accumulated Snowfall
- type: raster
- description: "RSI accumulated snowfall reports interpolated for five select blizzards."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap: '{"6":[30,144,201,255],"12":[171,148,214,255],"18":[90,22,128,255],"24":[217,67,212,255]}'
- nodata: -128
- legend:
- type: categorical
- min: "6"
- max: "24"
- stops:
- - color: "#1e90c9"
- label: "6"
- - color: "#ab94d6"
- label: "12"
- - color: "#5a1680"
- label: "18"
- - color: "#d943d4"
- label: "24"
- info:
- source: UAH
- spatialExtent: United States
- temporalResolution: 1950-2016; Inconsistent
- unit: Inches (in)
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** 1950-2016
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** United States
- - **Spatial Resolution:** N/A
- - **Data Units:** Inches (in)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- RSI reported accumulated snowfall that occurred from the Great Blizzard of 1978 in the Great Lakes region of the USA.
-
-
-
-
-
-
-
- ### About
-
- The Regional Snowfall Index (RSI) is a scale used to assess the societal impacts of major snowfall events across different regions in the United States. It accounts for snowfall amounts, the area affected, and population exposure to classify storms from Category 1 (Notable) to Category 5 (Extreme). Snowfall data for RSI calculations often come from NOAA’s Storm Events Database, which compiles reports from the National Weather Service (NWS), cooperative observers, and storm spotters. Additionally, automated observations from Automated Surface Observing Systems (ASOS) provide real-time snowfall measurements at airports and other key locations, contributing to the overall dataset used to analyze storm severity and impact.
-
-
-
-
-
-
- ### Access the Data
-
- This dataset was interpolated from confirmed blizzard reports from the [Storm Events Database](https://www.ncdc.noaa.gov/stormevents/), between 1950-2021.
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **When Winter Rages**
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Coleman, J. S. M., and R. M. Schwartz, 2017: An Updated Blizzard Climatology of the Contiguous United States (1959–2014): An Examination of Spatiotemporal Trends. J. Appl. Meteor. Climatol., 56, 173–187, https://doi.org/10.1175/JAMC-D-15-0350.1.
-
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/caldor-fire-characteristics-burn-severity.data.mdx b/datasets/caldor-fire-characteristics-burn-severity.data.mdx
deleted file mode 100644
index 272375e300..0000000000
--- a/datasets/caldor-fire-characteristics-burn-severity.data.mdx
+++ /dev/null
@@ -1,143 +0,0 @@
----
-id: caldor-fire-behavior-burn-severity
-name: "Caldor Fire Behavior and Burn Severity"
-isHidden: true
-description: "Assets describing the progression and active fire behavior of the 2021 Caldor Fire in California."
-media:
- src: ::file media/caldor_fire_behavior_and_burn_severity.thumbnail.jpg
- alt: Professional Firefighters Extinguishing Large, High-Priority Part of the Forest Fire.
- author:
- name: Gorodenkoff
- url: https://as1.ftcdn.net/v2/jpg/05/69/13/76/1000_F_569137667_HesDs5hfaLGGeY215cDghIQTxFDhO82U.jpg
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- This dataset describes the progression and active fire behavior of the 2021 Caldor Fire in California, as recorded by the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. It includes an extra layer detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team.
-layers:
- - id: caldor-fire-behavior
- stacCol: caldor-fire-behavior
- name: Fire Behavior
- type: raster
- description: ""
- initialDatetime: newest
- zoomExtent:
- - 10
- - 20
- sourceParams:
- colormap_name: inferno_r
- rescale:
- - 0
- - 93
- legend:
- type: gradient
- min: "0"
- max: "93"
- stops:
- - "#FBB41A"
- - "#BB3754"
- - "#781D6D"
- - "#34095F"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Categorical
- - id: caldor-fire-burn-severity
- stacCol: caldor-fire-burn-severity
- name: Burn Severity
- type: raster
- description: "Soil burn severity (SBS) conditions."
- initialDatetime: newest
- zoomExtent:
- - 10
- - 20
- sourceParams:
- colormap_name: inferno_r
- rescale:
- - 0
- - 5
- legend:
- type: gradient
- min: "0"
- max: "5"
- stops:
- - "#FBB41A"
- - "#BB3754"
- - "#781D6D"
- - "#34095F"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Categorical
----
-
-
-
- ## Dataset Details
- **Temporal Extent:** August 15, 2021
- **Spatial Extent:** California counties: El Dorado, Alpine and Amador
- **Spatial Resolution:** 30 meters
- **Data Type:** Research
-
-
-
-
-
-
- ## About
-
- This dataset describes the progression and active fire behavior of the 2021 Caldor Fire in California, as recorded by the algorithm detailed in https://www.nature.com/articles/s41597-022-01343-0. It includes an extra layer detailing the soil burn severity (SBS) conditions provided by the [Burned Area Emergency Response](https://burnseverity.cr.usgs.gov/baer/) team.
-
- The BARC has four classes of burn severity which include: high, moderate, low, and unburned. "Warm" colors indicate higher severity (red = "high" and yellow = "moderate") and "cool" colors indicate lower severity ("low" and "unburned"). Color definitions for each class are available in the [attribute table](https://burnseverity.cr.usgs.gov/baer/node/2046) for GeoTIFFs and in legends associated with other graphic products.
-
- This map then serves as a key component in the subsequent flood modeling and Geographic Information System (GIS) analysis. The BARC data is meant to be used as a main input into the development of a final soil burn severity map.
-
-
-
-
-
-
- ## Scientific Details
-
- For commonly asked questions about BARC or burn severity, please visit the following [BAER webpage](https://burnseverity.cr.usgs.gov/baer/faqs).
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/camp-fire-albedo-wsa-diff.data.mdx b/datasets/camp-fire-albedo-wsa-diff.data.mdx
deleted file mode 100644
index 44b2b34db4..0000000000
--- a/datasets/camp-fire-albedo-wsa-diff.data.mdx
+++ /dev/null
@@ -1,242 +0,0 @@
----
-id: campfire_albedo_wsa_difference_2015_2022
-name: "California Camp Fire Impacts observed from MODIS"
-isHidden: true
-description: "3-year average difference (2018-2022) - (2015-2018) of albedo, land surface temperature, and vegetation from MODIS for the 2018 California Camp Fire."
-media:
- src: ::file media/camp_fire_burn_scar_all.thumbnail.jpg
- alt: Brush and Tree Landscape Burning with Flames and Smoke During California Wildfire
- author:
- name: Erin
- url: https://as2.ftcdn.net/v2/jpg/02/33/05/25/1000_F_233052573_5Xpiw48FkuBnLzxopKwKHo2boD5QaZJW.jpg
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - MODIS
-infoDescription: |
- ::markdown
- In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo (WSA), and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values. This dataset is the Albedo WSA difference portion of that analysis.
-layers:
- - id: modis-albedo-wsa-diff-2015-2022
- stacCol: campfire-albedo-wsa-diff
- name: White Sky Albedo (WSA) Difference
- type: raster
- description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived Albedo over the 2018 Camp Fire burn scar domain."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - -0.1
- - 0.1
- nodata: -9999
- legend:
- type: gradient
- min: "-0.1"
- max: "0.1"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffff"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
-
- - id: modis-lst-day-diff-2015-2022
- stacCol: campfire-lst-day-diff
- name: Daytime Land Surface Temperature (LST) Difference
- type: raster
- description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived daytime land surface temperature over the 2018 Camp Fire burn scar domain."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - -7.5
- - 7.5
- nodata: -9999
- legend:
- unit:
- label: °C
- type: gradient
- min: "-7.5"
- max: "7.5"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffff"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
-
- - id: modis-lst-night-diff-2015-2022
- stacCol: campfire-lst-night-diff
- name: Nighttime Land Surface Temperature (LST) Difference
- type: raster
- description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived nighttime land surface temperature over the 2018 Camp Fire burn scar domain."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - -1.75
- - 1.75
- nodata: -9999
- legend:
- unit:
- label: °C
- type: gradient
- min: "-1.75"
- max: "1.75"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffff"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
-
- - id: modis-ndvi-diff-2015-2022
- stacCol: campfire-ndvi-diff
- name: Normalized Difference Vegetation Index (NDVI) Difference
- type: raster
- description: "3-year average difference (2018-2022) - (2015-2018) MODIS-derived normalized difference vegetation index over the 2018 Camp Fire burn scar domain."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - -0.5
- - 0.5
- nodata: -9999
- legend:
- type: gradient
- min: "-0.5"
- max: "0.5"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffff"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2015-2022
- - **Spatial Extent:** Butte County, California
- - **Spatial Resolution:** 1 km
- - **Data Units:** Albedo & NDVI (unitless); LST (°C)
- - **Data Type:** Research
-
-
-
-
- Satellite observations show that loss of vegetation caused by the Camp Fire leads to an average decrease in night time LST of 0.35 °C (0.64 °F). Locally, decreases in night time LST can be as much as 1.5 °C.
-
-
-
-
-
-
-
- ## About
-
- In order to examine how the fire event affected the changes in surface properties, we utilized the MODIS-derived Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) products for a six-year period centered on the Camp Fire event (2015-2022). We used these products which are available at 16-day intervals to compute monthly averaged spatial maps of NDVI, albedo, and LST. The monthly average spatial maps were then averaged over the areas affected by the Camp Fire to compute monthly mean values.
-
- [Normalized Difference Vegetation Index](https://www.earthdata.nasa.gov/topics/land-surface/land-use-land-cover/land-use-land-cover-classification/vegetation-index-0): A numerical value used to predict or assess vegetative characteristics such as plant leaf area, total biomass, and general health and vigor of the surface vegetation.
-
- [White Sky Albedo (WSA)](https://www.earthdata.nasa.gov/topics/land-surface/surface-radiative-properties/albedo): The ratio of sunlight reflected from the land surface to sunlight reaching the land surface.
-
- [Land Surface Temperature](https://www.earthdata.nasa.gov/topics/land-surface/surface-thermal-properties/land-surface-temperature): How hot the “surface” of Earth would feel to the touch in a particular location.
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Wildfires Affect Local Weather, Climate, and Hydrology**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/cattle-grasslands-pasture.data.mdx b/datasets/cattle-grasslands-pasture.data.mdx
deleted file mode 100644
index af56a454af..0000000000
--- a/datasets/cattle-grasslands-pasture.data.mdx
+++ /dev/null
@@ -1,472 +0,0 @@
----
-id: cattle-grasslands-pasture
-isHidden: true
-name: 'Cattle grasslands and pasture county level data'
-description: "Cattle grasslands and pasture county level data"
-media:
- src: ::file media/CMIP-winter-median.jpeg
- alt: Photo of Nisqually glacier
- author:
- name: Justin Pflug
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Snow
- - Precipitation
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA EIS
- - CMIP6
-infoDescription: |
- ::markdown
- Cattle and Vegetation Changes.
-layers:
- - id: county_grasslands_2019_pastu
- stacCol: county_grasslands_pasture_2019-v1
- name: 'County Grassland Land Percentage'
- type: raster
- description: 'County-level percentage of grassland estimated from NLCD land cover data'
- sourceParams:
- assets: grasslands
- resampling: bilinear
- bidx: 1
- nodata: -9999
- colormap_name: ylorrd
- rescale:
- - 0
- - 100
- legend:
- type: gradient
- label: Percentage of grassland
- unit:
- label: Percentage(%)
- min: "0"
- max: "88"
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: Maheshwari Neelam
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percentage
-
-
- - id: county_pasture_2019_pastu
- stacCol: county_grasslands_pasture_2019-v1
- name: 'County Pasture Land Percentage'
- type: raster
- description: 'County-level percentage of pasture estimated from NLCD land cover data'
- sourceParams:
- assets: pasture
- resampling: bilinear
- bidx: 1
- nodata: -9999
- colormap_name: ylorrd
- rescale:
- - 0
- - 67
- legend:
- type: gradient
- label: Percentage of pasture
- unit:
- label: Percentage (%)
- min: "0"
- max: "67"
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: Maheshwari Neelam
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percentage
-
-
- - id: usda_cattle_large_AFOs_2017
- stacCol: usda_cattle_AFOs_2017-v1
- name: 'USDA Cattle Large Animal Feeding Operations'
- type: raster
- description: 'Cattle: County-Level Distribution of Large (Number of operations that managed 200 or more heads) Animal Feeding Operations (AFO).'
- sourceParams:
- assets: large_AFOs
- resampling: bilinear
- bidx: 1
- nodata: -9999
- colormap_name: ylorrd
- rescale:
- - 0
- - 304
- legend:
- type: gradient
- label: Number of large AFOs
- unit:
- label: Number of large AFOs
- min: "0"
- max: "304"
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: USDA Census
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Number of large AFOs
-
- - id: usda_cattle_medium_AFOs_2017
- stacCol: usda_cattle_AFOs_2017-v1
- name: 'USDA Cattle Medium Animal Feeding Operations'
- type: raster
- description: 'Cattle: County-Level Distribution of Medium (Number of operations that managed between 50 and 199 heads) Animal Feeding Operations (AFO).'
- sourceParams:
- assets: medium_AFOs
- resampling: bilinear
- bidx: 1
- nodata: -9999
- colormap_name: ylorrd
- rescale:
- - 0
- - 1508
- legend:
- type: gradient
- label: Number of medium AFOs
- unit:
- label: Number of medium AFOs
- min: "0"
- max: "1508"
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: USDA Census
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Number of medium AFOs
-
-
- - id: usda_cattle_small_AFOs_2017
- stacCol: usda_cattle_AFOs_2017-v1
- name: 'USDA Cattle Small Animal Feeding Operations'
- type: raster
- description: 'Cattle: County-Level Distribution of Small (Number of operations that managed less than 50 heads) Animal Feeding Operations (AFO).'
- sourceParams:
- assets: small_AFOs
- resampling: bilinear
- bidx: 1
- nodata: -9999
- colormap_name: ylorrd
- rescale:
- - 0
- - 2312.0
- legend:
- label: Number of small AFOs
- unit:
- label: Number of small AFOs
- min: "0"
- max: "2312.0"
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: USDA Census
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Number of small AFOs
-
-
- - id: usda_cattle_total_heads_2017
- stacCol: usda_cattle_AFOs_2017-v1
- name: 'County Distribution of Total Number of Heads'
- type: raster
- description: 'Cattle: County-Level Distribution of Total Number of Heads.'
- sourceParams:
- assets: total_heads
- resampling: bilinear
- bidx: 1
- nodata: -9999
- colormap_name: ylorrd
- rescale:
- - 0
- - 1057272.0
- legend:
- type: gradient
- label: Total number of cattle heads
- unit:
- label: Total number of cattle heads
- min: "0"
- max: "1057272.0"
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
- info:
- source: USDA Census
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Total number of cattle heads
-
-
- - id: smap-L3-soil-moisture-AM-v1
- stacCol: smap-L3-soil-moisture-AM-v1
- name: 'SMAP L3 Soil Moisture AM passes (cm3/cm3)'
- type: raster
- description: 'This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) L-Band radiometer. The daily data here were collected from the descending (local solar time of 6 am) and ascending (local solar time of 6 pm) passes.'
- initialDatetime: newest
- sourceParams:
- assets: cog_default
- resampling: bilinear
- bidx: 1
- nodata: -9999
- colormap_name: bwr_r
- rescale:
- - 0.020
- - 0.926
- compare:
- datasetId: cattle-grasslands-pasture
- layerId: smap-L3-soil-moisture-AM-v1
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL dd yyyy')} VS ${dateFns.format(compareDatetime, 'LLL dd yyyy')}`;
- }
- legend:
- type: gradient
- label: cm3/cm3
- unit:
- label: cm3/cm3
- min: "0.020"
- max: "0.926"
- stops:
- - rgb(255, 0, 0)
- - rgb(255, 34, 34)
- - rgb(255, 96, 96)
- - rgb(255, 158, 158)
- - rgb(255, 220, 220)
- - rgb(220, 220, 255)
- - rgb(158, 158, 255)
- - rgb(96, 96, 255)
- - rgb(34, 34, 255)
- - rgb(0, 0, 255)
- info:
- source: NSIDC
- spatialExtent: Global
- temporalResolution: Daily
- unit: cm3/cm3
-
- - id: ndvi-global-2022
- stacCol: ndvi-global-2022
- name: 'NDVI from AVHRR GIMMS-3G+'
- type: raster
- description: 'This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions.'
- initialDatetime: newest
- sourceParams:
- assets: cog_default
- resampling: bilinear
- bidx: 1
- nodata: 0
- colormap_name: rdylgn
- rescale:
- - -0.3
- - 0.9
- compare:
- datasetId: cattle-grasslands-pasture
- layerId: ndvi-global-2022
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL dd yyyy')} VS ${dateFns.format(compareDatetime, 'LLL dd yyyy')}`;
- }
- legend:
- type: gradient
- label: NDVI
- min: -0.3
- max: 0.9
- stops:
- - '#a50026' # dark red
- - '#d73027' # red
- - '#fdae61' # orange
- - '#ffffbf' # pale yellow
- - '#a6d96a' # light green
- - '#1a9850' # green
- - '#006837' # dark green
- info:
- source: NSIDC
- spatialExtent: Global
- temporalResolution: Daily
- unit: NDVI
-
----
-
-
-
-
-
- County grassland
-
-
-
-
-
-
-
-
- County pasture
-
-
-
-
-
-
-
-
-
- usda_cattle_large_AFOs_2017
-
-
-
-
-
-
-
-
- usda_cattle_medium_AFOs_2017
-
-
-
-
-
-
-
-
- usda_cattle_small_AFOs_2017
-
-
-
-
-
-
-
-
- usda_cattle_total_heads_2017
-
-
-
-
-
-
-
-
- SMAP L3 Soil Moisture
-
-
-
-
-
-
-
-
- NDVI
-
-
-
diff --git a/datasets/co2.data.mdx b/datasets/co2.data.mdx
deleted file mode 100644
index 3e5f833877..0000000000
--- a/datasets/co2.data.mdx
+++ /dev/null
@@ -1,228 +0,0 @@
----
-id: co2
-name: "Carbon Dioxide"
-description: "The Impact of the COVID-19 Pandemic on Atmospheric CO2"
-media:
- src: ::file media/co2--dataset-cover.jpg
- alt: Power plant shooting steam at the sky.
- author:
- name: Marek Piwnicki
- url: https://unsplash.com/photos/WiZOyYqzUss
-taxonomy:
- - name: Topics
- values:
- - Air Quality
- - Greenhouse Gases
- - name: Subtopics
- values:
- - COVID-19
- - name: Source
- values:
- - GOSAT
-
-infoDescription: |
- ::markdown
- The Impact of the COVID-19 Pandemic on Atmospheric CO2
-layers:
- - id: co2-mean
- stacCol: co2-mean
- name: Mean CO2
- type: raster
- description: "The average background concentration of carbon dioxide (CO₂) in our atmosphere."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylbu_r
- rescale:
- - 0.000408
- - 0.000419
- compare:
- datasetId: co2
- layerId: co2-mean
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- legend:
- type: gradient
- min: "< 408 ppm"
- max: "> 419 ppm"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffbf"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: ppm
- - id: co2-diff
- stacCol: co2-diff
- name: Difference CO2
- type: raster
- description: "The changes in carbon dioxide (CO₂) levels in our atmosphere versus previous years."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: coolwarm
- rescale:
- - -0.0000015
- - 0.0000015
- legend:
- unit:
- label: ppm
- type: gradient
- min: "-1.5"
- max: "1.5"
- stops:
- - "#384cc2"
- - "#6384eb"
- - "#8fb3fd"
- - "#bad1f8"
- - "#dedbda"
- - "#f4c3ac"
- - "#f39779"
- - "#db5c48"
- - "#b50021"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: ppm
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 01, 2015 - February 13, 2022
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 50 kilometers
- - **Data Units:** Parts per million (ppm)
- - **Data Type:** Research
-
-
-
-
- Mean CO2 (parts per million) over the United States on June 1, 2021.
-
-
-
-
-
-
-
- ## Tracking CO2
-
- Lockdowns and other social distancing measures implemented in response to the COVID-19 pandemic have led to temporary reductions in carbon dioxide (CO2) emissions from fossil fuel combustion and other human activities.
-
-Scientists largely agree that the build-up of excess CO2 and other greenhouse gases within Earth's atmosphere has contributed to the rapid increase of global climate change. Determining whether these temporary reductions in CO2 emission are significant enough to contribute to the overall lowering of the world's carbon footprint will require more time and rigorous scientific study.
-
-However, initial studies suggest that although COVID-19-related CO2 emission reductions are expected to slow the speed at which CO2 accumulates in the atmosphere, they will not reduce the overall atmospheric concentration of CO2.
-
-CO2 emission reductions have been accompanied by comparable, or even greater, reductions in emissions of short-lived air pollutants, such as nitrogen dioxide (NO2). While fossil fuel combustion emits far more CO2 than NO2, much smaller relative changes are expected for atmospheric CO2 because it has a much longer atmospheric lifetime and there is much more CO2 in the atmosphere than NO2. Therefore, time-dependent, regional-scale changes in CO2 concentrations are expected to be no larger than 1 part per million (ppm), out of the normal 415 ppm CO2 background - a change of only 0.25%.
-
-To track atmospheric CO2 changes resulting from the lockdowns, observations collected by the NASA Orbiting Carbon Observatory-2 (OCO-2) satellite and Japan's Greenhouse gases Observing SATellite (GOSAT) during the first few months of 2020 were compared to results collected in previous years. The OCO-2 results were used to search for changes on regional scales over the globe. Targeted observations from GOSAT were used to track changes in large urban areas, such as Beijing, Tokyo, Mumbai, and New York. Both types of observations yielded key insights into the CO2 changes accompanying the economic disruptions caused by the COVID-19 pandemic.
-
-
-
-
-
-
- ### Regional Scale Changes in CO2 across the Globe
-
- To determine whether short-term reductions in CO2 emissions from coronavirus shutdowns are even detectable on a regional scale, scientists must create new methods of data analysis with enough sensitivity and precision to distinguish between normal seasonal changes in background CO2 levels and small perturbations caused by coronavirus shutdowns.
-
- To do this, scientists compare the timing of model-derived global atmospheric CO2 concentration variations constrained by OCO-2 measurements with CO2 emission changes estimated from fossil fuel use statistics from the Global Carbon Project. These comparisons focus on months coinciding with peak COVID-19 isolation periods to see if the emission reductions were accompanied by detectable, regional-scale CO2 changes.
-
- The maps below show these comparisons for the peak periods of the lockdowns in China (early February), southern Europe (early April) and the eastern U.S. (late April). The results show small (about 0.5 parts per million, or 0.125%) reductions in CO2 over each region at times that are well aligned with the largest CO2 emissions reductions in those regions reported by the Global Carbon Project. The CO2 map for late April (panel c) also appears to show a rebound in CO2 levels over East Asia and northern Pacific Ocean in late April, as China began to emerge from its coronavirus lockdowns. Many features are not likely to be associated with the lockdowns. The enhanced CO2 values in the southern hemisphere are probably due in part to the large wildfires over Australia in late December 2019, while the enhanced values in central Asia in April include contributions from wildfires in Siberia.
-
-
-
-
-
-
-
-
- **Top row**: Reported country-by-country reductions in fossil fuel use during the most intense periods of the COVID-19 lockdowns in a.) China (early February), b.) Europe (early April) and c.) Northeast U.S. (late April). Brighter blue colors indicate greater reductions.
- **Bottom row**: observed changes in atmospheric CO2 concentration differences derived from OCO-2 measurements. Blue shades indicate reductions in CO2, while red shades indicate increases relative to the baseline CO2 climatology.
-
-
-
-
-
-
-
-
- Monthly time series of lower atmospheric CO2 enhancements over Beijing, China for January 2017 through April 2020 derived from GOSAT data. The results for January through April of prior years are shown in blue, while those for 2020 are shown in green.
-
-
-
- ### CO2 Changes over Large Urban Areas
-
- Scientists use GOSAT data to determine changes in atmospheric CO2 over large urban areas, which experienced the largest changes in economic activity associated with the onset of the COVID-19 pandemic. While OCO-2 is optimized for detecting the subtle, regional-scale changes in CO2, GOSAT has advantages for tracking changes in CO2 emissions over large cities.
-
- GOSAT observations were analyzed to reveal CO2 concentration enhancements, such as fossil fuel emissions that contribute to higher levels of CO2 lower down in atmosphere over cities, relative to the CO2 concentrations at higher altitudes, which are less affected by city emissions. The figure below shows the CO2 concentration enhancements over Beijing, China, derived from GOSAT observations collected in January through April of each year from 2017 to 2020. The results from earlier years illustrate the amount of month-to-month variability in the observed CO2 enhancements that is typical during this season. However, while the CO2 concentration enhancements vary substantially from month-to-month, they are generally much lower in 2020 than in earlier years.
-
-
-
-
-
- Further inspection of the Beijing results reveals that all months in 2020 have smaller CO2 enhancements relative to prior years. While this behavior is consistent with reported COVID-19-related reductions in fossil fuel emissions from Beijing, it is important to remember that these results include variations in CO2 concentrations not only from COVID-19 shutdowns, but also from other processes such as photosynthesis and respiration by plants and transport by passing weather systems.
-
- Similar results were derived for the other cities. Shanghai shows reduced CO2 enhancements from February through April 2020. For New York, CO2 values were higher in January 2020, close to normal for February, and lower in March, as lockdowns were imposed. There is no data for New York in April due to cloud cover. In New Delhi, Mumbai and Dhaka, the story is somewhat more mixed. The CO2 enhancements are smaller or almost the same in February, reflecting the large role of natural processes, such as year-to-year differences in CO2 uptake and release by forests and crops. In March 2020, CO2 enhancements are higher than in earlier years in New Delhi, and lower in Mumbai and Dhaka. The CO2 enhancements decrease across all three cities in April, as lockdowns are implemented. However, these changes are very difficult to attribute to the pandemic because of the large-scale natural CO2 changes seen across India during this season.
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/conus-reach.data.mdx b/datasets/conus-reach.data.mdx
deleted file mode 100644
index 6ceaf85614..0000000000
--- a/datasets/conus-reach.data.mdx
+++ /dev/null
@@ -1,125 +0,0 @@
----
-id: conus-reach
-name: "Stream network across the Contiguous United States"
-description: "This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool"
-media:
- src: ::file media/CONUS_Nitrate_07012018.png
- alt: A map showing nitrate loads in the rivers of the United States on 07/01/2018
- author:
- name: NASA
- url: https://www.nasa.gov
-pubDate: 2023-03-03
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Hydrology
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool
-layers:
- - id: conus-reach
- stacCol: conus-reach
- name: Stream network across the Contiguous United States
- type: raster
- description: "This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: gnbu_r
- rescale:
- - 1
- - 1
- nodata: 65535
- info:
- source: NASA
- spatialExtent: Contiguous US
- temporalResolution: Annual
- unit: N/A
----
-
-
-
- ## Dataset Details
- - **Spatial Extent:** United States
- - **Spatial Resolution:** 100 meters
- - **Data Type:** Research
-
-
-
-
- River network across the Southeast United States.
-
-
-
-
-
-
-
- ## About
-
- This dataset describes the Stream network across the Contiguous United States delineated using Soil and Water Assessment Tool.
-
- The Soil & Water Assessment Tool is a small watershed to river basin-scale model used to simulate the quality and quantity of surface and ground water and predict the environmental impact of land use, land management practices, and climate change. SWAT is widely used in assessing soil erosion prevention and control, non-point source pollution control and regional management in watersheds.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **A New NASA Model Brings Open Science to Target Water Quality Problems**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional Resources
- [Soil & Water Assessment Tool](https://swat.tamu.edu/)
-
-
diff --git a/datasets/cyclone-events.data.mdx b/datasets/cyclone-events.data.mdx
deleted file mode 100644
index a97cbf4d51..0000000000
--- a/datasets/cyclone-events.data.mdx
+++ /dev/null
@@ -1,314 +0,0 @@
----
-id: cyclones-modis-sport-viirs
-name: 'Hurricane Beryl Remote Sensing Analysis'
-description: "Remote sensing information from select cyclone events"
-
-
-media:
- src: ::file ./media/cyclones_beryl_background.png
- alt: GOES TrueColor satellite imagery of Hurricane Beryl's eye in July of 2024.
- author:
- name: NOAA
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - Tropical
- - name: Source
- values:
- - MODIS
-layers:
- - id: modis_mosaic-cyclone-beryl
- stacCol: modis_mosaic-cyclone-beryl
- name: MODIS IR - Hurricane Beryl
- type: raster
- description: "Mosaiced MODIS infrared satellite imagery for Hurricane Beryl."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: cfastie
- nodata: -9999
- rescale:
- - 350
- - 30000
- legend:
- type: gradient
- stops:
- - "#000000" # Black
- - "#222222" # Dark Gray
- - "#555555" # Medium Gray
- - "#7F7F7F" # Light Gray
- - "#AFAFAF" # Very Light Gray
- - "#FFFFFF" # White
- - "#BFBFBF" # Light Gray (Post White)
- - "#808080" # Medium Gray (Post White)
- - "#404040" # Dark Gray (Post White)
- - "#0000FF" # Blue
- - "#5050FF" # Light Blue
- - "#00FF00" # Green
- - "#80FF00" # Yellow-Green
- - "#FFFF00" # Yellow
- - "#FFC000" # Orange-Yellow
- - "#FF8000" # Orange
- - "#FF0000" # Red
- - "#FF00A0" # Magenta-Red
- - "#FF00FF" # Magenta
- info:
- source: MODIS
- spatialExtent: Regional
- temporalResolution: 2000-Present, Inconsistent
- unit: N/A
- compare:
- datasetId: cyclones-modis-sport-viirs
- layerId: modis_mosaic-cyclone-beryl
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'yyyy-MM-dd')} VS ${dateFns.format(compareDatetime, 'yyyy-MM-dd')}`;
- }
-
- - id: sst-cyclone-beryl
- stacCol: sst-cyclone-beryl
- name: SSTs - Hurricane Beryl
- type: raster
- description: "Satellite-derived sea surface temperatures for Hurricane Beryl."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: turbo
- nodata: -9999
- rescale:
- - 295
- - 305
- legend:
- type: gradient
- unit:
- label: K
- min: 295
- max: 305
- stops:
- - "#30123b"
- - "#4146d1"
- - "#23d775"
- - "#f9f510"
- - "#fa6b02"
- - "#900c00"
- info:
- source: NASA SPoRT
- spatialExtent: Regional
- temporalResolution: Inconsistent
- unit: N/A
- compare:
- datasetId: cyclones-modis-sport-viirs
- layerId: sst-cyclone-beryl
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'yyyy-MM-dd')} VS ${dateFns.format(compareDatetime, 'yyyy-MM-dd')}`;
- }
-
- - id: viirs_mosaic-cyclone-beryl
- stacCol: viirs_mosaic-cyclone-beryl
- name: VIIRS IR - Hurricane Beryl
- type: raster
- description: "Mosaiced VIIRS infrared satellite imagery for Hurricane Beryl."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: cfastie
- nodata: -9999
- rescale:
- - 3500
- - 30000
- legend:
- type: gradient
- stops:
- - "#000000" # Black
- - "#222222" # Dark Gray
- - "#555555" # Medium Gray
- - "#7F7F7F" # Light Gray
- - "#AFAFAF" # Very Light Gray
- - "#FFFFFF" # White
- - "#BFBFBF" # Light Gray (Post White)
- - "#808080" # Medium Gray (Post White)
- - "#404040" # Dark Gray (Post White)
- - "#0000FF" # Blue
- - "#5050FF" # Light Blue
- - "#00FF00" # Green
- - "#80FF00" # Yellow-Green
- - "#FFFF00" # Yellow
- - "#FFC000" # Orange-Yellow
- - "#FF8000" # Orange
- - "#FF0000" # Red
- - "#FF00A0" # Magenta-Red
- - "#FF00FF" # Magenta
- info:
- source: VIIRS
- spatialExtent: Regional
- temporalResolution: 2000-Present, Inconsistent
- unit: N/A
- compare:
- datasetId: cyclones-modis-sport-viirs
- layerId: viirs_mosaic-cyclone-beryl
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'yyyy-MM-dd')} VS ${dateFns.format(compareDatetime, 'yyyy-MM-dd')}`;
- }
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** June 26, 2024 - July 11, 2024
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** Regional
- - **Spatial Resolution:** 1 km
- - **Data Type:** Research
-
-
-
-
-
- ## MODIS
- The Moderate Resolution Imaging Spectroradiometer (MODIS) Level-1B dataset provides high-quality satellite measurements of Earth's surface and atmosphere by capturing data across 36 different wavelength bands, ranging from visible to infrared. These measurements are carefully processed to ensure accuracy and are tied to specific locations on Earth. For certain bands, scientists can also analyze how sunlight interacts with surfaces, which helps in studying land and water properties. The dataset includes additional details like data quality, error estimates, and calibration information. Only Band 31 is being shown on this dataset landing page, which focuses on thermal infrared radiation, which is helpful in monitoring cloud top temperatures in cyclones.
-
-
-
-
-
- Mosaiced MODIS Infrared satellite imagery of Hurricane Beryl nearing the Caribbean Sea on July 1, 2024.
-
-
-
-
-
-
-
- ## SPoRT
- NASA’s Short-term Prediction Research and Transition (SPoRT) team provides a twice daily state of the art global Sea Surface Temperature (SST) product. SSTs are essential to hurricane forecasters in determining where a hurricane may be able to form and sustain itself, or even where it may be susceptible to weakening or strengthening. SSTs of approximately 26 °C (~80 °F) are the typical expected threshold of the amount of energy a hurricane requires from the surface of the ocean to form and sustain itself.
-
-
-
-
-
- Satellite-derived sea surface temperatures during Hurricane Beryl's peak intensity nearing the Caribbean Sea on July 1, 2024.
-
-
-
-
-
-
-
- ## VIIRS
- The Visible Infrared Imaging Radiometer Suite (VIIRS) Brightness Temperature (Band I5, Day) layer is the brightness temperature, measured in Kelvin (K), calculated from the top-of-the-atmosphere radiances. It does not provide an accurate temperature of either clouds or the land surface, but it does show relative temperature differences which can be used to distinguish features both in clouds and over clear land. It can be used to distinguish land, sea ice, and open water over the polar regions during winter (in cloudless areas). The VIIRS Brightness Temperature layer is calculated from VIIRS Calibrated Radiances (VNP02) and is available from the joint NASA/NOAA Suomi National Polar orbiting Partnership (Suomi NPP) satellite. The sensor resolution is 375m, the imagery resolution is 250m, and the temporal resolution is daily.
-
-
-
-
-
- Mosaiced VIIRS Infrared satellite imagery of Hurricane Beryl nearing the Caribbean Sea on July 1, 2024.
-
-
-
-
-
-
-
-
- ## Access the Data
-
- MODIS - Visit the NASA [LAADS DAAC](https://ladsweb.modaps.eosdis.nasa.gov/search/) to explore options for data access.
-
- SPoRT - Visit the NASA [SPoRT SST Page](https://weather.ndc.nasa.gov/sport/sst/) to explore options for data access.
-
- VIIRS - Visit the NASA [LAADS DAAC](https://ladsweb.modaps.eosdis.nasa.gov/search/) to explore options for data access.
-
-
-
-
-
-
-
-
- ## Key Publications
- Carl F. Schueler, John E. Clement, Philip E. Ardanuy, Carol Welsch, Frank DeLuccia, and Hilmer Swenson "NPOESS VIIRS sensor design overview", Proc. SPIE 4483, Earth Observing Systems VI, (18 January 2002); https://doi.org/10.1117/12.453451
-
- Petitcolin, F. and E. Vermote, 2002. Land surface reflectance, emissivity, and temperature from MODIS middle and thermal infrared data. Remote Sensing of Environment, 83(1). https://doi.org/10.1016/S0034-4257(02)00094-9
-
- S. L. Haines, G. J. Jedlovec and S. M. Lazarus, "A MODIS Sea Surface Temperature Composite for Regional Applications," in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 9, pp. 2919-2927, Sept. 2007, doi: 10.1109/TGRS.2007.898274.
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Hurricane Beryl**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
diff --git a/datasets/damage-probability-ian.data.mdx b/datasets/damage-probability-ian.data.mdx
deleted file mode 100644
index c165b98457..0000000000
--- a/datasets/damage-probability-ian.data.mdx
+++ /dev/null
@@ -1,160 +0,0 @@
----
-id: damage_probability_2022-10-03
-isHidden: true
-name: "NRT HLS-Derived Damage Probability Index"
-description: "Near real-time monitoring of land disturbances for CONUS based on the 30-m Harmonized Landsat Sentinel-2 (HLS) dataset."
-media:
- src: ::file ./media/HLS_Damage_Probability_Cover_Image_FL.jpg
- alt: Satellite imagery over Florida showing damage probability (using Viridis color ramp, with yellow being high probability and purple being low probability) for Oct 3, 2023.
- author:
- name: CONUS Disturbance Watcher
- url: https://gers.users.earthengine.app/view/nrt-conus
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - name: Source
- values:
- - HLS
-layers:
- - id: damage_probability_2022-10-03
- stacCol: damage_probability_2022-10-03
- name: Damage Probability
- type: raster
- description: "DPI values from 0 to 99. 0: no damage; 99: damage mostly likely"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: magma
- rescale:
- - 0
- - 99
- legend:
- type: gradient
- min: "0"
- max: "99"
- stops:
- - '#000004'
- - '#180f3d'
- - '#440f76'
- - '#721f81'
- - '#9e2f7f'
- - '#cd4071'
- - '#f1605d'
- - '#fd9668'
- - '#fec287'
- - '#f0f921'
----
-
-
-
- - **Temporal Extent:** January 1, 2022 - October 3, 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Continental United States
- - **Spatial Resolution:** 30 m x 30 m
- - **Data Units:** Disturbance probability from 0 to 1
- - **Data Type:** Research
- - **Data Latency:** Daily
-
-
-
-
- Damage Probability from UCONN GERS lab for Lee and Charlotte counties in Florida. This product is on a scale of 0 to 99 with 0 indicating 0 probability of damage and 99 indicating the highest probability of damage.
-
-
-
-
-
-
-
-
-## Overview
-The contiguous United States (CONUS) Disturbance Watcher system was built based on time-series analysis and machine learning techniques. The system consisted of two components. The first component, the retrospective disturbance analysis, extracted disturbance predictors from historical open access disturbance products and satellite datasets and built a series of machine learning models respectively for different lag stages. The second component, namely the near real-time monitoring, recursively updates per-pixel time-series models and outputted current vectors of disturbance predictor using Stochastic Continuous Change Detection (S-CCD) algorithm, and then applied the offline machine learning models from the first component to map disturbance probabilities and patches. The system could be updated regularly in an interval of one week, or updated as an user-defined interval, such as one day that was used for Hurricane Ian monitoring. The per-day updating interval provided the quickest mapping response and the most extensive damage region as some would be recovered after days, while the highest computational resources were needed.
-
-
-**Scientific Details:**
-The Hurricane Ian disturbance probability layer shows the likelihood impacted by Hurricane Ian. The data came from the map production of [CONUS Disturbance Watcher system](https://gers.users.earthengine.app/view/nrt-conus) on Oct. 3th, 2022, when the first post-hurricane Harmonized Landsat Sentinel-2 (HLS) 2.0 dataset on Sep. 30th, 2022 became available for downloading. The [HLS dataset](https://hls.gsfc.nasa.gov/) produce harmonized surface reflectance products from four satellite sensors, i.e., Landsat-8, Landsat-9, Sentinel-2A and Sentinel-2B, providing observation once every 2-3 days. The probability layer (scaled by 100) describes the probability produced by machine learning models. The higher probability reveals the high spectral similarity to the historical disturbance.
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Hurricane Ian and Impacts on Vegetation**
-
-
-
-
-
-## Source Data Product Citation
-Ye, S, Zhu, Z, 2023, Near Real-time Hurricane Ian Disturbance Probability Map: Global Environmental Remote Sensing Laboratory release
-
-## Key Publications
-Ye, S., Zhu, Z., & Suh, J. W. (2024). Leveraging past information and machine learning to accelerate land disturbance monitoring. Remote Sensing of Environment, 305, 114071, [https://doi.org/10.31223/X5WT2H](https://doi.org/10.31223/X5WT2H) .
-
-### Other Relevant Publications
-Ye, S., Rogan, J., Zhu, Z., & Eastman, J. R. (2021). A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection. Remote Sensing of Environment, 252, 112167, [https://doi.org/10.1016/j.rse.2020.112167](https://doi.org/10.1016/j.rse.2020.112167).
-
-Ye, S., Zhu, Z., & Cao, G. (2023). Object-based continuous monitoring of land disturbances from dense Landsat time series. Remote Sensing of Environment, 287, 113462, [https://doi.org/10.1016/j.rse.2023.113462](https://doi.org/10.1016/j.rse.2023.113462).
-
-
-
-
-
-
- ## Acknowledgment
- This work has been supported by the USGS-NASA Landsat Science Team (LST) Program for Toward Near Real-time Monitoring and Characterization of Land Surface Change for the Conterminous US (140G0119C0008)
-
-
-
-
-
-## License
-[Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/darnah-flood.data.mdx b/datasets/darnah-flood.data.mdx
deleted file mode 100644
index a584de2143..0000000000
--- a/datasets/darnah-flood.data.mdx
+++ /dev/null
@@ -1,154 +0,0 @@
----
-id: darnah-flood
-name: 'Darnah, Libya Flood'
-isHidden: true
-description: "HLS (SWIR FalseColor composites) imagery supporting the Darnah Flood Story"
-media:
- src: ::file media/darnah-flood-background.jpg
- alt: Aerial view over the Wadi Darnah River post-flood in Derna, Libya on September 14, 2023.
- author:
- name: Marwan Alfaituri (Reuters)
- url: https://abcnews.go.com/International/casualties-libya-floods-avoided-world-meteorological-organization-chief/story?id=103200104
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Precipitation
- - name: Source
- values:
- - HLS
- - GPM
-
-layers:
- - id: darnah-flood
- stacCol: darnah-flood
- name: HLS SWIR FalseColor Composite
- type: raster
- description: 'HLS falsecolor composite imagery using S30 Bands 12, 8A, and 4, over Darnah, Libya.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0
- - 5000
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- compare:
- datasetId: darnah-flood
- layerId: darnah-flood
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'LLL dd, yyyy')} VS ${dateFns.format(compareDatetime, 'LLL dd, yyyy')}`;
- }
- metadata:
- source: HLS
-
- - id: darnah-gpm-daily
- stacCol: darnah-gpm-daily
- name: GPM Accumulated Rainfall
- type: raster
- description: 'Accumulated Rainfall (mm) over the eastern Mediterranean Sea from Medicane Daniel (4 - 16 September, 2023).'
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
-
- sourceParams:
- colormap_name: inferno
- nodata: 0
- resampling: bilinear
- bidx: 1
- rescale:
- - 0.1
- - 500
-
- legend:
- type: gradient
- min: "0.1 mm"
- max: "500 mm"
- stops:
- - '#08041d'
- - '#1f0a46'
- - '#52076c'
- - '#f57c16'
- - '#f7cf39'
-
- metadata:
- source: GPM
----
-
-
-
-## Overview
-
-On Monday, September 11, 2023, the city of Darnah, Libya experienced the [deadliest flood disaster of the 21st century](https://www.google.com/url?q=https://www.aa.com.tr/en/environment/floods-in-libya-s-derna-worst-disaster-in-21st-century/2992617&sa=D&source=docs&ust=1709231595507737&usg=AOvVaw3MuRygRSSxtExzI_shVddG), and Africa’s deadliest flood ever recorded. A storm in the Mediterranean Sea dubbed ‘Medicane Daniel’ moved over northeastern Libya on the evening of the 10th, dumping prolific rain over the desert the morning of the 11th. A record 16” of rainfall was measured in 24 hours at the city of Al-Bayda, Libya (just west of Derna) from ‘Medicane’ Daniel. Two dams upstream of Darnah collapsed during the heavy rains leading to approximately [25% of the city being destroyed](https://www.google.com/url?q=https://www.reuters.com/world/africa/more-than-1000-bodies-recovered-libyan-city-after-floods-minister-2023-09-12/&sa=D&source=docs&ust=1709231595509452&usg=AOvVaw083l0kMybsbbwT18u4SVTm). The first dam broke around 3:00 AM local time on September 11th, and the second followed suit shortly thereafter, which exacerbated the death toll greatly. The International Committee of the Red Cross (ICRC) reported that proceeding the dam bursts, a wave as high as 23 feet (7 meters) rushed towards the city. With a population of 120,000, the major city of Darnah saw massive destruction, with entire districts of the city being washed away.Nearly 1,000 buildings are estimated to have been completely destroyed as well as 5 major bridges that connect the west and east sides of the city. The United Nations Office for the Coordination of Humanitarian Affairs initially reported a death toll currently sits at 11,300 with another 10,100 reported missing. This estimate was later revised to [3,958 fatalities](https://www.aljazeera.com/news/2023/9/18/libya-floods-conflicting-death-tolls-greek-aid-workers-die-in-crash) on September 18.
-
-
-
-
-
-
-
-## Scientific Research
-
-
-The primary dataset employed in analyzing the Darnah flood, alongside Integrated Multi-Satellite Retrivals for the Global Precipitation Measurement Mission (GPM IMERG) data, is a three-band HLS composite image created from the shortwave infrared, narrow near-infrared, and red bands of pre and post-flood HLS data, supplemented with total rainfall data from GPM taken from 5 to 16 September 2023. These scenes were acquired on September 7 and 22, 2023. The SWIR false color composite visually illustrates the extent of the greenup resulting from heavy rainfall leading to the flood, while GPM's total rainfall data provides insight into precipitation patterns associated with Medicane Daniel over the eastern Mediterranean region.
-
-These datasets support ongoing scientific research and analysis of the Darnah flood and its aftermath. They aid in assessing the flood's impact on local land cover, vegetation extent, sediment loading, and precipitation patterns. Furthermore, they facilitate the monitoring of long-term environmental recovery and ecosystem resilience, as well as evaluating the effectiveness of flood mitigation and dam rebuilding efforts upstream of Darnah.
-
-
-
-
-
-
- SWIR False color HLS imagery showing the destroyed area of Darnah, Libya along the Wadi Darnah River on 22 September 2023.
-
-
-
-
-
-
-
-## Interpreting the Data
-
-The HLS and GPM datasets concerning the Darnah Flood should be interpreted with careful consideration of temporal, spatial, and environmental factors.
-
-Temporal Aspects: The HLS SWIR FalseColor composite images were taken at 10:30 AM LST on September 7 and 22, 2023, corresponding to pre- and post-flood times. The GPM total rainfall data is a summed daily rainfall accumulation product that spans from September 5 to 16, 2023.
-
-Spatial Aspects: All three bands used from the HLS dataset are at 30-meter resolution, providing detailed spatial information. GPM's total rainfall data covers the entire eastern Mediterranean region, offering insights into the broader spatial distribution of precipitation associated with Medicane Daniel, and is at 0.1 by 0.1 degree spatial resolution.
-
-Environmental Aspects: When interpreting the data, it is crucial to consider the local topography and land cover. Darnah, situated along the southern coast of the Mediterranean Sea, experiences rapidly rising terrain to its south and lies within the Saharan Desert region.
-
-
-
-
-
-
-## Additional Resources
-
-* [Harmonized Landsat-Sentinel](https://hls.gsfc.nasa.gov/)
-
-* [European Commission Report 9/13/2023](https://upload.wikimedia.org/wikipedia/commons/2/2c/ECDM_20230913_FL_Libya.pdf)
-
-
-
-
-
-
-
-## Data Story
-
-* [The Deadliest Flood of the 21st Century](https://www.earthdata.nasa.gov/dashboard/stories/darnah-flood)
-
-
-
diff --git a/datasets/derecho-tornado-tracks.data.mdx b/datasets/derecho-tornado-tracks.data.mdx
deleted file mode 100644
index 4900a30bac..0000000000
--- a/datasets/derecho-tornado-tracks.data.mdx
+++ /dev/null
@@ -1,200 +0,0 @@
----
-id: tornado-tracks-derecho
-isHidden: true
-name: "May 12th, 2022 Tornado Tracks"
-description: "Utilizing NWS tornado track data to highlight the damage of the 05/12/2022 derecho."
-media:
- src: ::file ./media/derecho-background.jpg
- alt: Intense derecho thunderstorm as it sweeps across farmland on September 23, 2018
- author:
- name: Jim Reed
- url: https://fineartamerica.com/featured/4-derecho-thunderstorm-jim-reed-photographyscience-photo-library.html
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - name: Source
- values:
- - NWS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** May 12, 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 50 meters
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: tornado-tracks-derecho
- stacCol: tornado-tracks-derecho
- name: May 12th 2022 Tornadoes (Paths)
- type: raster
- description: "This dataset shows official NWS tornado center path lines categorized by maximum EF rating."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: tornado_ef_scale
- nodata: -999
- rescale:
- - 0
- - 255
- legend:
- type: categorical
- stops:
- - color: "#b3bcc9" # Grey for EFUNK
- label: EFUNK
- - color: "#add8e6" # Light blue for EF0
- label: EF0
- - color: "#90ee90" # Green for EF1
- label: EF1
- - color: "#ffe71f" # Yellow for EF2
- label: EF2
- - color: "#ffa500" # Orange for EF3
- label: EF3
- - color: "#ff0000" # Red for EF4
- label: EF4
- - color: "#ff00ff" # Pink for EF5
- label: EF5
- info:
- source: National Weather Service (NWS)
- spatialExtent: CONUS
- temporalResolution: N/A
- unit: N/A
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** May 12th 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 50 meters
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- EF-2 tornado path through Castlewood, South Dakota, produced from the Serial Derecho of May 12th, 2022.
-
-
-
-
-
-
-
- ### About
-
- The National Weather Service’s (NWS) Damage Assessment Toolkit (DAT) is a pivotal geographic information system (GIS)-hosted dataset designed to support post-storm damage surveys conducted by meteorologists. This toolkit plays a crucial role in documenting and analyzing tornado and significant straight-line wind damage across affected areas. Ground-based surveys are carried out to capture this information, which is then geospatially referenced and uploaded to the DAT database.
-
- This dataset encompasses comprehensive elements such as tornado track centerlines, polygons depicting Enhanced Fujita (EF) scale ratings along tornado paths, and detailed descriptions with meteorological statistics for each logged damage location. In some cases, it also includes imagery collected by survey teams, adding further context to damage assessments. The comprehensive information in this dataset makes it invaluable for researchers, planners, and emergency responders.
-
-
-
-
-
-
-
-
- ### What the DAT Offers
-
- * Tornado Track Centerlines: Geospatial data capturing the precise paths of tornadoes, providing insights into their trajectory and extent.
-
- * Enhanced Fujita (EF) Scale Polygons: Detailed polygons of the EF rating at each location along a tornado’s path, offering a better understanding of the severity of the storm across different points.
-
- * Location-Specific Damage Descriptions: Comprehensive descriptions of damage at each surveyed point, paired with relevant meteorological statistics to offer deeper insight into storm impacts.
-
- * Damage Imagery: When available, surveyor-captured images provide visual context to logged damage points, further enhancing data interpretation and analysis.
-
-
-
-
-
-
- ### Access the Data
-
- Visit the [Storm Damage Viewer](https://apps.dat.noaa.gov/StormDamage/DamageViewer/) page to explore a GIS-hosted page that contains the DAT dataset.
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- J. Parks Camp, NWSFO, Tallahassee, FL; and P. Kirkwood, J. G. LaDue, L. A. Schultz, and N. Parikh., National Weather Service Damage Assessment Toolkit: Transitioning to Operations, Abstract 9.1 presented at 2017 Annual Meeting, AMS, Seattle, Washington, 26 Jan.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Leonardo, D., 2011: Damage Assessment Toolkit business case analysis: NWS OSIP Project 08-024. NWS Rep., 16 pp., https://osip.nws.noaa.gov/osip/projectDetail.php?document=23295.
-
- Stellman, K., T. Brice, D. Hansing, A. Foster, C. Pieper, and K. Lander, 2009: How geographic information system software is improving the effectiveness of the National Weather Service. 89th Annual Meeting, New Orleans, LA, Amer. Meteor. Soc., 5A.11, http://ams.confex.com/ams/89annual/webprogram/Paper148642.html.
-
-
-
-
-
-
-
- ### Other Publications
-
- National Wind Institute, 2006: A recommendation for an enhanced Fujita scale (EF-scale). Texas Tech University Wind Science and Engineering Center Rep., 111 pp., www.depts.ttu.edu/nwi/Pubs/EnhancedFujitaScale/EFScale.pdf.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **NASA Data Fusion Analysis of Derechos and Their Impact on Rural America**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/derecho-windgusts.data.mdx b/datasets/derecho-windgusts.data.mdx
deleted file mode 100644
index 6b132e89cd..0000000000
--- a/datasets/derecho-windgusts.data.mdx
+++ /dev/null
@@ -1,155 +0,0 @@
----
-id: windgusts-derecho
-isHidden: true
-name: 'NCEI Interpolated Wind Gusts for the May 12th, 2022 Derecho'
-description: "NCEI Storm Events Database Wind Gusts that are Interpolated for the Derecho of May 12th, 2022"
-media:
- src: ::file ./media/derecho-background.jpg
- alt: Intense derecho thunderstorm as it sweeps across farmland on September 23, 2018
- author:
- name: Jim Reed
- url: https://fineartamerica.com/featured/4-derecho-thunderstorm-jim-reed-photographyscience-photo-library.html
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - Air Quality
- - name: Source
- values:
- - NCEI
-infoDescription: |
- ::markdown
- - **Temporal Extent:** May 12, 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Northern Plains region
- - **Spatial Resolution:** N/A
- - **Data Units:** Miles Per Hour (mph)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: windgusts-derecho
- stacCol: windgusts-derecho
- name: Interpolated NCEI Wind Gust Reports for the Derecho of May 12th, 2022
- type: raster
- description: "NCEI Storm Events Database Wind Gusts that are Interpolated for the Derecho of May 12th, 2022"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bupu
- rescale:
- - 75
- - 100
- legend:
- type: gradient
- unit:
- label: mph
- min: 70
- max: 95
- stops:
- - "#f7fcfd" #Very Pale Cyan
- - "#bfd3e6" #Pastel Blue
- - "#8c95c6" #Muted Lavender Blue
- - "#88409c" #Deep Violet
- - "#4d004b" #Dark Purple
- info:
- source: UAH
- spatialExtent: Northern Plains region
- temporalResolution: Daily
- unit: Miles Per Hour (mph)
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** May 12, 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Northern Plains region
- - **Spatial Resolution:** N/A
- - **Data Units:** Miles Per Hour (mph)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- NCEI Storm Events Database wind gusts that are interpolated for the Derecho of May 12th, 2022 within the Northern Plains region.
-
-
-
-
-
-
-
- ### About
-
- The National Centers for Environmental Information (NCEI) Storm Events Database is a comprehensive archive of severe weather reports across the United States, including wind gust data collected from various sources. This dataset compiles observations from the National Weather Service (NWS), trained storm spotters, and automated surface observing systems (ASOS), providing detailed records of wind events, including peak gust speeds, locations, and associated impacts.
-
- By analyzing wind gust reports from the NCEI Storm Events Database, researchers can assess the spatial and temporal distribution of high-wind events, identify trends in extreme wind occurrences, and evaluate their impacts on infrastructure, transportation, and public safety. This dataset is particularly valuable for studying severe convective wind events, derechos, and other high-impact wind phenomena, helping to inform mitigation strategies and improve forecasting efforts.
-
-
-
-
-
-
- ### Access the Data
-
- This dataset was interpolated from confirmed derecho reports from the [Storm Events Database](https://www.ncdc.noaa.gov/stormevents/), on May 12th, 2022.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Li, J., Geiss, A., Feng, Z., Leung, L. R., Qian, Y., and Cui, W., 2024: A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes. Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-112.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **NASA Data Fusion Analysis of Derechos and Their Impact on Rural America**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/derecho-wldas.data.mdx b/datasets/derecho-wldas.data.mdx
deleted file mode 100644
index 5dc57e0991..0000000000
--- a/datasets/derecho-wldas.data.mdx
+++ /dev/null
@@ -1,184 +0,0 @@
----
-id: wldas-derecho-sm
-isHidden: true
-name: "WLDAS Soil Moisture Dataset for May 11th, 2022"
-description: "WLDAS is a surface meteorological analysis and land-surface model dataset running in operations to produce outputs of soil moisture, snow, surface fluxes, streamflow, etc. for drought monitoring and other applications."
-media:
- src: ::file ./media/derecho-background.jpg
- alt: Intense derecho thunderstorm as it sweeps across farmland on September 23, 2018
- author:
- name: Jim Reed
- url: https://fineartamerica.com/featured/4-derecho-thunderstorm-jim-reed-photographyscience-photo-library.html
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - Air Quality
- - name: Source
- values:
- - WLDAS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** May 11th, 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Western CONUS
- - **Spatial Resolution:** 0.01° x 0.01°
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: wldas-derecho-sm
- stacCol: wldas-derecho-sm
- name: 0-10 cm Soil Moisture
- type: raster
- description: "0-10 cm Soil Moisture Dataset"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylgn
- rescale:
- - 0
- - 0.4
- legend:
- type: gradient
- unit:
- label: m3/m3
- min: "0"
- max: "0.4"
- stops:
- - "#a50026" #Deep Crimson
- - "#f46d43" #Burnt Orange
- - "#fee08b" #Pastel Yellow
- - "#d9ef8b" #Lime Yellow
- - "#66bd63" #Medium Green
- - "#006837" #Deep Forest Green
- info:
- source: WLDAS
- spatialExtent: Western CONUS
- temporalResolution: N/A
- unit: N/A
-
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** May 11th, 2022
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Western CONUS
- - **Spatial Resolution:** 0.01° x 0.01°
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- Topsoil moisture demonstrating drought condtions prior to the serial derecho of May 12th, 2022.
-
-
-
-
-
-
-
- ### About
-
- NASA’s Western Land Data Assimilation System (WLDAS) provides high-resolution soil moisture data, offering critical insights into land surface hydrology and water availability. This dataset integrates satellite observations and land surface modeling to generate detailed soil moisture estimates, enabling researchers to assess water storage variability and land-atmosphere interactions at multiple temporal and spatial scales.
-
- By analyzing soil moisture dynamics, WLDAS helps improve understanding of drought development, flood potential, and agricultural water stress. It supports early warning systems by identifying regions experiencing soil moisture deficits or surpluses, which is essential for managing water resources, optimizing irrigation strategies, and mitigating climate-related hazards. The dataset is particularly valuable for monitoring agricultural productivity, ecosystem responses, and hydrological extremes, informing policy decisions and adaptation strategies in a changing climate.
-
-
-
-
-
-
-
-
- ### What WLDAS Soil Moisture Data Offers
-
- * High-Resolution Soil Moisture Monitoring: Delivers detailed spatial and temporal assessments of soil moisture dynamics, essential for analyzing land-atmosphere interactions, hydrological cycles, and ecosystem responses.
-
- * Drought and Flood Risk Analysis: Supports quantitative evaluation of soil moisture anomalies, aiding in early detection of drought conditions and flood potential, which is critical for water resource management and hazard mitigation.
-
- * Agricultural and Environmental Applications: Enhances understanding of soil moisture variability’s impact on crop productivity, vegetation health, and biogeochemical processes, informing sustainable land-use planning and climate adaptation strategies.
-
-
-
-
-
-
-
- ### Access the Data
-
- Visit NASA's [WLDAS home page](https://ldas.gsfc.nasa.gov/wldas/wldas-project-goals) to explore options for data access.
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- Erlingis, J., M. Rodell, C.D. Peters-Lidard, B. Li, S.V. Kumar, J.S. Famiglietti, S.L. Granger, J.V. Hurley, P.-W. Liu, and D.M. Mocko, 2021: A High-Resolution Land Data Assimilation System Optimized for the Western United States. Journal of the American Water Resources Association, 1-19, doi:10.1111/1752-1688.12910
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Other Publications
-
- * Erlingis, J., B. Li, M. Rodell, NASA/GSFC/HSL (2024): WLDAS Noah-MP-3.6 Land Surface Model L4 Daily 0.01 degree x 0.01 degree Version D1.0, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], doi:10.5067/ABBHPUIGJH5M
-
- * Erlingis, J.M., M. Rodell, C.D. Peters-LIdard, B. Li, and S.V. Kumar, 2020: Applications of the Western Land Data Assimilation System. AGU Fall Meeting. Virtual, American Geophysical Union.
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **NASA Data Fusion Analysis of Derechos and Their Impact on Rural America**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
diff --git a/datasets/disalexi-etsuppression.data.mdx b/datasets/disalexi-etsuppression.data.mdx
deleted file mode 100644
index b33148f202..0000000000
--- a/datasets/disalexi-etsuppression.data.mdx
+++ /dev/null
@@ -1,182 +0,0 @@
----
-id: disalexi-etsuppression
-name: "DisALEXI ET Suppression"
-description: "Change in ET using DisALEXI model of OpenET observations for 2017-20 fires, calculated as the difference of ET in the immediate post-fire water year from ET in the immediate pre-fire water year"
-media:
- src: ::file media/disalexi_et_suppression.thumbnail.jpg
- alt: Evapotranspiration over Czech forests. Evaporation of water from spruce forests near Liberec. loss of water as vapor from leaf vents.
- author:
- name: Fauren
- url: https://as2.ftcdn.net/v2/jpg/04/10/94/25/1000_F_410942594_8Jzv5Aqv683Oke1zrU9s2lptsYaovJmt.jpg
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Evapotranspiration
- - Water Cycle
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- Impact of fires on changes in evapotranspiration, obtained OpenET observations (DisALEXI model) for 2017-20 fires
-layers:
- - id: disalexi-etsuppression
- stacCol: disalexi-etsuppression
- name: DisALEXI ET Anomalies
- type: raster
- description: "Standardized ET anomaly using DisALEXI model of OpenET observations for 2017-20 fires, calculated as the difference of ET in the immediate post-fire water year from ET in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- asset_bidx: cog_default|1
- colormap_name: rdylbu
- rescale:
- - -1.0
- - 1.0
- compare:
- datasetId: mtbs-burn-severity
- layerId: mtbs-burn-severity
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `ET Anomalies: ${dateFns.format(datetime, 'yyyy')} VS MTBS: ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- legend:
- type: gradient
- label: ET Anomalies
- min: "-1.0"
- max: "1.0"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Difference
- - id: mtbs-burn-severity
- stacCol: mtbs-burn-severity
- name: MTBS Burn Severity
- type: raster
- description: "Burn severities and extents of fires from Monitoring Trends in Burn Severity (MTBS) program during the years 2016-2021 over Western US"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylgn_r
- rescale:
- - 1
- - 4
- legend:
- type: categorical
- stops:
- - color: "#94c772"
- label: "Unburned"
- - color: "#faf88e"
- label: "Low"
- - color: "#ea915e"
- label: "Moderate"
- - color: "#971d2b"
- label: "High"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Categorical
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2017 - 2020
- - **Spatial Extent:** Western United States
- - **Spatial Resolution:** ~1 km
- - **Data Units**: Standardized anomaly
- - **Data Type:** Research
-
-
-
-
- High resolution observations of ET from OpenET (DisALEXI model) captures the impact of fires on vegetation. Large declines in post-fire standardized ET anomalies are observed (left) that correspond well with burn scars from the MTBS burn severity dataset (right) during the fire year 2020.
-
-
-
-
-
-
-
- ## About
-
- Impact of fires on changes in evapotranspiration, obtained OpenET observations (DisALEXI model) for 2017-20 fires
-
- Changes in ET are calculated as the difference of ET in the immediate post-fire water year from ET in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Hydrological Drivers and Impacts of Fire**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
-
- ## Additional Resources
-
- * [OpenET Product](https://openetdata.org/)
- * [MTBS - Project overview](https://www.mtbs.gov/project-overview)
- * [Interactive MTBS Viewer for Continental US](https://www.mtbs.gov/viewer/?region=conus)
-
-
\ No newline at end of file
diff --git a/datasets/ecco-surface-height-change.data.mdx b/datasets/ecco-surface-height-change.data.mdx
deleted file mode 100644
index 94d87d1e98..0000000000
--- a/datasets/ecco-surface-height-change.data.mdx
+++ /dev/null
@@ -1,126 +0,0 @@
----
-id: ecco-surface-height-change
-name: "ECCO sea-surface height change from 1992 to 2017"
-description: "Gridded global sea-surface height change from Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate."
-media:
- src: ::file media/ecco-surface-height-change--dataset-cover.jpg
- alt: Wave crashing on a sandy beach
- author:
- name: Lance Asper
- url: https://unsplash.com/photos/3P3NHLZGCp8
-taxonomy:
- - name: Topics
- values:
- - Sea Level Change
- - Water Resources
- - name: Subtopics
- values:
- - Coastal Risk
- - Hydrology
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- Gridded global sea-surface height change from 1992 to 2017 from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate. The dataset was calculated as the difference between the annual means over 2017 and 1992, from the 0.5 degree, gridded monthly mean data product available on [PO.DAAC](https://podaac.jpl.nasa.gov/dataset/ECCO_L4_SSH_05DEG_MONTHLY_V4R4).
-layers:
- - id: ecco-surface-height-change
- stacCol: ecco-surface-height-change
- name: ECCO sea-surface height change (m)
- type: raster
- description: "Gridded global sea-surface height change from 1992 to 2017 from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdbu
- rescale:
- - -0.313
- - 0.313
- legend:
- type: gradient
- min: "-0.31"
- max: "0.31"
- stops:
- - "#EF8A62"
- - "#F7F7F7"
- - "#67A9CF"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Annual
- unit: meters
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 1992 and 2017
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 0.5 x 0.5 degree
- - **Data Units:** meters (m)
- - **Data Type:** Research
-
-
-
-
- Change in sea surface height (m) between 1992 and 2017 as estimated from the ECCO model.
-
-
-
-
-
-
-
- ## About
-
- Gridded global sea-surface height change from 1992 to 2017 from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate. The dataset was calculated as the difference between the annual means over 2017 and 1992, from the 0.5 degree, gridded monthly mean data product available on [PO.DAAC](https://podaac.jpl.nasa.gov/dataset/ECCO_L4_SSH_05DEG_MONTHLY_V4R4).
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Unraveling the Components of Coastal Risk**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
diff --git a/datasets/epa-anthropogenic-methane.data.mdx b/datasets/epa-anthropogenic-methane.data.mdx
deleted file mode 100644
index d20e490aa9..0000000000
--- a/datasets/epa-anthropogenic-methane.data.mdx
+++ /dev/null
@@ -1,2733 +0,0 @@
----
-id: epa-ch4emission-yeargrid-v2express-manure
-isHidden: true
-name: U.S. Gridded Anthropogenic Methane Emissions Inventory
-description: Spatially disaggregated 0.1°x 0.1° maps of annual U.S. anthropogenic methane emissions from over 25 emission sources, consistent with the U.S. Inventory of Greenhouse Gas Emissions and Sinks.
-media:
- src: ::file media/epa-annual--cover.jpg
- alt: Total Gridded Methane Emissions from the U.S. Inventory of Greenhouse Gas Emissions and Sinks
- author:
- name: EPA
-taxonomy:
- - name: Topics
- values:
- - Air Quality
- - name: Subtopics
- values:
- - Surface Meteorology
- - Urban
- - name: Source
- values:
- - EPA
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- ::markdown
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- - Temporal Resolution: Annual
- - Spatial Extent: Contiguous United States
- - Spatial Resolution: 0.1° x 0.1°
- - Data Units: Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)
- - Data type: Research (v2 express extension)
- - Data Latency: N/A
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- - "#DA2222"
- - "#B8221E"
- - "#95211B"
- - "#721E17"
- - "#521A13"
- media:
- src: ::file ./media/epa-ch4emission-yeargrid-v2express.thumbnails.other.stationary.combustion.annual.png
- alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Stationary combustion (annual)
- - id: 1A-mobile-combustion-othe
- stacApiEndpoint: https://earth.gov/ghgcenter/api/stac
- tileApiEndpoint: https://earth.gov/ghgcenter/api/raster
- stacCol: epa-ch4emission-yeargrid-v2express
- name: Other - Mobile Combustion (annual)
- type: raster
- description: Annual methane emission fluxes from Mobile Combustion (inventory Energy 1A sub-category)
- initialDatetime: newest
- projection:
- id: "equirectangular"
- basemapId: "light"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: mobile-combustion-other
- colormap_name: epa-ghgi-ch4
- rescale:
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- datasetId: epa-ch4emission-yeargrid-v2express
- layerId: 1A-mobile-combustion-othe
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- analysis:
- exclude: true
- metrics:
- - mean
- sourceParams:
- dst_crs: "+proj=cea"
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- spatialExtent: Contiguous United States
- temporalResolution: Annual
- unit: Mg CH₄/km²/yr
- legend:
- unit:
- label: Mg CH₄/km²/yr
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- - "#721E17"
- - "#521A13"
- media:
- src: ::file ./media/epa-ch4emission-yeargrid-v2express.thumbnails.other.mobile.combustion.annual.png
- alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Mobile combustion (annual)
- - id: 1A-abn-ong-other
- stacApiEndpoint: https://earth.gov/ghgcenter/api/stac
- tileApiEndpoint: https://earth.gov/ghgcenter/api/raster
- stacCol: epa-ch4emission-yeargrid-v2express
- name: Other - Abandoned Oil and Gas Wells (annual)
- type: raster
- description: Annual methane emission fluxes from Abandoned Oil and Gas Wells (inventory Energy 1B2a and 1B2b sub-categories)
- initialDatetime: newest
- projection:
- id: "equirectangular"
- basemapId: "light"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: abn-ong-other
- colormap_name: epa-ghgi-ch4
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- datasetId: epa-ch4emission-yeargrid-v2express
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- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- analysis:
- exclude: true
- metrics:
- - mean
- sourceParams:
- dst_crs: "+proj=cea"
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- source: EPA
- spatialExtent: Contiguous United States
- temporalResolution: Annual
- unit: Mg CH₄/km²/yr
- legend:
- unit:
- label: Mg CH₄/km²/yr
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- min: 0
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- - "#521A13"
- media:
- src: ::file ./media/epa-ch4emission-yeargrid-v2express.thumbnails.other.abandoned.oil.gas.annual.png
- alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Abandoned Oil and Gas Wells (annual)
- - id: 1A-petro-production-other
- stacApiEndpoint: https://earth.gov/ghgcenter/api/stac
- tileApiEndpoint: https://earth.gov/ghgcenter/api/raster
- stacCol: epa-ch4emission-yeargrid-v2express
- name: Other - Petrochemical Production (annual)
- type: raster
- description: Annual methane emission fluxes from Petrochemical Production (inventory Industrial Processes and Product Use category 2B8)
- initialDatetime: newest
- projection:
- id: "equirectangular"
- basemapId: "light"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: petro-production-other
- colormap_name: epa-ghgi-ch4
- rescale:
- - 0
- - 20
- minzoom: 0
- maxzoom: 5
- compare:
- datasetId: epa-ch4emission-yeargrid-v2express
- layerId: 1A-petro-production-other
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- analysis:
- exclude: true
- metrics:
- - mean
- sourceParams:
- dst_crs: "+proj=cea"
- info:
- source: EPA
- spatialExtent: Contiguous United States
- temporalResolution: Annual
- unit: Mg CH₄/km²/yr
- legend:
- unit:
- label: Mg CH₄/km²/yr
- type: gradient
- min: 0
- max: 20
- stops:
- - "#FFFFFF"
- - "#6F4C9B"
- - "#6059A9"
- - "#5568B8"
- - "#4E79C5"
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- - "#4E96BC"
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- - "#DA2222"
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- - "#95211B"
- - "#721E17"
- - "#521A13"
- media:
- src: ::file ./media/epa-ch4emission-yeargrid-v2express.thumbnails.other.petrochemical.production.annual.png
- alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Petrochemical Production (annual)
- - id: 1A-ferroalloy-production-other
- stacApiEndpoint: https://earth.gov/ghgcenter/api/stac
- tileApiEndpoint: https://earth.gov/ghgcenter/api/raster
- stacCol: epa-ch4emission-yeargrid-v2express
- name: Other - Ferroalloy Production (annual)
- type: raster
- description: Annual methane emission fluxes from Ferroalloy Production (inventory Industrial Processes and Product Use category 2C2)
- initialDatetime: newest
- projection:
- id: "equirectangular"
- basemapId: "light"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: ferroalloy-production-other
- colormap_name: epa-ghgi-ch4
- rescale:
- - 0
- - 20
- compare:
- datasetId: epa-ch4emission-yeargrid-v2express
- layerId: 1A-ferroalloy-production-other
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- analysis:
- exclude: true
- metrics:
- - mean
- sourceParams:
- dst_crs: "+proj=cea"
- info:
- source: EPA
- spatialExtent: Contiguous United States
- temporalResolution: Annual
- unit: Mg CH₄/km²/yr
- legend:
- unit:
- label: Mg CH₄/km²/yr
- type: gradient
- min: 0
- max: 20
- stops:
- - "#FFFFFF"
- - "#6F4C9B"
- - "#6059A9"
- - "#5568B8"
- - "#4E79C5"
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- - "#4E96BC"
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- - "#DF4828"
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- - "#B8221E"
- - "#95211B"
- - "#721E17"
- - "#521A13"
- media:
- src: ::file ./media/epa-ch4emission-yeargrid-v2express.thumbnails.other.ferroalloy.production.annual.png
- alt: U.S. Gridded Anthropogenic Methane Emissions Inventory - Other - Ferroalloy Production (annual)
-
----
-
-
-
- **Temporal Extent:** 2012 - 2020
- **Temporal Resolution:** Annual
- **Spatial Extent:** Contiguous United States
- **Spatial Resolution:** 0.1° x 0.1°
- **Data Units:** Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)
- **Data type:** Research (v2 express extension)
-
-
- The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA [Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks) (U.S. GHGI). This dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for manure management. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Megagrams of CH4 per km2 per year (Mg/km²/yr).
-
- ## Source Data Product Citation
- Gridded GHGI Version 2 & Express Extension **(this dataset in US GHG Center)**:
- McDuffie, E. E., Maasakkers, J. D., Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, Daniel, J., Jeong, S., Irving, B., & Weitz, M. (2023). Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI) (v1.0) [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.8367082](https://doi.org/10.5281/zenodo.8367082)
-
- Gridded GHGI Version 1:
- Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A. A., Bowman, K. W., Jeong, S., Fischer, M. L. (2016) A Gridded National Inventory of U.S. Methane Emissions [Data set]. Available at: [https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data)
-
- ## Version History
- The gridded methane GHGI is continually updated to capture ongoing improvements and updates to the U.S. GHG Inventory. The gridded methane GHGI currently includes 2 versions, which reflect sectoral methane emissions that are consistent with different versions of the U.S. GHGI. Versions include:
-
- Current Version(s)
- - Gridded methane GHGI Version 2 - Express Extension (0.1°×0.1° annual emission maps for 2012-2020, consistent with the 2022 U.S. GHGI)
-
- Previous Versions
- - Gridded methane GHGI Version 1 (0.1°×0.1° annual emission maps for 2012, consistent with the 2016 U.S. GHGI)
-
- **Data available on the Data Exploration page correspond to the V2 Express Extension dataset.**
-
- For more information on the current data set versions, see the associated publication: [Massakkers et al., 2023.](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) or visit the [EPA gridded inventory webpage](https://www.epa.gov/ghgemissions/us-gridded-methane-emissions). For more information on the previous version, see the associated publication: [Massakkers et al., 2016.](https://pubs.acs.org/doi/10.1021/acs.est.6b02878)
-
-
-
-
-
- ## Dataset Accuracy
- Uncertainties underlying the development of national methane emission estimates are discussed in each annual U.S. GHGI Report. Additional characterization of resolution-dependent uncertainties are discussed in [Maasakkers et al. (2023)](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138).
-
- ## Disclaimer
- This dataset has been transformed from its original format (NetCDF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly. The manuscript describing the gridded methane GHGI has been peer-reviewed, but is not part of the same annual expert and public review processes as the U.S. EPA National and State-level Inventory.
-
- Users of these datasets are asked to cite the original references [Maasakkers et al. (2023)](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) or [Maasakkers, et al., (2016)](https://pubs.acs.org/doi/10.1021/acs.est.6b02878) in their publications and are encouraged to reach out to the development team with further questions.
-
- ## Scientific Details
- The gridded methane GHGI is developed by spatially allocating national annual methane emissions from individual source categories from the Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI) to a 0.1 deg x 0.1 deg (~10 x 10 km) grid using a series of spatial and temporal proxy datasets at the state, county, and grid levels. Where possible, the proxy data are the same as those used to develop the GHGI so that the gridded emissions can be, as closely as possible, a spatial and temporal representation of those in the national-level U.S. GHGI Report.
-
- The development of the gridded GHGI enables more direct comparisons between the methane emissions reported in the annual U.S. GHGI and those derived from atmospheric methane observations, such as through inverse analyses, with the aim of improving national inventory estimates and better understanding uncertain sources of methane emissions.
-
- Details of the methodological development of this dataset are described in the paper Maasakkers et al., 2023: [https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138)
-
- ## Key Publications
- Maasakkers, J. D., McDuffie, E. E.,, Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, D. J., Jeong, S., Irving, B., & Weitz, M. (2023). A gridded inventory of annual 2012-2018 U.S. anthropogenic methane emissions. Environmental Science & Technology, 57(43), 16276-16288. https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138
-
- ## Other Relevant Publications
- Maasakkers, J., Jacob, D., Sulprizio, M., Turner, A., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A., Bowman, K., Jeong, S., Fischer, M. (2016). Gridded National Inventory of U.S. Methane Emissions. *Environmental Science & Technology*, 50(23), 13123-13133. https://doi.org/10.1021/acs.est.6b02878
-
- ## Learn More
- - Learn more about how this data helps identify trends in U.S. methane emissions in the U.S. Gridded Anthropogenic Greenhouse Gas Emissions Data Insight
- - Check out other GHG data [from the EPA](https://www.epa.gov/ghgemissions)
- - Check out the [data interpretation notes](https://drive.google.com/file/d/1_c6SrKr4z2SNs4fCy3QQMlX92G09Yf6R/view?usp=drive_link) for more information when viewing this dataset in the US GHG Center Exploration environment
-
- ## Acknowledgment
- This dataset was developed in collaboration between researchers at the U.S. EPA, Netherlands Institute for Space Research (SRON), Harvard University, and Lawrence Berkeley National Laboratory.
-
- ## Limitations of use
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
- ## License
- [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0)
-
- ## Data Stewardship
- The EPA gridded emissions for manure management in the VEDA platform are served by the U.S. Greenhouse Gas Center data catalog. For information on data stewardship within the U.S. Greenhouse Gas Center please refer to the documentation below.
- - [Data Workflow](https://github.com/US-GHG-Center/ghgc-docs/blob/main/data_workflow/media/epa-ch4emission-grid-v2express_Data_Flow.png)
- - [Data Transformation Code](https://us-ghg-center.github.io/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express.html)
- - [US GHG Center Data Intake Processing and Verification Report](https://us-ghg-center.github.io/ghgc-docs/processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing%20and%20Verification%20Report.html)
-
-
-
diff --git a/datasets/ercot-modis-lst.data.mdx b/datasets/ercot-modis-lst.data.mdx
deleted file mode 100644
index 62437ca360..0000000000
--- a/datasets/ercot-modis-lst.data.mdx
+++ /dev/null
@@ -1,143 +0,0 @@
----
-id: ercot-heatwave
-isHidden: true
-name: MODIS Land Surface Temperatures
-description: MODIS LST dataset for the 2023 heatwave in Texas on June 23, 2023.
-media:
- src: ::file ./media/urban-heat.jpg
- alt: Sunset over Tokyo
- author:
- name: Arto Marttinen
- url: https://unsplash.com/photos/6xh7H5tWj9c
-taxonomy:
- - name: Topics
- values:
- - Air Quality
- - Sustainable Energy
- - name: Subtopics
- values:
- - Heat
- - Surface Meteorology
- - name: Source
- values:
- - MODIS
-
-infoDescription: |
- ::markdown
- Terra MODIS has been instrumental in capturing LST data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST values.
-layers:
- - id: ERCOT-MODIS
- stacCol: ERCOT-MODIS
- name: Texas LST Day During the 2023 Heatwave
- type: raster
- description: "The Land Surface Temperature (LST) data is MODIS-derived daily data, measured at 1 km spatial resolution."
- zoomExtent:
- - 2
- - 16
- sourceParams:
- colormap_name: surface_temperature
- nodata: 999
- rescale:
- - -30
- - 110
- legend:
- type: gradient
- unit:
- label: Fahrenheit
- min: -30
- max: 110
- stops:
- - "#3CCBCE"
- - "#C5F8FF"
- - "#FEC5FF"
- - "#E079FB"
- - "#094FC9"
- - "#009FFF"
- - "#44E2FF"
- - "#147F4F"
- - "#79B32C"
- - "#FDFE00"
- - "#FF8700"
- - "#FF0F00"
- - "#9D0F2B"
- - "#4D0000"
- - "#BA2E6D"
- info:
- source: NASA
- spatialExtent: Texas
- temporalResolution: Daily
- unit: Fahrenheit
-
----
-
-
- ## Dataset Details
- ##### Land Surface Temperature
- - **Temporal Extent:** 2000-Present
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Texas
- - **Spatial Resolution:** 1 km
- - **Data Units:** Fahrenheit (F)
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
-
- ## Overview
- During the extreme heatwaves from 2022 to 2024, satellite data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) helped reveal how hot the ground became in Texas. MODIS measures land surface temperature (LST), which shows how hot the surface of the Earth is—such as roads, rooftops, and soil—rather than the air temperature we normally feel. These measurements are taken twice a day from the Terra and Aqua satellites and have a resolution of about 1 kilometer, giving a detailed view of surface heating.
-
- Looking at this LST data during each heatwave shows how much hotter the surface got compared to normal. It also helps identify where the heat was most intense and how long it lasted. This is especially useful in areas like West Texas, where ground weather stations are spread out. MODIS makes it possible to track and compare heatwave patterns over time and across different parts of the region, providing a clearer picture of how severe these heat events really were.
-
-
-
-
-
- ## Data Acquisition
-
- NASA’s Terra satellite has played a key role in collecting land surface temperature (LST) data. Orbiting Earth since 1999, Terra uses its MODIS instrument to scan the planet in several wavelengths of light, helping researchers measure how hot the ground gets across different regions. By comparing LST data from the 2000s and 2010s, scientists can see how surface temperatures have changed over time.
-
- For example, in Texas, MODIS data makes it possible to detect long-term warming trends and analyze how land use or environmental conditions may be contributing to hotter surface temperatures. These comparisons show clear increases in heat at the surface during recent years, especially when viewed alongside short-term events like the 2022–2024 heatwaves.
-
- Users can access MODIS data for anywhere across the globe [here](https://modis.gsfc.nasa.gov/data/), or click 'Explore Data' at the top of this page for a quick examination of the specific data used in this study.
-
-
-
-
-
- ## Importance of Heat Stress Datasets
- MODIS LST data is a powerful tool for tracking surface temperatures over time. It helps researchers understand how heat builds up and changes across different landscapes. During major heat events, like the ones from 2022 to 2024, this satellite data allows for clear comparisons of how severe the surface heating is and where it is most intense. With this information, scientists can better monitor extreme heat conditions and study how they develop across regions like Texas.
-
-
-
-
-
- ## Disclaimer
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
- ## Key Publications
-
- Wan, Z. (2014). New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. *Remote Sensing of the Environment, 140*, 36-45. https://doi.org/10.1016/j.rse.2013.08.027
-
-
-
-
-
-
- ## Data Stories Using This Dataset
- **[Preparing ERCOT for Weather Extremes in Texas](https://www.earthdata.nasa.gov/dashboard/stories/ercot-sustainability)**
-
-
-
-
-
- ## License
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/fb_population.ej.data.mdx b/datasets/fb_population.ej.data.mdx
deleted file mode 100644
index 3d217e1b36..0000000000
--- a/datasets/fb_population.ej.data.mdx
+++ /dev/null
@@ -1,148 +0,0 @@
----
-id: facebook_population_density
-name: 'Facebook Population Density'
-description: 'Facebook high-resolution population density with a 30m resolution'
-media:
- src: ::file ./media/fb-population--dataset-cover.png
- alt: 2015 high resolution population density for Paris.
- author:
- name: NASA
- url:
-taxonomy:
- - name: Topics
- values:
- - Socioeconomic
- - name: Subtopics
- values:
- - Urban
- - name: Source
- values:
- - Meta
-infoDescription: |
- ::markdown
- In partnership with the Center for International Earth Science Information Network (CIESIN) at Columbia University, Meta [formerly known as Facebook] used census data and computer vision techniques (Convolutional Neural Networks) to identify buildings from publicly accessible mapping services to create population density datasets. These high-resolution maps estimate the number of individuals living within 30-meter grid tiles on a global scale. The population estimates are based on data from the Gridded Population of the World (GPWv4) data collection.
-layers:
- - id: facebook_population_density
- stacCol: facebook_population_density
- name: 'Facebook Population Density'
- type: raster
- description: 'Facebook high-resolution population density with a 30m² resolution'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: ylorrd
- rescale:
- - 0
- - 69
- info:
- source: Meta
- spatialExtent: Global
- temporalResolution: Annual
- unit: Units of People per Square Meter
- legend:
- type: gradient
- min: "0"
- max: "69"
- stops:
- - "#ffffcc"
- - "#fee187"
- - "#feab49"
- - "#fc5b2e"
- - "#d41020"
- - "#800026"
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2015
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 30 meters
- - **Data Units:** Number of people (density)
- - **Data Type:** Research
-
-
-
-
- Population density in units of people per square meter for New York, NY, USA for 2015
-
-
-
-
-
-
-
- ## About
-
- In coordination with the Center for International Earth Science Information Network (CIESIN) at Columbia University, Meta [formerly known as Facebook] used census data and computer vision techniques (Convolutional Neural Networks) to identify buildings from publicly accessible mapping services to create population density datasets. These high-resolution maps estimate the number of individuals living within 30-meter grid tiles on a global scale. The population estimates are based on data from the Gridded Population of the World (GPWv4) data collection.
-
- **NASA did not participate in the development of these data and makes no claims regarding their accuracy, reliability, or suitability for any particular use.**
-
-
-
-
-
-
- ## Interpreting the data
- Population density is provided in units of people per square meter, at the near global scale and is available for the year 2015. The data is gridded at 30m resolution and is only available in locations where buildings are present (i.e. there will be no data over large parks or industrial areas). Higher population density regions (shaded in red) typically correspond to urban centers and lower population (in yellow) to rural and suburban locations.
-
-
-
-
-
-
- ## Scientific Details
-
- The 30-meter resolution population density dataset uses machine learning techniques to produce global population maps using building footprints and census population data from CIESIN. The high resolution population data can be used to support the applications community when assessing the impacts of natural disasters or assessing risk at near global coverage.
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
- ## Credits
- 1. https://dataforgood.facebook.com/dfg/docs/methodology-high-resolution-population-density-maps
-
- 2. https://dataforgood.facebook.com/dfg/tools/high-resolution-population-density-maps
-
- 3. T. G. Tiecke et al., ‘Mapping the world population one building at a time’, CoRR, https://arxiv.org/abs/1712.05839, 2017.
-
-
diff --git a/datasets/fire.data.mdx b/datasets/fire.data.mdx
deleted file mode 100644
index 0af875aee2..0000000000
--- a/datasets/fire.data.mdx
+++ /dev/null
@@ -1,99 +0,0 @@
----
-id: fire
-name: Fire Perimeters
-description: Fire perimeters generated from VIIRS sensor observations.
-usage:
- - url: https://nasa-impact.github.io/veda-docs/notebooks/tutorials/mapping-fires.html
- label: View example notebook
- title: VEDA documentation for visualization and download
-media:
- src: ::file ./media/fire--dataset-cover.jpg
- alt: Forest burning at night
- author:
- name: Matt Howard
- url: https://unsplash.com/photos/eAKDzK4lo4o
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- Fire perimeter data is generated by the FEDs algorithm. The FEDs algorithm tracks fire movement and severity by ingesting observations from the VIIRS thermal sensors on the Suomi NPP and NOAA-20 satellites. This algorithm uses raw VIIRS observations to generate a polygon of the fire, locations of the active fire line, and estimates of fire mean Fire Radiative Power (FRP). The VIIRS sensors overpass at ~1:30 AM and PM local time, and provide estimates of fire evolution ~ every 12 hours. The data produced by this algorithm describe where fires are in space and how fires evolve through time. This CONUS-wide implementation of the FEDs algorithm is based on [Chen et al 2020’s algorithm for California.](https://www.nature.com/articles/s41597-022-01343-0)
-layers:
- - id: eis_fire_perimeter
- stacCol: eis_fire_perimeter
- name: Fire Perimeter
- type: vector
- description: eis_fire_perimeter
- zoomExtent:
- - 5
- - 20
- info:
- source: NASA
- spatialExtent: Contiguous United States
- temporalResolution: Daily
- unit: N/A
----
-
-
-
- ## FEDs Fire Perimeters
-
- Fire perimeter data is generated by the FEDs algorithm. The FEDs algorithm tracks fire movement and severity by ingesting observations from the VIIRS thermal sensors on the Suomi NPP and NOAA-20 satellites. This algorithm uses raw VIIRS observations to generate a polygon of the fire, locations of the active fire line, and estimates of fire mean Fire Radiative Power (FRP). The VIIRS sensors overpass at ~1:30 AM and PM local time, and provide estimates of fire evolution ~ every 12 hours. The data produced by this algorithm describe where fires are in space and how fires evolve through time. This CONUS-wide implementation of the FEDs algorithm is based on [Chen et al 2020’s algorithm for California.](https://www.nature.com/articles/s41597-022-01343-0)
-
-
-
-
-
-
-
- ## Scientific Application Using FEDS Fire Perimeters
- FEDS Fire Perimeters offer insight into pre-fire risk, fire behaviour, and post-fire effects. The Earth Information System - Fire team is using FEDs perimeters to understand the full lifecycle of a fire.
-
-
-
-
-
-
- ## Datasets generated using FEDs Fire perimeters
-
- * Caldor Fire Behavior and Burn Severity
- * Thomas Fire Analysis
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/gedi.data.mdx b/datasets/gedi.data.mdx
deleted file mode 100644
index ad965bad74..0000000000
--- a/datasets/gedi.data.mdx
+++ /dev/null
@@ -1,105 +0,0 @@
----
-id: GEDI_ISS_L3_Canopy_Height_Mean_RH100_201904-202303
-name: GEDI Canopy Height AGL
-featured: true
-description: The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity.
-media:
- src: ::file ./media/gedi-background.jpg
- alt: GEDI Canopy Height over the Colorado Rockies.
- author:
- name: Andrew Blackford
-infoDescription: |
- ::markdown
- - Temporal Extent: 2019 Apr 18
- - Temporal Resolution: N/A
-layers:
- - id: GEDI_ISS_L3_Canopy_Height_Mean_RH100_201904-202303
- stacApiEndpoint: https://openveda.cloud/api/stac
- stacCol: GEDI_ISS_L3_Canopy_Height_Mean_RH100_201904-202303
- name: GEDI Canopy Height AGL
- type: wmts
- description: The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.
- sourceParams:
- layers: 1
- version: 1.3.0
- crs: EPSG:3857
- styles:
- zoomExtent:
- - 0
- - 5
- analysis:
- exclude: true
- legend:
- type: gradient
- min: 0
- max: 1
- stops:
- - rgb(215, 210, 210)
- - rgb(144, 168, 63)
- - rgb(87, 133, 41)
- - rgb(46, 96, 28)
- - rgb(10, 26, 8)
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2019
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 1 km
- - **Data Type:** Research
-
-
-
-
- Example of GEDI Canopy Height showing the vegetation destruction caused by the 2018 Camp Fire in California.
-
-
-
-
-
-
-
-
-## About GEDI
-
-The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.
-
-The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.
-
-The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.
-
-The GEDI02_B data product contains 96 layers for each of the eight-beam ground transects (or laser footprints located on the land surface). Datasets provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the layers can be found in the GEDI Level 2B Data Dictionary.
-
-##### Known Issues
-
-* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
-* Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this [document](https://lpdaac.usgs.gov/documents/2236/GEDI_CORRECTED_RGT_FILENAMES.pptx) for the correct RGT numbers.
-* Known Issues: Section 8 of the User Guide provides additional information on known issues.
-
-##### Improvements/Changes from Previous Versions
-
-* Metadata has been updated to include spatial coordinates.
-* Granule size has been reduced from one full ISS orbit (~1.19 GB) to four segments per orbit (~0.30 GB).
-* Filename has been updated to include segment number and version number.
-* Improved geolocation for an orbital segment.
-* Added elevation from the SRTM digital elevation model for comparison.
-* Modified the method to predict an optimum algorithm setting group per laser shot.
-* Added additional land cover datasets related to phenology, urban infrastructure, and water persistence.
-* Added selected_mode_flag dataset to root beam group using selected algorithm.
-* Removed shots when the laser is not firing.
-* Modified file name to include segment number and dataset version.
-
-
-
-
diff --git a/datasets/geoglam.data.mdx b/datasets/geoglam.data.mdx
deleted file mode 100644
index aeba48e25a..0000000000
--- a/datasets/geoglam.data.mdx
+++ /dev/null
@@ -1,164 +0,0 @@
----
-id: geoglam
-name: "GEOGLAM Crop Conditions"
-description: Open, timely, science-driven information on crop conditions
-media:
- src: ::file ./media/geoglam--dataset-cover.jpg
- alt: Bird's eye view of fields
- author:
- name: Jean Wimmerlin
- url: https://unsplash.com/photos/RUj5b4YXaHE
-taxonomy:
- - name: Topics
- values:
- - Agriculture
- - name: Subtopics
- values:
- - Land Use
- - name: Source
- values:
- - GEOGLAM
-infoDescription: |
- ::markdown
- The Group on Earth Observation's Global Agricultural Monitoring Initiative (GEOGLAM) Global Crop Monitor uses remote sensing data like global precipitation and soil moisture measurements to help reduce uncertainty, promote market transparency, and provide early warning for crop failures through multi-agency collaboration.
-layers:
- - id: geoglam
- stacCol: geoglam
- name: GEOGLAM Crop Conditions
- type: raster
- description: Combined crop conditions across both the Crop Monitor for AMIS and Crop Monitor for Early Warning
- zoomExtent:
- - 0
- - 16
- sourceParams:
- colormap: '{"1": [120, 120, 120], "2": [130, 65, 0], "3": [66, 207, 56], "4": [245, 239, 0], "5": [241, 89, 32], "6": [168, 0, 0], "7": [0, 143, 201]}'
- bidx: 1
- unscale: false
- resampling: nearest
- max_size: 1024
- return_mask: true
- initialDatetime: newest
- legend:
- type: categorical
- stops:
- - color: "#3A8DC6"
- label: "Exceptional"
- - color: "#62D246"
- label: "Favourable"
- - color: "#FFFF00"
- label: "Watch"
- - color: "#EC5830"
- label: "Poor"
- - color: "#891911"
- label: "Failure"
- - color: "#787878"
- label: "Out of season"
- - color: "#804115"
- label: "No data"
- info:
- source: GEOGLAM
- spatialExtent: Global
- temporalResolution: Monthly
- unit: Categorical
----
-
-
-
- ## Examples of COVID-19 Impact on Global Food Supplies
-
- Measures to slow the spread of COVID-19 affected the food supply chain in many ways, including the availability of inputs, labor, transport, and cross-border trade. The Group on Earth Observation's Global Agricultural Monitoring Initiative (GEOGLAM) Global Crop Monitor uses remote sensing data like global precipitation and soil moisture measurements to help reduce uncertainty, promote market transparency, and provide early warning for crop failures through multi-agency collaboration. During the pandemic, this tool - developed in conjunction with NASA's food and agriculture program (NASA Harvest), ESA (European Space Agency) and JAXA, Japan Aerospace Exploration Agency - is increasingly used in lieu of on-the-ground validation of crop conditions.
-
- Data from the GEOGLAM Crop Monitor inform two different agricultural tools that have helped lessen global concerns over food security during the novel coronavirus pandemic: the Agricultural Market Information System (AMIS) and the Crop Monitor for Early Warning (CM4EW). AMIS provides agricultural information based on remote sensing observations for the major producing nations of four primary crops - wheat, maize, rice, and soybeans. CM4EW provides agricultural data for countries at higher risk of food insecurity.
-
-
-
-
- Global crop conditions as of July 28, 2020. Blue and green colors indicate exceptional and favorable crop conditions, while red and burgundy indicate poor crop conditions and crop failure. Yellow areas are currently under watch for potential negative impacts on crops.
-
-
-
-
-
-
- ### Major Producing and Exporting Countries
-
- Current estimates from GEOGLAM Crop Monitor data indicate the global food supply is adequate. While many countries experienced lockdowns and travel bans as coronavirus spread, most farmers were able to continue operations due to the rural nature of most farm communities and the relatively less labor-intensive cultivation techniques associated with key crops.
-
- However, the spread of the coronavirus did have an impact on the ability of governments and agricultural organizations to perform in-person field surveys of sowing, crop progress, and harvesting. This reinforced the need for strong remote sensing capabilities. Satellite-based information from AMIS helped confirm that global food production during the early parts of the pandemic was secure, leading to the resumption of normal trade flows after some large producer and export countries issued temporary trade restrictions.
-
- "Assessing the global supply situation and being able to predict unexpected shortfalls is the single most important task to guarantee global food security,” explained Abdolreza Abbassian, Secretary of AMIS and a U.N. Food and Agriculture Organization senior economist. “However, such assessments must be evidence-based and credible, and this is where reliance on timely information from remote sensing plays a fundamental role.”
-
-
-
-
-
-
-
- Maize 1 conditions across East Africa as of July 28, 2020. Data inputs from a wide variety of Earth observation satellites combined with field statistics are used to generate meaningful crop condition reports.
-
-
-
- ## COVID-19 Impacts in East Africa
-
- During the 2020 growing season in East Africa, agricultural production faced the triple threat of desert locusts, deadly flooding and COVID-19 impacts.
-
- The overall impact of the pandemic on agricultural production of major grains within the region was generally limited, and supplies of staple foods were reported to be sufficient. However, production was disrupted in some areas through COVID-19 restrictions, causing agricultural labor supply shortages and disrupting supply chains, limiting farmers' access to seeds, fertilizers, and other inputs. This resulted in reported declines in planted area and yields in Ethiopia, Somalia and elsewhere across the region, and it will be critical to continue to monitor the situation and to provide timely and evidence driven crop assessments.
-
-
-
-
-
- ## COVID-19 Impacts in Southeast Asia
-
- In Southern Asia, the GEOGLAM crop condition assessments are coordinated by the Asian Rice Crop Estimation & Monitoring (Asia-RiCE) initiative led by the Japan Aerospace Exploration Agency (JAXA) with inputs from the region's national ministries of agriculture. COVID-19 impacted the region by restricting the ability of governments to do field surveys, particularly during the height of the outbreak.
-
- Currently, on the northern side of Southeast Asia, the dry-season rice has come to a close and the wet-season rice (main producing season) is underway. The dry season, which ended in May-June, was affected by persistent dry conditions that drove down yields and planted area in Myanmar, Thailand, and Laos. The wet-season rice began under generally favorable conditions, with ample rainfall in most areas except for southern Vietnam. Additionally, there has been some flooding in Bangladesh.
-
- In the southern side (Indonesia), during the wet-season, reduced rainfall delayed the sowing of the rice and eventually resulted in less total sown area and a reduction in yields. As a consequence of the delay in the wet-season, the sowing of dry-season rice was delayed. Despite the delay, good rainfall continued into the traditional dry season.
-
-
-
-
- Rice conditions across Southeast Asia as of July 28, 2020. Remotely sensed data is useful to visualize crop conditions and regions susceptible to potential crop failure
-
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
diff --git a/datasets/global-reanalysis-da.data.mdx b/datasets/global-reanalysis-da.data.mdx
deleted file mode 100644
index 2d958009da..0000000000
--- a/datasets/global-reanalysis-da.data.mdx
+++ /dev/null
@@ -1,457 +0,0 @@
----
-id: global-reanalysis-da
-name: 'A Global Reanalysis for Water, Energy, and Carbon Cycle Variables'
-description: "A high-resolution (10 km) global data product that integrates NASA’s state-of-the-art model with satellite observations"
-media:
- src: ::file ./media/global_tws_blackbg_v2.png
- alt: One day of terrestrial water storage from LIS outputs.
- author:
- name: NASA LIS
- url:
-pubDate: 2023-03-01
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- The reanalysis product is created using the [NASA Land Information System](https://lis.gsfc.nasa.gov/) modeling framework to merge land surface model simulations with observations from satellites through data assimilation. The team uses the Noah-MP land surface model and assimilates soil moisture from the European Space Agency’s Climate Change Initiative Program (ESA CCI), leaf area index from the Moderate Resolution Imaging Spectroradiometer (MODIS), and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment and the follow-on missions (GRACE/GRACE-FO).
-
-layers:
- - id: lis-global-da-evap
- stacCol: lis-global-da-evap
- name: 'Evapotranspiration'
- type: raster
- description: 'Gridded total evapotranspiration (in kg m-2 s-1) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: viridis
- rescale:
- - 0
- - 0.0001
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-evap
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: kg m-2 s-1
- type: gradient
- label: Evapotranspiration [kg m-2 s-1]
- min: "0"
- max: "0.0001"
- stops:
- - '#440154'
- - '#3b528b'
- - '#21918c'
- - '#5ec962'
- - '#fde725'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: kg m-2 s-1
- - id: lis-global-da-gpp
- stacCol: lis-global-da-gpp
- name: 'Gross Primary Productivity'
- type: raster
- description: 'Gridded gross primary productivity (in g m-2 s-1) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: viridis
- rescale:
- - 0
- - 0.0001
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-gpp
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: g m-2 s-1
- type: gradient
- label: Gross primary productivity [g m-2 s-1]
- min: "0"
- max: "0.0001"
- stops:
- - '#440154'
- - '#3b528b'
- - '#21918c'
- - '#5ec962'
- - '#fde725'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: g m-2 s-1
-
- - id: lis-global-da-gws
- stacCol: lis-global-da-gws
- name: 'Groundwater Storage'
- type: raster
- description: 'Gridded groundwater storage (in mm) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: viridis
- rescale:
- - 4500
- - 5000
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-gws
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: mm
- type: gradient
- label: Groundwater storage [mm]
- min: "4500"
- max: "5000"
- stops:
- - '#440154'
- - '#3b528b'
- - '#21918c'
- - '#5ec962'
- - '#fde725'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: mm
-
- - id: lis-global-da-swe
- stacCol: lis-global-da-swe
- name: 'Snow Water Equivalent'
- type: raster
- description: 'Gridded snow water equivalent (in kg m-2) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: blues
- rescale:
- - 0
- - 500
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-swe
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: mm
- type: gradient
- label: Snow Water Equivalent [mm]
- min: "0"
- max: "500"
- stops:
- - "#F7FBFF"
- - "#D0E1F2"
- - "#94C4DF"
- - "#4A98C9"
- - "#2164AB"
- - "#0E316B"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: mm
-
- - id: lis-global-da-streamflow
- stacCol: lis-global-da-streamflow
- name: 'Streamflow'
- type: raster
- description: 'Routed streamflow (in m3 s-1) from 10km global LIS+HyMAP with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: viridis
- rescale:
- - 0
- - 2500
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-streamflow
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: m3 s-1
- type: gradient
- label: Streamflow [m3 s-1]
- min: "0"
- max: "2500"
- stops:
- - '#440154'
- - '#3b528b'
- - '#21918c'
- - '#5ec962'
- - '#fde725'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: m3 s-1
-
- - id: lis-global-da-qs
- stacCol: lis-global-da-qs
- name: 'Surface runoff'
- type: raster
- description: 'Gridded surface runoff (in kg m-2 s-1) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: viridis
- rescale:
- - 0
- - 0.0001
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-qs
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: kg m-2 s-1
- type: gradient
- label: Surface runoff [kg m-2 s-1]
- min: "0"
- max: "0.00001"
- stops:
- - '#440154'
- - '#3b528b'
- - '#21918c'
- - '#5ec962'
- - '#fde725'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: kg m-2 s-1
-
- - id: lis-global-da-qsb
- stacCol: lis-global-da-qsb
- name: 'Subsurface runoff'
- type: raster
- description: 'Gridded subsurface runoff (in kg m-2 s-1) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: viridis
- rescale:
- - 0
- - 0.0001
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-qsb
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: kg m-2 s-1
- type: gradient
- label: Subsurface runoff [kg m-2 s-1]
- min: "0"
- max: "0.0001"
- stops:
- - '#440154'
- - '#3b528b'
- - '#21918c'
- - '#5ec962'
- - '#fde725'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: kg m-2 s-1
-
- - id: lis-global-da-tws
- stacCol: lis-global-da-tws
- name: 'Terrestrial Water Storage'
- type: raster
- description: 'Gridded terrestrial water storage (in mm) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: viridis
- rescale:
- - 5000
- - 5800
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-tws
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: mm
- type: gradient
- label: Terrestrial Water Storage [mm]
- min: "5000"
- max: "5800"
- stops:
- - '#440154'
- - '#3b528b'
- - '#21918c'
- - '#5ec962'
- - '#fde725'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: mm
-
- - id: lis-global-da-totalprecip
- stacCol: lis-global-da-totalprecip
- name: 'Total Precipitation'
- type: raster
- description: 'Gridded total precipitation (in kg m-2 s-1) from 10km global LIS with assimilation'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: blues
- rescale:
- - 0
- - 0.0001
- compare:
- datasetId: global-reanalysis-da
- layerId: lis-global-da-totalprecip
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: kg m-2 s-1
- type: gradient
- label: Total precipitation [kg m-2 s-1]
- min: "0"
- max: "0.00001"
- stops:
- - "#F7FBFF"
- - "#D0E1F2"
- - "#94C4DF"
- - "#4A98C9"
- - "#2164AB"
- - "#0E316B"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: kg m-2 s-1
-
----
-
-
-## Introduction
-🚧 This page presents work in progress and not a peer-reviewed data product! 🚧
-
-Realistic estimates of water, energy, and carbon cycle variables are necessary for accurate understanding of earth system processes. Land surface models simulate processes at the Earth’s surface and can provide spatiotemporal estimates of a whole suite of variables like precipitation, soil moisture, and evapotranspiration. However, models often have biases that cause high uncertainties in important water budget variables. Therefore, tools like data assimilation, where observations are used to constrain model simulations, are often used.
-
-This dataset is a new global reanalysis that includes variables – such as terrestrial water storage, snow water equivalent, and gross primary productivity – to help quantify the water and energy budget. At the present time, the model reanalysis output is available at 10 km spatial resolution and a daily temporal resolution from January 1, 2003 through December 31, 2021. Details on the model setup are provided below, as well as links to how the reanalysis output can be used to address key science questions.
-
-Authors: Melissa Wrzesien, Wanshu Nie, Sujay Kumar, Kim Locke
-
-
-
-
-
-
-## Modeling Setup
-The reanalysis product is created using the [NASA Land Information System](https://lis.gsfc.nasa.gov/) modeling framework to merge land surface model simulations with observations from satellites through data assimilation. The team uses the Noah-MP land surface model and assimilates soil moisture from the European Space Agency’s Climate Change Initiative Program (ESA CCI), leaf area index from the Moderate Resolution Imaging Spectroradiometer (MODIS), and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment and the follow-on missions (GRACE/GRACE-FO).
-
-The output variables available on VEDA include evapotranspiration (ET), gross primary productivity (GPP), groundwater storage (GWS), snow water equivalent (SWE), streamflow, surface runoff, subsurface runoff, terrestrial water storage (TWS), and total precipitation. See the [VEDA Analysis tool](https://www.earthdata.nasa.gov/dashboard/eis/analysis) to make interactive plots of the variables over a user-specified domain and time period.
-
-
-
-
-
-
-## Explore the Data
-The global reanalysis is a large dataset with nearly two decades of daily output. Here we show a comparison of two dates for a single variable. We encourage users to Explore the Data to look at different dates and to compare variables.
-
-An example of how trends calculated from the global reanalysis model output can be used to understand changes in TWS, GPP, and ET, can be seen in the corresponding data story.
-
-
-
-
- Terrestrial water storage over North America compared on June 1, 2005 (left) and June 1, 2020 (right), captured by LIS assimilation.
-
-
-
-
-
-
-### Explore the modeling framework:
-* [Land Information System](https://lis.gsfc.nasa.gov/)
-### Explore the remote sensing datasets:
- * [GRACE-FO](https://gracefo.jpl.nasa.gov/data/grace-fo-data/)
- * [ESA CCI](https://esa-soilmoisture-cci.org/)
- * [MODIS](https://modis.gsfc.nasa.gov/)
-
-
diff --git a/datasets/hd-nighttime-lights.data.mdx b/datasets/hd-nighttime-lights.data.mdx
deleted file mode 100644
index 5cee239f49..0000000000
--- a/datasets/hd-nighttime-lights.data.mdx
+++ /dev/null
@@ -1,249 +0,0 @@
----
-id: nighttime-lights
-name: 'NASA High-Definition Black Marble Night Lights'
-description: 'During the COVID-19 pandemic, researchers are using night light observations to track variations in energy use, migration, and transportation in response to social distancing and lockdown measures.'
-media:
- src: ::file ./media/nighttime-lights--dataset-cover.jpg
- alt: Satellite image of Earth at night.
- author:
- name: NASA Earth Observatory
- url: https://earthobservatory.nasa.gov/images/90008/night-light-maps-open-up-new-applications
-taxonomy:
- - name: Topics
- values:
- - COVID 19
- - name: Source
- values:
- - Black Marble
-
-infoDescription: |
- ::markdown
- Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis.
-layers:
- - id: ercot-houston-nightlights-freeze
- stacCol: ercot-houston-nightlights-freeze
- name: Nightlights For 2021 Texas Deep Freeze
- type: raster
- description: 'Nightlights data are collected by the Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite.'
- zoomExtent:
- - 8
- - 16
- sourceParams:
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- compare:
- datasetId: nighttime-lights
- layerId: ercot-houston-nightlights-freeze
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- unit:
- label:W cm-2 sr-1
- min: 0
- max: 255
- stops:
- - '#08041d'
- - '#1f0a46'
- - '#52076c'
- - '#f57c16'
- - '#f7cf39'
- info:
- source: NOAA & NASA
- spatialExtent: Regional
- temporalResolution: N/A
- unit: Artificial Light Emissions (W cm^-2 sr^-1)
-
- - id: nightlights-hd-monthly
- stacCol: nightlights-hd-monthly
- name: Nightlights Monthly
- type: raster
- description: 'Nightlights data are collected by the Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite.'
- zoomExtent:
- - 8
- - 16
- sourceParams:
- bidx: 1
- colormap_name: inferno
- rescale:
- - 0
- - 255
- compare:
- datasetId: nighttime-lights
- layerId: nightlights-hd-monthly
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- legend:
- type: gradient
- min: Less
- max: More
- stops:
- - '#08041d'
- - '#1f0a46'
- - '#52076c'
- - '#f57c16'
- - '#f7cf39'
- info:
- source: NOAA & NASA
- spatialExtent: Global
- temporalResolution: Monthly
- unit: N/A
-
- - id: delta-disasters-hd-blackmarble-nightlights
- stacCol: delta-disasters-hd-blackmarble-nightlights
- name: High Definition Black Marble Nightlights (2023 Rolling Fork Tornado)
- type: raster
- description: 'The High Definition Nightlights dataset is a derived product for measuring light emissions for a given location. Darker colors indicate lower light emissions while lighter colors indicate high light emissions.'
- zoomExtent:
- - 8
- - 16
- sourceParams:
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- compare:
- datasetId: nighttime-lights
- layerId: delta-disasters-hd-blackmarble-nightlights
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- unit:
- label:W cm-2 sr-1
- min: 0
- max: 255
- stops:
- - '#08041d'
- - '#1f0a46'
- - '#52076c'
- - '#f57c16'
- - '#f7cf39'
- info:
- source: NOAA & NASA
- spatialExtent: Regional
- temporalResolution: N/A
- unit: Artificial Light Emissions (W cm^-2 sr^-1)
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 2019 - March 2023
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** Select sites
- - **Spatial Resolution:** 500 meters
- - **Data Units:** W cm^-2 sr^-1
- - **Data Type:** Research
-
-
-
-
- Lighting changes in Jianghan District, a commercial area of Wuhan, and nearby residential areas.
-
-
-
-
-
-
- ## About
- Images of Earth at night give us an extraordinary view of human activity over time. The nighttime environment illuminates Earth features, including city infrastructure, lightning flashes, fishing boats navigating open water, gas flares, aurora, and natural hazards, such as lava flowing from an active volcano. Paired with the moonlight, researchers can also spot snow and ice, as well as other reflective surfaces that allow nighttime land and ocean analysis.
-
- During the COVID-19 pandemic, researchers are using night light observations to track variations in energy use, migration, and transportation in response to social distancing and lockdown measures.
-
-
-
-
-
- ## Scientific research
- Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis.
-
- [New research](https://science.nasa.gov/earth-science/rrnes-awards) funded by NASA’s Rapid Response and Novel Research in the Earth Sciences (RRNES) program seeks to discover what nightlights can tell us about the impacts of novel coronavirus-related shutdowns.
-
-
-
-
-
- ## Interpreting the data
- Each spotlight city has a slider for turning night lights on and off. The darker purple indicates fewer night lights, while the lighter yellow indicates more. By comparing regions before and after guidelines to shelter-in-place began, researchers are able to visualize the extent to which social distancing measures affected various economic activities based on whether light pollution increased or decreased, which highways were shut down, and which cities stayed the same.
-
- The products featured are 500-meter (VNP46) and 30-meter Black Marble High Definition (HD) nighttime lights. Black Marble HD downscales radiances from the 500-meter product to street level using optical imagery from Landsat 8, a NASA and U.S. Geological Survey (USGS) satellite, along with OpenStreetMap ancillary layers. This helps visualize neighborhoods and commercial centers that have less activity – or closures – due to social distancing restrictions.
-
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- - **Air Quality and COVID-19**
-
-
-
-
-
-
- ## Credits
- Black Marble data courtesy of [Universities Space Research Association (USRA) Earth from Space Institute (EfSI)](https://www.usra.edu/efsi-our-mission) and NASA Goddard Space Flight Center’s [Terrestrial Information Systems Laboratory](https://science.gsfc.nasa.gov/earth/terrestrialinfo/) using VIIRS day-night band data from the Suomi National Polar-orbiting Partnership and Landsat-8 Operational Land Imager (OLI) data from the U.S. Geological Survey.
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional resources
- #### NASA Features
-
- * [Nighttime Images Capture Change In China](https://earthobservatory.nasa.gov/images/146481/nighttime-images-capture-change-in-china)
-
- #### Explore the data
-
- * [Nighttime Images Show Changes In Human Activity](https://earthdata.nasa.gov/learn/articles/feature-articles/nighttime-images-wuhan)
-
- #### Explore the Missions
-
- * [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/)
- * [Suomi National Polar-orbiting Partnership (Suomi NPP)](https://www.nasa.gov/mission_pages/NPP/main/index.html)
-
-
diff --git a/datasets/hls-events.ej.data.mdx b/datasets/hls-events.ej.data.mdx
deleted file mode 100644
index feef104e67..0000000000
--- a/datasets/hls-events.ej.data.mdx
+++ /dev/null
@@ -1,312 +0,0 @@
----
-id: hls_events
-name: 'Harmonized Landsat and Sentinel-2 (Selected Events)'
-description: '30-meter resolution harmonized Landsat 8/9 and Sentinel-2A/B data products'
-usage:
- - url: "https://nasa-impact.github.io/veda-docs/notebooks/quickstarts/hls-visualization.html"
- label: View example notebook
- title: 'Multi-Band Visualization Preview for Harmonized Landsat Sentinel-2 (HLS)'
- - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https://github.com/NASA-IMPACT/veda-docs&urlpath=lab/tree/veda-docs/notebooks/quickstarts/hls-visualization.ipynb&branch=main"
- label: Run example notebook
- title: 'Multi-Band Visualization Preview for Harmonized Landsat Sentinel-2 (HLS)'
-media:
- src: ::file ./media/hls-events-ej--dataset-cover.png
- alt: 2017 harmonized Landsat 8 shortwave infrared (SWIR) false color composite image that provides enhanced contrast to detect flood extent.
- author:
- name: NASA
- url: https://nasa.gov/
-taxonomy:
- - name: Topics
- values:
- - Biodiversity
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Floods
- - Land Use
- - Coastal Risk
- - name: Source
- values:
- - HLS
-infoDescription: |
- ::markdown
- Input data from Landsat 8/9 and Sentinel-2A/B is reprojected and Sentinel-2 data adjusted so that the output data products, HLSL30 (Landsat-derived) and HLSS30 (Sentinel-2-derived) can be used interchangeably. The harmonization of the Optical Land Imager (OLI) on Landsat 8/9 and Multispectral Imager (MSI) on Sentinel-2A/B increases the time series density of plot-scale observations such that data is available every 2-4 days over a given location.
-layers:
- - id: hls-l30-002-ej
- stacCol: hls-l30-002-ej-reprocessed
- name: HLS Landsat SWIR
- type: raster
- description: 'Harmonized Landsat SWIR: small subset near Puerto Rico'
- zoomExtent:
- - 4
- - 20
- sourceParams:
- algorithm: swir
- assets:
- - B07
- - B05
- - B04
- compare:
- datasetId: hls_events
- layerId: hls-l30-002-ej
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- info:
- source: NASA
- spatialExtent: Puerto Rico
- temporalResolution: Daily
- unit: N/A
-
- - id: hls-s30-002-ej
- stacCol: hls-s30-002-ej-reprocessed
- name: HLS Sentinel-2 SWIR
- type: raster
- description: 'Harmonized Sentinel-2 SWIR: small subset near Puerto Rico'
- zoomExtent:
- - 4
- - 20
- sourceParams:
- algorithm: swir
- assets:
- - B12
- - B8A
- - B04
- info:
- source: NASA
- spatialExtent: Puerto Rico
- temporalResolution: Daily
- unit: N/A
-
- - id: ndvi
- stacCol: hls-ndvi
- name: NDVI
- type: raster
- description: "NDVI: 0 to 1; 0 = little to no vegetation; 1 = heavy vegetation"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylgn
- rescale:
- - 0
- - 1
- legend:
- type: gradient
- min: "0"
- max: "1"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee08b"
- - "#d9ef8b"
- - "#66bd63"
- - "#006837"
- compare:
- datasetId: hls_events
- layerId: ndvi
-
- - id: ndvi_difference
- stacCol: hls-ndvi-difference
- name: NDVI Difference
- type: raster
- description: "NDVI Difference: -1 to 1; -1 = decrease in vegetation; 1 = increase in vegetation"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdbu
- rescale:
- - "-1"
- - "1"
- legend:
- type: gradient
- min: "-1"
- max: "1"
- stops:
- - "#67001f"
- - "#d6604d"
- - "#fddbc7"
- - "#d1e5f0"
- - "#4393c3"
- - "#053061"
-
- - id: entropy-difference
- stacCol: hls-entropy-difference
- name: Entropy Difference
- type: raster
- description: "Bitemporal Different with higher values indicating higher likelihood of change from before to after Ian."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - "-1"
- - "1"
- legend:
- type: gradient
- min: "-1"
- max: "1"
- stops:
- - '#0000ff'
- - '#6666ff'
- - '#ccccff'
- - '#ffffff'
- - '#ffcccc'
- - '#ff6666'
- - '#ff0000'
-
-
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** Sept. 1, 2017 - Sept. 22, 2023
- - **Spatial Resolution:** 30 m x 30 m
- - **Data Units:** Surface Reflectance
- - **Data Type:** Research
- - **Data Latency:** 2 to 3 days
-
-
-
-
- HLS normalized difference vegetation index (NDVI) pre/post-Ian over Sanibel Island, Cape Coral, and portions of Fort Myers.
-
-
-
-
-
-
-
- ## About
- Input data from Landsat 8/9 and Sentinel-2A/B is reprojected and Sentinel-2 data adjusted so that the output data products, HLSL30 (Landsat-derived) and HLSS30 (Sentinel-2-derived) can be used interchangeably. The harmonization of the Optical Land Imager (OLI) on Landsat 8/9 and Multispectral Imager (MSI) on Sentinel-2A/B increases the time series density of plot-scale observations such that data is available every 2-4 days over a given location.
-
- The data currently in this dashboard represents a subset of the available data. Data will continue to be added to this dashboard over time.
-
-
-
-
-
- ## Scientific research
- The production of atmospherically corrected HLS products is a collaborative effort between NASA, the U.S. Geological Survey (USGS), and the European Space Agency (ESA). Bandpass adjustments applied to Sentinel-2 data spectral bands match surface reflectance values in corresponding spectral bands in Landsat 8 and 9. This adjustment allows for 30m observation of the land surface every 2-4 days. HLSL30 and HLSS30 products are typically used for land use land cover applications including land use change, land use classification, fire monitoring, agricultural monitoring, and flooding, among others.
-
-
-
-
-
-
-
- Harmonized Landsat 8 SWIR image provides enhanced contrast to detect flood extent in Puerto Rico before and after Hurricane Maria in 2017.
-
-
-
-## Interpreting the data
-HLS imagery shows the impact of flooding for Hurricanes Maria and Ida that made landfall in Puerto Rico (2017) and New Orleans, LA (2021) respectively. The imagery displayed is a shortwave infrared (SWIR) false color composite that provides enhanced contrast to detect flood extent. In SWIR false color composite imagery, water is identified by dark blue colors, vegetation is bright green, clouds are white, and ice is blue.
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- - **The Deadliest Flood of the 21st Century**
-
- - **Hurricane Ian and Impacts on Vegetation**
-
-
-
-
-
-
-
- ## Source Data Product Citation
- Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161.
-
- ## Key Publications
- Su Ye, John Rogan, Zhe Zhu, J. Ronald Eastman, A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection, Remote Sensing of Environment, Volume 252, 2021,112167, ISSN 0034-4257, (https://doi.org/10.1016/j.rse.2020.112167)[https://doi.org/10.1016/j.rse.2020.112167].
-
- Su Ye, Zhe Zhu, Guofeng Cao, Object-based continuous monitoring of land disturbances from dense Landsat time series, Remote Sensing of Environment, Volume 287, 2023, 113462, ISSN 0034-4257, (https://doi.org/10.1016/j.rse.2023.113462)[https://doi.org/10.1016/j.rse.2023.113462].
-
- ### Other Relevant Publications
- Ye, S., Zhu, Z., & Suh, J. W. (2024). Leveraging past information and machine learning to accelerate land disturbance monitoring. Remote Sensing of Environment, 305, 114071.
-
- Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSS30.002
-
- Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSL30.002
-
-
- ## Acknowledgment
- This work has been supported by the USGS-NASA Landsat Science Team (LST) Program for Toward Near Real-time Monitoring and Characterization of Land Surface Change for the Conterminous US (140G0119C0008)
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional Resources
- 1. [HLSL30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlsl30v002/)
-
- 2. [HLSS30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlss30v002/)
-
- 3. [HLS Webinar with LPDAAC](https://www.youtube.com/watch?v=N2S4KGNo_XY)
-
- 4. [Harmonized Landsat-Sentinel](https://hls.gsfc.nasa.gov/)
-
- 5. [European Commission Report 9/13/2023](https://upload.wikimedia.org/wikipedia/commons/2/2c/ECDM_20230913_FL_Libya.pdf)
-
-
diff --git a/datasets/hls_2.0.data.mdx b/datasets/hls_2.0.data.mdx
deleted file mode 100644
index c6d13f6e7c..0000000000
--- a/datasets/hls_2.0.data.mdx
+++ /dev/null
@@ -1,172 +0,0 @@
----
-id: HLS_2.0
-name: "Harmonized Landsat Sentinel-2 (HLS)"
-description: "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites."
-media:
- src: ::file ./media/hls_20241004_true_color.jpg
- alt: HLS L30 and S30 true color composite from October 4, 2024 over Lake Superior
- author:
- name: NASA
- url:
-taxonomy:
- - name: Topics
- values:
- - Satellite Imagery
- - name: Source
- values:
- - NASA
- - USGS
- - ESA
-infoDescription: |
- ::markdown
- The Landsat (HLSL30) and Sentinel-2 (HLSS30) collections are processed separately and can be viewed in an orbit-wise fashion by selecting a date. There will not be imagery for all areas on every day so you may need to toggle between several days to find suitable imagery for your area of interest.
-
- ## Dataset Details:
-
- - Temporal Extent:
- - HLSL30: 2013 - present
- - HLSS30: 2015 - present
- - Temporal Resolution: revisit ~2-3 days
- - Spatial Extent: global
- - Spatial Resolution: 30 x 30 meters
- - Data Latency: <5 days
-
-
-layers:
- - id: HLSL30_2.0_true_color
- media:
- src: ::file ./media/hls_20241004_true_color.jpg
- alt: HLS L30 and S30 true color composite from October 4, 2024 over Lake Superior
- type: cmr
- stacCol: HLSL30_2.0
- stacApiEndpoint: "https://cmr.earthdata.nasa.gov/stac/LPCLOUD"
- tileApiEndpoint: "https://staging.openveda.cloud/api/titiler-cmr/WebMercatorQuad/tilejson.json"
- name: "HLS Landsat 8/9 True Color Composite"
- description: "True color composite image from Landsat 8/9 bands B04, B03, B02"
- time_density: day
- zoomExtent:
- - 7
- - 18
- sourceParams:
- concept_id: C2021957657-LPCLOUD
- backend: rasterio
- bands_regex: B[0-9][0-9]
- bands:
- - B04
- - B03
- - B02
- minzoom: 7
- maxzoom: 18
- color_formula: Gamma RGB 3.5 Saturation 1.2 Sigmoidal RGB 15 0.35
- - id: HLSL30_2.0_false_color
- media:
- src: ::file ./media/hls_20241004_false_color.jpg
- alt: HLS L30 and S30 false color composite from October 4, 2024 over Lake Superior
- type: cmr
- stacCol: HLSL30_2.0
- stacApiEndpoint: "https://cmr.earthdata.nasa.gov/stac/LPCLOUD"
- tileApiEndpoint: "https://staging.openveda.cloud/api/titiler-cmr/WebMercatorQuad/tilejson.json"
- name: "HLS Landsat 8/9 False Color Composite"
- description: "False color composite image from Landsat 8/9 bands B05, B03, B02"
- time_density: day
- zoomExtent:
- - 7
- - 18
- sourceParams:
- concept_id: C2021957657-LPCLOUD
- backend: rasterio
- bands_regex: B[0-9][0-9]
- bands:
- - B05
- - B03
- - B02
- minzoom: 7
- maxzoom: 18
- color_formula: Gamma RGB 2.5 Saturation 1.2 Sigmoidal RGB 10 0.35
- - id: HLSS30_2.0_true_color
- media:
- src: ::file ./media/hls_20241004_true_color.jpg
- alt: HLS L30 and S30 true color composite from October 4, 2024 over Lake Superior
- type: cmr
- stacCol: HLSS30_2.0
- stacApiEndpoint: "https://cmr.earthdata.nasa.gov/stac/LPCLOUD"
- tileApiEndpoint: "https://staging.openveda.cloud/api/titiler-cmr/WebMercatorQuad/tilejson.json"
- name: "HLS Sentinel-2 True Color Composite"
- description: "True color composite image from Sentinel-2 bands B04, B03, B02"
- time_density: day
- zoomExtent:
- - 7
- - 18
- sourceParams:
- concept_id: C2021957295-LPCLOUD
- backend: rasterio
- bands_regex: B[0-9][0-9A-Za-z]
- bands:
- - B04
- - B03
- - B02
- minzoom: 7
- maxzoom: 18
- color_formula: Gamma RGB 3.5 Saturation 1.2 Sigmoidal RGB 15 0.35
- - id: HLSS30_2.0_false_color
- media:
- src: ::file ./media/hls_20241004_false_color.jpg
- alt: HLS L30 and S30 false color composite from October 4, 2024 over Lake Superior
- type: cmr
- stacCol: HLSS30_2.0
- stacApiEndpoint: "https://cmr.earthdata.nasa.gov/stac/LPCLOUD"
- tileApiEndpoint: "https://staging.openveda.cloud/api/titiler-cmr/WebMercatorQuad/tilejson.json"
- name: "HLS Sentinel-2 False Color Composite"
- description: "False color composite image from Sentinel-2 bands B8A, B03, B02"
- time_density: day
- zoomExtent:
- - 7
- - 18
- sourceParams:
- concept_id: C2021957295-LPCLOUD
- backend: rasterio
- bands_regex: B[0-9][0-9A-Za-z]
- bands:
- - B8A
- - B03
- - B02
- minzoom: 7
- maxzoom: 18
- color_formula: Gamma RGB 2.5 Saturation 1.2 Sigmoidal RGB 10 0.35
----
-
-
-
- The Harmonized Landsat and Sentinel-2 (HLS) project is an extension of research conducted at NASA's Goddard Space Flight Center in Greenbelt, MD, that takes input data from the joint NASA/USGS Landsat 8 and Landsat 9 and the ESA (European Space Agency) Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites to generate a harmonized, analysis-ready surface reflectance data product with observations every two to three days.
-
- The HLS project is a major outcome of the [Satellite Needs Working Group](https://www.earthdata.nasa.gov/about/nasa-support-snwg) assessment in 2016. In that assessment, federal agencies and end users identified a need for more frequent Landsat-like observations to track short-term changes in vegetation and other land components to support agricultural monitoring and land cover classification at moderate to high resolution in both the visible and thermal components of the electromagnetic spectrum. Spectral similarities between the Landsat 8 Operational Land Imager ([OLI](https://www.earthdata.nasa.gov/data/instruments/oli)), the Landsat 9 OLI-2, and the Sentinel-2 MultiSpectral Instrument ([MSI](https://www.earthdata.nasa.gov/data/instruments/sentinel-2-msi)) present an opportunity to harmonize data from these sensors to generate higher-frequency imagery products for land surface monitoring and applications.
-
- Previous versions of HLS data products produced by the HLS Science Team at Goddard had limited spatial coverage — only covering North America and other select global locations. The current version of the HLS algorithm is a cloud-based software stack that expands the spatial coverage to include all land masses globally, outside of Antarctica.
-
- Two data products are generated as part of the HLS project: the L30 data product generated with Landsat 8 and Landsat 9 data, and the S30 product generated using Sentinel-2 data. These data are available through [Earthdata Search](https://search.earthdata.nasa.gov/search?q=HLS) as well as through NASA's Land Processes Distributed Active Archive Center ([LP DAAC](https://www.earthdata.nasa.gov/centers/lp-daac)). Feedback or questions about HLS data products can be made in the [Earthdata Forum for HLS](https://forum.earthdata.nasa.gov/viewtopic.php?f=7&t=618&sid=6c381c5ceb5d223e9910f8f6e7dce06e).
-
- ## Dataset Details
- - **Temporal Extent:**
- - HLSL30: April 2013 - present
- - HLSS30: December 2015 - present
- - **Temporal Resolution:** revisit ~2-3 days
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 30 x 30 meters
- - **Data Latency:** ~5 days
-
-
-
-
- HLSL30 imagery from October 1, 2024 showing fall foliage in the North Shore Highlands in Northern Minnesota, US
-
-
-
diff --git a/datasets/is2sitmogr4.data.mdx b/datasets/is2sitmogr4.data.mdx
deleted file mode 100644
index 0c9a6b5733..0000000000
--- a/datasets/is2sitmogr4.data.mdx
+++ /dev/null
@@ -1,137 +0,0 @@
----
-id: is2sitmogr4
-name: "Sea Ice Thickness"
-description: A gridded view of winter sea ice thickness across the Arctic Ocean
-media:
- src: ::file ./media/sea-ice-thick--dataset-cover.jpg
- alt: Huge chunk of ice calving into the sea below
- author:
- name: Matt Broch
- url: https://unsplash.com/photos/bwD3GLrV4pY
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - Sea Level Change
- - name: Subtopics
- values:
- - Coastal Risk
- - Glaciers
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- This data set reports monthly, gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading.
-layers:
- - id: IS2SITMOGR4-cog
- stacCol: IS2SITMOGR4-cog
- name: ICESat-2 L4 Monthly Gridded Sea Ice Thickness
- type: raster
- description: "Sea Ice thickness in meters"
- projection:
- id: polarNorth
- zoomExtent:
- - 0
- - 16
- sourceParams:
- colormap_name: plasma
- rescale:
- - 0
- - 16
- legend:
- type: gradient
- min: Less
- max: More
- stops:
- - '#0d0887'
- - '#7e03a8'
- - '#cc4778'
- - '#f89540'
- - '#f89540'
- info:
- source: NASA
- spatialExtent: Polar
- temporalResolution: Monthly
- unit: Meters
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2018 - 2021
- - **Spatial Extent:** Northern latitudes
- - **Spatial Resolution:** ~10 km
- - **Data Units:** meters (m)
- - **Data Type:** Research
-
-
-
-
- Monthly sea ice thickness over the Northern Hemisphere.
-
-
-
-
-
-
-
- ## About
-
- This data set reports monthly, gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10), Version 5 data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading.
-
-
-
-
-
-
- ## Reference
-
- Petty, A. A., N. Kurtz, R. Kwok, T. Markus, T. A. Neumann, and N. Keeney. 2022. ICESat-2 L4 Monthly Gridded Sea Ice Thickness, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/OE8BDP5KU30Q.
-
-
-
-
-
-
- ## Data Access
-
- Access this dataset at the [National Snow & Ice Data Center](https://nsidc.org/data/IS2SITMOGR4/versions/2)
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/la-fires-frp.data.mdx b/datasets/la-fires-frp.data.mdx
deleted file mode 100644
index 36f52ec393..0000000000
--- a/datasets/la-fires-frp.data.mdx
+++ /dev/null
@@ -1,188 +0,0 @@
----
-id: la-fires-frp
-name: 'Fire Radiative Power (FRP) - 2025 LA Fires'
-description: "Maximum daily FRP during the week of the 2025 Palisades and Eaton Fires in Southern California."
-media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
- author:
- name: NASA CDSA - Planet Labs
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - MODIS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** January 6-12, 2025
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 0.1 x 0.1 degree
- - **Data Units:** W m-2
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: la-fires-frp
- stacCol: la-fires-frp
- name: Fire Radiative Power (FRP) - 2025 LA Fires
- type: raster
- description: 'Maximum daily FRP during the week of the 2025 Palisades and Eaton Fires in Southern California.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: afmhot_r
- rescale:
- - 0
- - 100
- legend:
- type: gradient
- unit:
- label: W m-2
- min: 0
- max: 100
- stops:
- - "#FFFFFF"
- - "#FFFFC6"
- - "#FFFF8E"
- - "#FFD555"
- - "#FF9C1C"
- - "#E36300"
- - "#AA2B00"
- - "#710000"
- - "#390000"
- - "#000000"
- info:
- source: MODIS
- spatialExtent: Global
- temporalResolution: Daily
- unit: W m-2
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 6-12, 2025
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 0.1 x 0.1 degree
- - **Data Units:** W m-2
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- Maximum FRP in the Los Angeles area on January 9, 2025.
-
-
-
-
-
-
-
- ### About
-
- The Fire Radiative Power (FRP) dataset is derived from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor aboard the Terra and Aqua satellites. FRP measures the energy released by active fires, providing a quantitative estimate of fire intensity and biomass burning. This dataset highlights the maximum FRP values detected daily during the peak of the 2025 Palisades and Eaton Fires in Southern California.
-
-
-
-
-
-
-
-
- ### What MODIS FRP Data Offers
-
- * Global Fire Monitoring: MODIS provides near-real-time fire observations across the globe, detecting wildfires and controlled burns.
- * Fire Intensity Estimation: FRP values indicate the energy output of fires, which helps in assessing fire behavior and severity.
- * Climate and Atmospheric Research: FRP data contributes to studies on smoke emissions, air quality, and carbon cycle modeling.
- * Disaster Response and Management: Fire radiative power helps first responders and policymakers track wildfire progression and allocate resources effectively.
-
-
-
-
-
-
-
- ### Access the Data
-
- The MODIS FRP dataset is publicly available via:
-
- * [NASA FIRMS (Fire Information for Resource Management System)](https://firms.modaps.eosdis.nasa.gov/)
-
- * [NASA Earthdata](https://earthdata.nasa.gov/)
-
- * [MODIS Active Fire Products](https://modis.gsfc.nasa.gov/data/dataprod/mod14.php)
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- NASA LP DAAC, 2025. MODIS/Terra and Aqua Fire Radiative Power Data.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Giglio, L., J. T. Randerson, and G. R. van der Werf (2013). Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). Journal of Geophysical Research: Biogeosciences, 118(1), 317-328.https://doi.org/10.1002/jgrg.20042
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Fanning the Flames**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
diff --git a/datasets/la-fires-gedi.data.mdx b/datasets/la-fires-gedi.data.mdx
deleted file mode 100644
index 30b0209ce0..0000000000
--- a/datasets/la-fires-gedi.data.mdx
+++ /dev/null
@@ -1,183 +0,0 @@
----
-id: la-fires-gedi
-isHidden: true
-name: 'Eaton and Palisades Fires (2025) Above Ground Biomass Density'
-description: "Satellite-derived above-ground biomass density (extracted from GEDI data) over the burn scar extents of the Eaton and Palisades Fires in Southern California (2025)."
-media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
- author:
- name: NASA CDSA - Planet Labs
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - NASA LPDAAC
-infoDescription: |
- ::markdown
- - **Temporal Extent:** January 7, 2025
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Local
- - **Spatial Resolution:** 3 m
- - **Data Units:** Mg/Ha
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: la-fires-gedi
- stacCol: la-fires-gedi
- name: Eaton and Palisades Fires (2025) Above Ground Biomass Density
- type: raster
- description: 'Satellite-derived above-ground biomass density (extracted from GEDI data) over the burn scar extents of the Eaton and Palisades Fires in Southern California (2025).'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: ylgn
- rescale:
- - 0
- - 250
- legend:
- type: gradient
- unit:
- label: Mg ha-1
- min: 0
- max: 250
- stops:
- - "#FFFFE5"
- - "#F7FCB9"
- - "#D9F0A3"
- - "#ADDD8E"
- - "#78C679"
- - "#41AB5D"
- - "#238443"
- - "#006837"
- - "#004529"
- info:
- source: NASA LPDAAC
- spatialExtent: Local
- temporalResolution: N/A
- unit: MG Ha-1
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 7, 2025
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Local
- - **Spatial Resolution:** 3 m
- - **Data Units:** Mg/Ha
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- GEDI-derived above-ground biomass density across the 2025 Eaton Fire.
-
-
-
-
-
-
-
- ### About
-
- The Global Ecosystem Dynamics Investigation (GEDI) provides high-resolution lidar measurements of forest structure and biomass, enabling improved estimates of above-ground biomass density. This dataset represents biomass distribution over the burn scars of the Eaton and Palisades Fires, offering valuable insight into vegetation carbon loss and recovery potential.
-
-
-
-
-
-
-
-
-
- ### What This Data Offers
-
- * High-Resolution Biomass Estimates: GEDI data provides detailed above-ground biomass density at 3-meter spatial resolution.
- * Post-Fire Carbon Analysis: Helps assess carbon emissions from wildfires and potential for regrowth.
- * Forest and Fire Management: Supports decision-making for land restoration and fire recovery strategies.
- * NASA-Led Remote Sensing Innovation: Uses advanced spaceborne lidar technology to generate precise measurements.
-
-
-
-
-
-
-
- ### Access the Data
-
- The GEDI data used for this analysis is publicly available [here](https://www.earthdata.nasa.gov/data/instruments/gedi-lidar)
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- NASA Earthdata - GEDI Level 4A Aboveground Biomass Density.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Dubayah, R., et al. (2020). The Global Ecosystem Dynamics Investigation: High-resolution lidar measurements of Earth's forests and topography. *Science of Remote Sensing, 2*, 100002. https://doi.org/10.1016/j.srs.2020.100002
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Fanning the Flames**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
diff --git a/datasets/la-fires-hls.data.mdx b/datasets/la-fires-hls.data.mdx
deleted file mode 100644
index f086507b89..0000000000
--- a/datasets/la-fires-hls.data.mdx
+++ /dev/null
@@ -1,63 +0,0 @@
----
-id: la-fires-HLS
-isHidden: true
-name: 'HLS False Color Imagery - 2025 LA Fires'
-description: "HLS L30 Satellite Imagery before and after the 2025 LA Fires."
-media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
- author:
- name: NASA CDSA - Planet Labs
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - HLS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** January 6-14, 2025
- - **Temporal Resolution:** ~3 days
- - **Spatial Extent:** Global with the exception of Antarctica
- - **Spatial Resolution:** 30 m x 30 m
- - **Data Units:** Surface Reflectance
- - **Data Type:** Research
- - **Data Latency:** 2 to 3 days
-
-layers:
- - id: la-fires-HLS
- stacCol: la-fires-HLS
- name: HLS False Color Satellite Imagery (2025 LA Fires)
- type: raster
- description: 'HLS Satellite Imagery showing the burn scars caused by the 2025 LA Fires.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- rescale: 0,4000
- legend:
- type: categorical
- stops:
- - color: "rgba(0, 0, 0, 0)" # using transparent for now to fix the issues with scrollytelling
- label: "Imagery"
- compare:
- datasetId: la-fires-HLS
- layerId: la-fires-HLS
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- info:
- source: HLS
- spatialExtent: nearly global
- temporalResolution: ~3 days
- unit: Radiance
-
----
diff --git a/datasets/la-fires-hrrr-wind.data.mdx b/datasets/la-fires-hrrr-wind.data.mdx
deleted file mode 100644
index dd4c5ffa6b..0000000000
--- a/datasets/la-fires-hrrr-wind.data.mdx
+++ /dev/null
@@ -1,190 +0,0 @@
----
-id: la-fires-hrrr-wind
-isHidden: true
-name: 'HRRR 10 Meter Wind Gusts (2025 LA Fires)'
-description: "Simulated 10 meter wind gusts from the High Resolution Rapid Refresh (HRRR) at 02Z on January 8, 2025, around the time that the Eaton Fire ignited. The Palisades Fire had just been ignited earlier that day."
-media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
- author:
- name: NASA CDSA - Planet Labs
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - NOAA
-infoDescription: |
- ::markdown
- - **Temporal Extent:** January 9, 2025
- - **Temporal Resolution:** Hourly
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 3 km
- - **Data Units:** mph
- - **Data Type:** Research/Modeling
- - **Data Latency:** Hourly
-
-layers:
- - id: la-fires-hrrr-wind
- stacCol: la-fires-hrrr-wind
- name: HRRR 10 Meter Wind Gusts (2025 LA Fires)
- type: raster
- description: 'Simulated 10 meter wind gusts from the High Resolution Rapid Refresh (HRRR) at 02Z on January 8, 2025, around the time that the Eaton Fire ignited. The Palisades Fire had just been ignited earlier that day.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: cool
- rescale:
- - 0
- - 60
- legend:
- type: gradient
- unit:
- label: mph
- min: 0
- max: 60
- stops:
- - "#0055FF"
- - "#007AFF"
- - "#009FFF"
- - "#00C3FF"
- - "#00E0FF"
- - "#00F7FF"
- - "#2CFFFF"
- - "#5BFFFF"
- - "#82FCFF"
- - "#A7F0FF"
- - "#C5E2FF"
- - "#DCD3FF"
- - "#EFA8FF"
- - "#F67CFF"
- - "#FF00FF"
- info:
- source: NOAA NCEP
- spatialExtent: CONUS
- temporalResolution: Hourly
- unit: mph
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 8, 2025, 02Z
- - **Temporal Resolution:** Hourly
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 3 km
- - **Data Units:** mph
- - **Data Type:** Research/Modeling
- - **Data Latency:** Hourly
-
-
-
-
- 10 meter modeled wind gusts from the HRRR on January 8, 2025 at 02 UTC.
-
- Percent damage by parcel from the 2025 Eaton Fire in Altadena, California as determined by the LA County Assessor's Office.
-
-
-
-
-
-
-
- ### About
-
- This dataset provides an assessment of parcel-level structural damage caused by the 2025 Eaton and Palisades Fires in Los Angeles County, CA. The Los Angeles County Assessor's Office evaluated damage percentages based on field assessments, aerial imagery, and satellite data. The dataset categorizes each parcel into different levels of damage severity, supporting post-disaster impact analysis and recovery planning.
-
-
-
-
-
-
-
-
- ### What This Data Offers
-
- * Detailed Damage Assessments: Provides parcel-level classification of wildfire impact ranging from "No Damage" to "Destroyed" (>50% damage).
- * Support for Disaster Recovery Efforts: Used by local agencies to assess financial losses and prioritize rebuilding efforts.
- * Integration with Other Datasets: Can be combined with satellite imagery, topography, and fire behavior models for enhanced post-fire analysis.
- * Public and Governmental Use: Valuable for emergency managers, policymakers, researchers, and insurance assessments.
-
-
-
-
-
-
- ### Access the Data
-
- The LA County Parcel Damage dataset can be accessed through:
-
- * [Los Angeles County Assessor's Office](https://assessor.lacounty.gov/)
-
- * [California Statewide Parcel Data](https://gis.data.ca.gov/)
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- Los Angeles County Assessor’s Office, 2025. Wildfire Parcel Damage Assessment for Eaton and Palisades Fires.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- FEMA (2022). Post-Wildfire Damage Assessment and Recovery Planning Guide. https://www.fema.gov/emergency-managers/national-preparedness/plan
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Fanning the Flames**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
diff --git a/datasets/la-fires-sea.data.mdx b/datasets/la-fires-sea.data.mdx
deleted file mode 100644
index c1d9f6da75..0000000000
--- a/datasets/la-fires-sea.data.mdx
+++ /dev/null
@@ -1,178 +0,0 @@
----
-id: la-fires-sea
-isHidden: true
-name: 'LA County Significant Ecological Areas (SEAs)'
-description: "Significant Ecological Areas and Coastal Resource Areas in Los Angeles County, California."
-media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
- author:
- name: NASA CDSA - Planet Labs
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - LA County Dept. of Regional Planning
-infoDescription: |
- ::markdown
- - **Temporal Extent:** January 1, 2025
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** LA County, CA
- - **Spatial Resolution:** 30 m
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: la-fires-sea
- stacCol: la-fires-sea
- name: LA County Significant Ecological Areas (SEAs)
- type: raster
- description: "Significant Ecological Areas (SEAs) and Coastal Resource Areas (CRAs) in Los Angeles County, California."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap: '{"1":[18,94,135,255],"2":[12,110,20,255],"3":[73,230,86,255],"4":[73,141,230,255],"5":[73,230,227,255]}'
- legend:
- type: categorical
- min: "1"
- max: "5"
- stops:
- - color: "#49e656"
- label: "SEA"
- - color: "#0c6e14"
- label: "SEA (Urban)"
- - color: "#498de6"
- label: "CRA"
- - color: "#125e87"
- label: "CRA (Urban)"
- - color: "#49e6e3"
- label: "CRA (Ocean)"
- info:
- source: LA County Dept. of Regional Planning
- spatialExtent: LA County, CA
- temporalResolution: N/A
- unit: N/A
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 1, 2025
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** LA County, CA
- - **Spatial Resolution:** 30 m
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- LA County's Significant Ecological Areas (SEAs) in the area impacted by the 2025 Palisades Fire.
-
-
-
-
-
-
-
- ### About
-
- The Significant Ecological Areas (SEAs) and Coastal Resource Areas (CRAs) dataset identifies ecologically valuable and environmentally sensitive regions in Los Angeles County. These areas are designated based on biodiversity, habitat connectivity, and ecological function. SEAs support native plant and animal species, while CRAs include coastal habitats vital for marine and terrestrial ecosystems.
-
-
-
-
-
-
-
-
- ### What This Data Offers
-
- * Biodiversity Protection: SEAs highlight areas critical for wildlife, native vegetation, and ecosystem services.
- * Coastal Resource Management: CRAs include coastal wetlands, dunes, and estuaries, providing resilience against sea-level rise and erosion.
- * Land Use Planning & Conservation: Supports environmental protection policies and planning efforts in Los Angeles County.
- * Wildfire Risk and Recovery Analysis: Helps assess how ecological areas are impacted by fire and guides post-fire restoration efforts.
-
-
-
-
-
-
- ### Access the Data
-
- The Significant Ecological Areas dataset is available through the [LA County Department of Regional Planning](https://planning.lacounty.gov/sea).
-
-
-
-
-
-
- ### Citing this Dataset
-
- LA County Dept. of Regional Planning, 2025. Significant Ecological Areas and Coastal Resource Areas Dataset.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- LA County Dept. of Regional Planning (2022). Significant Ecological Areas Program: Conservation Guidelines and Land Use Planning in LA County. https://planning.lacounty.gov/sea .
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Fanning the Flames**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
diff --git a/datasets/la-fires-slope.data.mdx b/datasets/la-fires-slope.data.mdx
deleted file mode 100644
index 438d776036..0000000000
--- a/datasets/la-fires-slope.data.mdx
+++ /dev/null
@@ -1,182 +0,0 @@
----
-id: la-fires-slope
-isHidden: true
-name: 'Eaton and Palisades Fires (2025) Slope'
-description: "Computed slope from a digital elevation model (DEM) of the 2025 Palisades and Eaton Fires' burn scar extent."
-media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
- author:
- name: NASA CDSA - Planet Labs
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - USGS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** 2024-02-07
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** Eaton and Palisades Fire burn scar extent
- - **Spatial Resolution:** 30 m
- - **Data Units:** degrees
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: la-fires-slope
- stacCol: la-fires-slope
- name: Eaton and Palisades Fires (2025) Slope
- type: raster
- description: 'Computed slope from a digital elevation model (DEM) of the 2025 Palisades and Eaton Fires burn scar extent.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: ylorrd
- rescale:
- - 0
- - 90
- legend:
- type: gradient
- unit:
- label: degrees
- min: 0
- max: 90
- stops:
- - "#FFFFCC"
- - "#FFF7BC"
- - "#FFEDA0"
- - "#FED976"
- - "#FEB24C"
- - "#FD8D3C"
- - "#FC4E2A"
- - "#E31A1C"
- - "#BD0026"
- - "#800026"
- info:
- source: USGS
- spatialExtent: Local
- temporalResolution: N/A
- unit: degrees
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2024-02-07
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** Eaton and Palisades Fire burn scar extent
- - **Spatial Resolution:** 30 m
- - **Data Units:** degrees
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
- Digital Elevation Model (DEM)-derived slope values for the Eaton Fire burn scar extent.
-
-
-
-
-
-
-
- ### About
-
- The slope dataset is derived from a 30-meter Digital Elevation Model (DEM) provided by the USGS National Map. Slope is a critical factor in wildfire behavior, as steeper slopes can accelerate fire spread by pre-heating vegetation ahead of the flames. Understanding terrain characteristics is essential for post-fire analysis, erosion modeling, and mitigation planning.
-
-
-
-
-
-
-
-
- ### What USGS DEM Data Offers
-
- * High-Resolution Elevation Data: The 30-meter DEM provides detailed terrain information used for slope calculations.
- * Critical for Wildfire Analysis: Slope influences fire spread, post-burn erosion risks, and flood susceptibility.
- * Nationally Available: USGS provides extensive DEM coverage across the U.S., making it a valuable dataset for environmental analysis.
- * Supports Hydrological and Geological Studies: The dataset is widely used for watershed modeling, geologic mapping, and land cover analysis.
-
-
-
-
-
-
- ### Access the Data
-
- The original DEM dataset used for this slope computation is available from the [USGS National Map](https://www.usgs.gov/programs/national-geospatial-program/national-map).
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- USGS National Elevation Dataset (NED), 2024. Available at: https://www.usgs.gov/
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Gesch, D. B. (2018). "The National Elevation Dataset." Photogrammetric Engineering and Remote Sensing, 70(6), 775-780.https://doi.org/10.14358/PERS.70.6.775
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Fanning the Flames**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
diff --git a/datasets/lahaina-fire.data.mdx b/datasets/lahaina-fire.data.mdx
deleted file mode 100644
index 5a718790b3..0000000000
--- a/datasets/lahaina-fire.data.mdx
+++ /dev/null
@@ -1,257 +0,0 @@
----
-id: lahaina-fire
-name: 'Lahaina Fire'
-isHidden: true
-description: "HLS (BAIS2 and SWIR FalseColor composites) and Landsat-8 thermal imagery supporting the Lahaina, HI Wildfire Story"
-media:
- src: ::file ./media/lahaina-fire-background.jpg
- alt: Wildfire erupting over Lahaina, HI, August 8, 2023
- author:
- name: Matthew Thayer/AP
- url: https://www.sfchronicle.com/travel/article/hawaii-fire-maui-lahaina-18289213.php
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - HLS
- - Landsat
-
-infoDescription: |
- ::markdown
- On August 8th, 2023, a devastating wildfire rapidly spread through the city of Lahaina, Hawai’i, which is located on the island of Maui and home to over 13,000 residents. This destructive wildfire was initially ignited by a downed powerline on Lahainaluna Road and was later fueled by intense wind gusts that persisted throughout the day. The National Weather Service recorded wind gusts as high as 67 mph in the area, contributing to the rapid spread of the wildfire across much of Lahaina during the afternoon hours of August 8th.
-layers:
- - id: hls-bais2-v2
- stacCol: hls-bais2-v2
- name: BAIS-2 Burned Area
- type: raster
- description: 'Experimental burned-area calculation from the HLS scene taken on August 13,2023 over Lahaina, HI'
- zoomExtent:
- - 4
- - 20
- sourceParams:
- colormap_name: rdylbu_r
- rescale:
- -0.5
- - 1
- resampling: bilinear
- bidx: 1
- nodata: -9999
- compare:
- datasetId: lahaina-fire
- layerId: landsat-nighttime-thermal
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- min: "Low Burn Confidence"
- max: "High Burn Confidence"
- stops:
- - "#313695"
- - "#74add1"
- - "#e0f3f8"
- - "#fee090"
- - "#f46d43"
- - "#a50026"
- info:
- source: NASA
- spatialExtent: Hawaii
- temporalResolution: Annual
- unit: N/A
-
- - id: swir-falsecolor-composite
- stacCol: hls-swir-falsecolor-composite
- name: HLS SWIR FalseColor Composite
- type: raster
- description: 'HLS falsecolor composite imagery using S30 Bands 12, 8A, and 4, over Lahaina, HI.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0
- - 5000
- resampling: bilinear
- bidx: [1,2,3]
- compare:
- datasetId: lahaina-fire
- layerId: swir-falsecolor-composite
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- info:
- source: NASA
- spatialExtent: Hawaii
- temporalResolution: Annual
- unit: N/A
-
- - id: landsat-nighttime-thermal
- stacCol: landsat-nighttime-thermal
- name: Landsat-8 Thermal Band
- type: raster
- description: 'Nighttime Thermal band from Landsat-8 on August 8, 2023 shows the extent of the ongoing Lahaina Fire.'
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
-
- sourceParams:
- colormap_name: inferno
- nodata: 0
- resampling: bilinear
- bidx: 1
- rescale:
- - 180
- - 255
- compare:
- datasetId: lahaina-fire
- layerId: hls-bais2-v2
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
-
- legend:
- type: gradient
- min: "No Active Fire"
- max: "Active Fire"
- stops:
- - '#08041d'
- - '#1f0a46'
- - '#52076c'
- - '#f57c16'
- - '#f7cf39'
- info:
- source: NASA
- spatialExtent: Hawaii
- temporalResolution: Annual
- unit: N/A
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** August 8 - August 13, 2023
- - **Spatial Extent:** Hawaii
- - **Data Type:** Research
-
-
-
-
- BAIS2 burned area calculations show the most highly impacted and highest probability of scorched areas along Front Street in Lahaina, HI after the wildfire (2023 August 13).
-
-
-
-
-
-
- ## Overview
-
- On August 8th, 2023, a devastating wildfire rapidly spread through the city of Lahaina, Hawai’i, which is located on the island of Maui and home to over 13,000 residents. This destructive wildfire was initially ignited by a downed powerline on Lahainaluna Road and was later fueled by intense wind gusts that persisted throughout the day. The National Weather Service recorded wind gusts as high as 67 mph in the area, contributing to the rapid spread of the wildfire across much of Lahaina during the afternoon hours of August 8th.
-
-
-
-
-
-
-
-## Scientific Research
-
-Three datasets were utilized in the analysis of the Lahaina Fire. The first dataset is an experimental index was calculated using HLS imagery captured after the fire on August 13th, 2023. This innovative Burned Area Index (BAIS2) offers superior accuracy in delineating the burned areas compared to traditional Normalized Burn Ratio Indices 1 and 2. It particularly excels in identifying severely scorched fields situated just uphill from the city.
-
-The second dataset utilized is a three-band HLS composite image generated from the shortwave infrared, narrow near-infrared, and red bands of pre and post-fire HLS data. These scenes were acquired on August 8 and 13, 2023. The SWIR false color composite provides a visually compelling representation of the extent of the burned area resulting from the Lahaina Fire.
-
-The third dataset utilized is thermal infrared imagery collected from Landsat-8 on the night of August 8, 2023, offers essential insights into the intensity of the fire and the identification of hotspots within the affected area.
-
-These datasets support ongoing scientific research and analysis of the Lahaina Fire and its aftermath, including assessing the fire's impact on the local vegetation cover, the monitoring of long-term recovery of the local environment and ecosystem, and even evaluation of the effectiveness of fire mitigation and suppression efforts.
-
-
-
-
-
-
- ## Interpreting the Data
-
- These three datasets concerning the Lahaina Fire should be interpreted with a special consideration of the temporal, spatial, and environmental aspects.
-
- Temporal Aspects: The HLS SWIR FalseColor composite images were taken at 10:30 AM LST on August 8 and 13, 2023, which correspond to pre and post-fire times. The Landsat8 Thermal imagery was taken at 10:30 PM LST on August 8, 2023 as the wildfire was ongoing in the city of Lahaiana. The BAIS2 calculation was taken from the red band of the August 13, 2023 HLS imagery.
-
- Spatial Aspects: All three datasets are at 30 meter resolution, so as to ensure ease of comparison between the datasets.
-
- Environmental Aspects: When interpreting the data, it is essential to consider the local topography and land cover. Lahaina lies along the west coast of Maui, with rapidly rising terrain just to its east.
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Lahaina Fire**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
-
- ## Additional Resources
-
- * [Harmonized Landsat-Sentinel](https://hls.gsfc.nasa.gov/)
-
- * [Landsat8](https://landsat.gsfc.nasa.gov/satellites/landsat-8/)
-
- * [Landsat8 Nighttime Data Acquisition](https://www.usgs.gov/faqs/how-do-i-search-and-download-ascending-nighttime-landsat-scenes)
-
- * [PDC/FEMA Report on the Lahaina Fire](https://www.mauicounty.gov/CivicAlerts.aspx?AID=12683)
-
-
-
diff --git a/datasets/landsat-demo.data.mdx b/datasets/landsat-demo.data.mdx
new file mode 100644
index 0000000000..56db6c00dc
--- /dev/null
+++ b/datasets/landsat-demo.data.mdx
@@ -0,0 +1,89 @@
+---
+id: landsat-demo
+name: A-landsat-demo
+description: landsat-demo
+
+media:
+ src: ::file ./media/LA-Fires-background.png
+ alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
+ author:
+ name: Earth Observatory NASA
+ url: https://earthobservatory.nasa.gov
+taxonomy:
+ - name: Disaster
+ values:
+ - Hurricanes and Cyclones
+ - name: Source
+ values:
+ - NASA
+ - name: Event
+ values:
+ - 2024 Hurricane Milton
+
+infoDescription: |
+ ::markdown
+ - Temporal Extent: October 10, 2023 - Ongoing
+ - Temporal Resolution: Daily
+ - Spatial Extent: Global
+ - Spatial Resolution: 500m
+ - Data Units: Watts per square centimeter per steradian (W/cm²/sr)
+ - Data Type: Operational
+ - Data Latency: Updated daily
+layers:
+
+ - id: landsat-demo
+ stacApiEndpoint: https://dev.openveda.cloud/api/stac
+ tileApiEndpoint: https://dev.openveda.cloud/api/raster
+ stacCol: landsat-demo
+ name: landsat-demo
+ type: raster
+ initialDatetime: newest
+ description: 'landsat-demo'
+ media:
+ src: ::file ./media/LA-Fires-background.png
+ alt: Hurricane Milton layer
+ sourceParams:
+ assets: swir
+ bidx:
+ - 1
+ - 2
+ - 3
+ resampling: nearest
+ nodata: 0
+ zoomExtent:
+ - 0
+ - 20
+ analysis:
+ exclude: true
+ info:
+ source: NASA
+ spatialExtent: Global
+ temporalResolution: Daily
+
+---
+
+
+
+
+
+
+
+
+
+
+ Landsat demo
+
+
\ No newline at end of file
diff --git a/datasets/landslides-hls-disturbance.data.mdx b/datasets/landslides-hls-disturbance.data.mdx
deleted file mode 100644
index 7d11471d53..0000000000
--- a/datasets/landslides-hls-disturbance.data.mdx
+++ /dev/null
@@ -1,63 +0,0 @@
----
-id: landslides-hls-disturbance
-name: 'HLS Disturbance Product - Hurricane Helene'
-isHidden: true
-description: "Difference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of a 20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - HLS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** October 2, 2024
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Southern Appalachia
- - **Spatial Resolution:** 30 m
- - **Data Units:** percent
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: landslides-hls-disturbance
- stacCol: landslides-hls-disturbance
- name: HLS Disturbance Product - Hurricane Helene
- type: raster
- description: "Difference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of a 20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: ylorbr
- resampling: bilinear
- bidx: 1
- rescale: 1,100
- nodata: 0
- legend:
- type: gradient
- min: "1%"
- max: "100%"
- stops:
- - "#ffffe5"
- - "#fff7bc"
- - "#fee391"
- - "#fec44f"
- - "#fe9929"
- - "#ec7014"
- - "#cc4c02"
- - "#8c2d04"
- info:
- source: HLS
- spatialExtent: Southern Appalachia
- temporalResolution: N/A
- unit: percent
-
----
diff --git a/datasets/landslides-imerg.data.mdx b/datasets/landslides-imerg.data.mdx
deleted file mode 100644
index 515e51ac94..0000000000
--- a/datasets/landslides-imerg.data.mdx
+++ /dev/null
@@ -1,62 +0,0 @@
----
-id: landslides-imerg
-name: 'IMERG Total Precipitation - Hurricane Helene'
-isHidden: true
-description: "Total accumulated precipitation from IMERG Final over September 23-30, 2024 to cover precipitation related to Hurricane Helene in the Southeast US."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - GPM
-infoDescription: |
- ::markdown
- - **Temporal Extent:** September 23-30, 2024
- - **Temporal Resolution:** 30 min
- - **Spatial Extent:** Southeast US
- - **Spatial Resolution:** 0.1 x 0.1 degree
- - **Data Units:** inches
- - **Data Type:** Research
- - **Data Latency:** 30 min
-
-layers:
- - id: landslides-imerg
- stacCol: landslides-imerg
- name: IMERG Total Precipitation - Hurricane Helene
- type: raster
- description: "Total accumulated precipitation from IMERG Final over September 23-30, 2024 to cover precipitation related to Hurricane Helene in the Southeast US."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: nipy_spectral
- resampling: bilinear
- bidx: 1
- rescale: 0,18
- legend:
- type: gradient
- min: "0 in"
- max: "18 in"
- stops:
- - "#000000"
- - "#870098"
- - "#0078dd"
- - "#00aa88"
- - "#00dc00"
- - "#efed00"
- - "#fe0000"
- - "#cccccc"
- info:
- source: GPM
- spatialExtent: Southeast US
- temporalResolution: 30 min
- unit: inches
-
----
diff --git a/datasets/landslides-inventory.data.mdx b/datasets/landslides-inventory.data.mdx
deleted file mode 100644
index 882103d92f..0000000000
--- a/datasets/landslides-inventory.data.mdx
+++ /dev/null
@@ -1,55 +0,0 @@
----
-id: landslides-inventory
-name: 'USGS Landslides Inventory - Hurricane Helene'
-isHidden: true
-description: "Locations of confirmed landslides per the USGS that occurred as a result of Hurricane Helene."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - USGS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** September 29, 2024
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Southern Appalachia
- - **Spatial Resolution:** 1 km
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: landslides-inventory
- stacCol: landslides-inventory
- name: USGS Landslides Inventory - Hurricane Helene
- type: raster
- description: "Locations of confirmed landslides per the USGS that occurred as a result of Hurricane Helene."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap: '{"1":[75,128,140,255],"2":[206,210,211,255],"3":[206,210,211,255],"4":[202,34,84,255],"5":[0,173,255,255],"6":[206,210,211,255],"7":[195,139,105,255]}'
- resampling: bilinear
-
- legend:
- type: categorical
- stops:
- - color: "#4B7F8C"
- label: Road
- - color: "#CA2254"
- label: Building
- - color: "#00ADFF"
- label: River
- - color: "#CED2D3"
- label: Other
- - color: "#C38B69"
- label: None
----
diff --git a/datasets/landslides-kd-impactful.data.mdx b/datasets/landslides-kd-impactful.data.mdx
deleted file mode 100644
index d28ec94acd..0000000000
--- a/datasets/landslides-kd-impactful.data.mdx
+++ /dev/null
@@ -1,55 +0,0 @@
----
-id: landslides-kd-impacted-structures
-name: 'Impactful Landslides Kernel Density - Hurricane Helene'
-isHidden: true
-description: "Kernel density analysis of confirmed landslides that per the USGS directly impacted structures, roads, or rivers and occurred as a result of Hurricane Helene."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - USGS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** September 29, 2024
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Southern Appalachia
- - **Spatial Resolution:** 1 km
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: landslides-kd-impacted-structures
- stacCol: landslides-kd-impacted-structures
- name: Impactful Landslides Kernel Density - Hurricane Helene
- type: raster
- description: "Kernel density analysis of confirmed landslides that per the USGS directly impacted structures, roads, or rivers and occurred as a result of Hurricane Helene."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: thermal
- resampling: bilinear
- rescale: 0,10000
- legend:
- type: gradient
- min: "0"
- max: "10000"
- stops:
- - "#000004"
- - "#2c1176"
- - "#4f03a1"
- - "#7e03a8"
- - "#c51b7d"
- - "#f1605d"
- - "#fdc328"
- - "#fcffa4"
----
diff --git a/datasets/landslides-nc-flood.data.mdx b/datasets/landslides-nc-flood.data.mdx
deleted file mode 100644
index 611a963ad9..0000000000
--- a/datasets/landslides-nc-flood.data.mdx
+++ /dev/null
@@ -1,48 +0,0 @@
----
-id: landslides-nc-flood
-name: 'NC DHHS Flood Extent - Hurricane Helene'
-isHidden: true
-description: "Flood extents as determined from Sentinel, NOAA, and USGS datasets and produced by the North Carolina Department of Health and Human Services (NC DHHS). The majority of this raster has flood levels that exceed 100 year FEMA flood extent recurrence levels."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - NC DHHS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** September 29, 2024
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Western North Carolina
- - **Spatial Resolution:** 100 m
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: landslides-nc-flood
- stacCol: landslides-nc-flood
- name: NC DHHS Flood Extent - Hurricane Helene
- type: raster
- description: "Flood extents as determined from Sentinel, NOAA, and USGS datasets and produced by the North Carolina Department of Health and Human Services (NC DHHS). The majority of this raster has flood levels that exceed 100 year FEMA flood extent recurrence levels."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: blues_r
- resampling: bilinear
- rescale: 0.01,5
- nodata: 0
- legend:
- type: categorical
- stops:
- - color: "#4895C7"
- label: Flooded
----
diff --git a/datasets/landslides-ndvi.data.mdx b/datasets/landslides-ndvi.data.mdx
deleted file mode 100644
index d6b5643030..0000000000
--- a/datasets/landslides-ndvi.data.mdx
+++ /dev/null
@@ -1,58 +0,0 @@
----
-id: landslides-ndvi
-name: 'S2 NDVI Difference - Hurricane Helene'
-isHidden: true
-description: "Normalized Difference Vegetation Index (NDVI) differences post minus pre-Helene in the Southern Appalachia region. This index was computed from Sentinel 2 data at 10m spatial resolution."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - USGS
- - Sentinel
-infoDescription: |
- ::markdown
- - **Temporal Extent:** October 12, 2024
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Southern Appalachia
- - **Spatial Resolution:** 10 m
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: landslides-ndvi
- stacCol: landslides-ndvi
- name: S2 NDVI Difference - Hurricane Helene
- type: raster
- description: "Normalized Difference Vegetation Index (NDVI) differences post minus pre-Helene in the Southern Appalachia region. This index was computed from Sentinel 2 data at 10m spatial resolution."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: coolwarm_r
- resampling: bilinear
- rescale: -0.5,0.5
- nodata: 3.4028235e+38
-
- legend:
- type: gradient
- min: "-0.5"
- max: "0.5"
- stops:
- - "#b40426"
- - "#e26952"
- - "#f7a889"
- - "#edd1c2"
- - "#c9d7f0"
- - "#9abbff"
- - "#6788ee"
- - "#3b4cc0"
----
diff --git a/datasets/landslides-planet.data.mdx b/datasets/landslides-planet.data.mdx
deleted file mode 100644
index e1b48ac7db..0000000000
--- a/datasets/landslides-planet.data.mdx
+++ /dev/null
@@ -1,93 +0,0 @@
----
-id: landslides-planet
-name: 'PlanetScope Satellite Imagery - Hurricane Helene'
-description: "TrueColor RGB commercial satellite imagery from Planet Labs of damage in North Carolina and Tennessee from Hurricane Helene's impacts as well as select scenes before and during Helene. This data was made available through the NASA Commercial Satellite Data Acquisition (CSDA) Program."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - Planet
-infoDescription: |
- ::markdown
- - **Temporal Extent:** September 21-October 8, 2024
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Western North Carolina and Eastern Tennessee
- - **Spatial Resolution:** 3 m
- - **Data Units:** Radiance
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: landslides-planet-pre
- stacCol: landslides-planet-pre
- name: PlanetScope Satellite Imagery (NC Hurricane Helene - Pre)
- type: raster
- description: "TrueColor RGB commercial satellite imagery from Planet Labs of portions of North Carolina from before Hurricane Helene's impacts. This data was made available through the NASA Commercial Satellite Data Acquisition (CSDA) Program."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0
- - 255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- - id: landslides-planet-post
- stacCol: landslides-planet-post
- name: PlanetScope Satellite Imagery (NC/TN Hurricane Helene - Post)
- type: raster
- description: "TrueColor RGB commercial satellite imagery from Planet Labs of damage in North Carolina and Tennessee from Hurricane Helene's impacts. This data was made available through the NASA Commercial Satellite Data Acquisition (CSDA) Program."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0
- - 255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- - id: landslides-planet-during
- stacCol: landslides-planet-during
- name: PlanetScope Satellite Imagery (Asheville NC - Hurricane Helene - During)
- type: raster
- description: "TrueColor RGB commercial satellite imagery from Planet Labs of Asheville, North Carolina during massive flodding caused by Hurricane Helene. This data was made available through the NASA Commercial Satellite Data Acquisition (CSDA) Program."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0
- - 255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
-
----
diff --git a/datasets/landslides-risk-usgs.data.mdx b/datasets/landslides-risk-usgs.data.mdx
deleted file mode 100644
index 06d26933d0..0000000000
--- a/datasets/landslides-risk-usgs.data.mdx
+++ /dev/null
@@ -1,57 +0,0 @@
----
-id: landslides-risk-usgs
-name: 'USGS Landslide Hazard Estimate Model - Hurricane Helene'
-isHidden: true
-description: "The two modeling approaches used were adapted from existing models and were run in early October 2024 using initial precipitation estimates to produce rapid event-specific landslide hazard estimate maps. These preliminary maps were initially released provisionally to meet the need for timely best science during the emergency response (version 1.0). The outputs were used to provide early situational awareness of what areas may have been more severely impacted by landslides and debris flows. These results were also used in planning reconnaissance."
-media:
- src: ::file ../stories/media/landslides-helene/landslides-background.png
- alt: Landslide closes I-40 in western North Carolina.
- author:
- name: CNN Newsource
-taxonomy:
- - name: Topics
- values:
- - Disasters
- - Tropical
- - name: Source
- values:
- - USGS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** September 29, 2024
- - **Temporal Resolution:** N/A
- - **Spatial Extent:** Southern Appalachia
- - **Spatial Resolution:** 6 km
- - **Data Units:** N/A
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: landslides-risk-usgs
- stacCol: landslides-risk-usgs
- name: USGS Landslide Hazard Estimate Model - Hurricane Helene
- type: raster
- description: "The two modeling approaches used were adapted from existing models and were run in early October 2024 using initial precipitation estimates to produce rapid event-specific landslide hazard estimate maps. These preliminary maps were initially released provisionally to meet the need for timely best science during the emergency response (version 1.0). The outputs were used to provide early situational awareness of what areas may have been more severely impacted by landslides and debris flows. These results were also used in planning reconnaissance."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: gist_stern
- resampling: bilinear
- rescale: 0.01,1
- nodata: 0
-
- legend:
- type: gradient
- min: "0%"
- max: "100%"
- stops:
- - "#f9f9f9"
- - "#862c45"
- - "#474788"
- - "#6c6ccf"
- - "#9191b5"
- - "#b5b542"
- - "#d9d97e"
- - "#fefefa"
----
diff --git a/datasets/lis-suppression-evap-transpiration.data.mdx b/datasets/lis-suppression-evap-transpiration.data.mdx
deleted file mode 100644
index b434d7cf2a..0000000000
--- a/datasets/lis-suppression-evap-transpiration.data.mdx
+++ /dev/null
@@ -1,196 +0,0 @@
----
-id: lis-etsuppression
-name: "LIS Modeled Suppression"
-description: "Change in Transpiration and Evapotranspiration for 2020 fires using Land Information System outputs"
-media:
- src: ::file media/lis_modeled_et_and_transpiration_suppression.thumbnail.jpg
- alt: Aerial View of East Troublesome wildfire burn scar
- author:
- name: Kent
- url: https://as2.ftcdn.net/v2/jpg/04/56/61/87/1000_F_456618759_78z9x3cJLCb2FKh5DpoFwiI6t1iTLbik.jpg
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Evapotranspiration
- - Water Cycle
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- Change in ET for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model. Change is calculated as the difference of ET in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET.
-layers:
- - id: lis-etsuppression
- stacCol: lis-etsuppression
- name: Evapotranspiration (ET) Anomalies
- type: raster
- description: "ET anomaly for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- asset_bidx: cog_default|1
- colormap_name: rdylbu
- rescale:
- - -0.6
- - 0.6
- compare:
- datasetId: mtbs-burn-severity
- layerId: mtbs-burn-severity
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `ET Anomalies: ${dateFns.format(datetime, 'yyyy')} VS MTBS: ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- legend:
- type: gradient
- label: ET Anomalies
- min: "-0.6"
- max: "0.6"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Western United States
- temporalResolution: Annual
- unit: Percentage Diff
-
- - id: lis-tvegsuppression
- stacCol: lis-tvegsuppression
- name: Transpiration Anomalies
- type: raster
- description: "Standardized transpiration anomalies for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- asset_bidx: cog_default|1
- colormap_name: rdylbu
- rescale:
- - -0.6
- - 0.6
- compare:
- datasetId: mtbs-burn-severity
- layerId: mtbs-burn-severity
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `Transpiration Anomalies: ${dateFns.format(datetime, 'yyyy')} VS MTBS: ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- legend:
- type: gradient
- label: Transpiration Anomalies
- min: "-0.6"
- max: "0.6"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Western United States
- temporalResolution: Annual
- unit: Percent Diff
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2020
- - **Spatial Extent:** Western United States
- - **Spatial Resolution:** 1 km
- - **Data Units:** Standardized anomalies
- - **Data Type:** Research
-
-
-
-
- High resolution observations of ET anomalies from OpenET (DisALEXI model).
-
-
-
-
-
-
-
- ## About
-
- Change in evapotranspiration (ET) and vegetation transpiration for 2020 fires using model outputs from Land Information System (LIS) framework that synthesizes multiple remote sensing observations within the Noah-MP land surface model.
-
-
-
-
-
-
-
- ## Scientific Details
- Change in ET is calculated as the difference of ET in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire ET and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in ET. Change in vegetation transpiration is calculated as the difference of transpiration in the immediate post-fire water year from that in the immediate pre-fire water year. The difference is normalized by pre-fire transpiration and negative values denote vegetation disturbance induced by fire or by a climatological anomaly resulting in the decline in transpiration.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Hydrological Drivers and Impacts of Fire**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
-
-
- ## Additional Resources
-
- * [OpenET Product](https://openetdata.org/)
- * [MTBS - Project overview](https://www.mtbs.gov/project-overview)
- * [Interactive MTBS Viewer for Continental US](https://www.mtbs.gov/viewer/?region=conus)
-
-
\ No newline at end of file
diff --git a/datasets/lis.da.trend.data.mdx b/datasets/lis.da.trend.data.mdx
deleted file mode 100644
index d3434e5940..0000000000
--- a/datasets/lis.da.trend.data.mdx
+++ /dev/null
@@ -1,187 +0,0 @@
----
-id: lis-global-da-trends
-name: 'Global Water Cycle Reanalysis Trends'
-description: "Trend in TWS and GPP modeled using data assimilation within Land Information System framework"
-media:
- src: ::file ./media/twsanomaly-globe.png
- alt: TWS trend of anomalies from LIS outputs.
- author:
- name: NASA LIS
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- Realistic estimates of water and energy cycle variables are necessary for accurate understanding of earth system processes. We develop a 10 km global reanalysis product of water, energy, and carbon fluxes by assimilating satellite observed surface soil moisture, leaf area index, and terrestrial water storage anomalies into a land surface model within NASA Land Information System Framework. We applied a seasonal and trend decomposition algorithm to get the trend estimates for terrestrial water storage and gross primary production. The method can better help to deal with [nonstationarities](https://github.com/Earth-Information-System/sea-level-and-coastal-risk/blob/main/AMS_2023_Wanshu_Nie_for_VEDA_Discovery.pdf) and seasonal shifts and provide a more robust estimate of trends.
-layers:
- - id: lis-global-da-tws-trend
- stacCol: lis-global-da-tws-trend
- name: 'LIS DA TWS Trend'
- type: raster
- description: 'Trends in TWS from LIS data assimilation output'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- bidx: 1
- colormap_name: rdbu
- rescale:
- - -20
- - 20
- nodata: -9999.
- compare:
- datasetId: lis-global-da-trends
- layerId: lis-global-da-gpp-trend
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `TWS trend (2003-2021) VS GPP trend (2003-2021)`;
- }
- legend:
- unit:
- label: (mm/yr)
- type: gradient
- label: TWS Trend (mm/yr)
- min: "-20"
- max: "20"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Annual
- unit: mm/yr
-
- - id: lis-global-da-gpp-trend
- stacCol: lis-global-da-gpp-trend
- name: 'LIS DA GPP Trend'
- type: raster
- description: 'Trends in GPP from LIS data assimilation output'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- bidx: 1
- colormap_name: rdbu
- rescale:
- - -40
- - 40
- nodata: -9999.
- legend:
- unit:
- label: (gC/m2/yr)
- type: gradient
- label: GPP Trend (gC/m2/yr)
- min: "-40"
- max: "40"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Annual
- unit: mm/yr
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2003 - 2021
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 10 km
- - **Data Type:** Research
-
-
-
-
- Theil-Sen Slope estimation of the decomposed trend component of terrestrial water storage (mm/yr) and gross primary production (gC/m2/yr) for 2003-2021 from global reanalysis dataset.
-
-
-
-
-
-
-
- ## About
-
- Realistic estimates of water and energy cycle variables are necessary for accurate understanding of earth system processes. We develop a 10 km global reanalysis product of water, energy, and carbon fluxes by assimilating satellite observed surface soil moisture, leaf area index, and terrestrial water storage anomalies into a land surface model within NASA Land Information System Framework. We applied a seasonal and trend decomposition algorithm to get the trend estimates for terrestrial water storage and gross primary production. The method can better help to deal with [nonstationarities](https://github.com/Earth-Information-System/sea-level-and-coastal-risk/blob/main/AMS_2023_Wanshu_Nie_for_VEDA_Discovery.pdf) and seasonal shifts and provide a more robust estimate of trends.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **A New View of the Global Water Cycle**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional Resources
-
- * [EIS Freshwater](https://freshwater.eis.smce.nasa.gov/)
- * [Land Information System](https://lis.gsfc.nasa.gov/)
- * [Global Reanalysis Dataset page](https://www.earthdata.nasa.gov/dashboard/data-catalog?taxonomy=%7B%22Topics%22%3A%22eis%22%7D)
-
-
-
diff --git a/datasets/mo_npp_vgpm.data.mdx b/datasets/mo_npp_vgpm.data.mdx
deleted file mode 100644
index 5d2d97d4e4..0000000000
--- a/datasets/mo_npp_vgpm.data.mdx
+++ /dev/null
@@ -1,121 +0,0 @@
----
-id: npp
-name: "Ocean Net Primary Production"
-description: "Ocean Net Primary Production (NPP)"
-usage:
- - url: 'https://github.com/NASA-IMPACT/veda-docs/blob/main/notebooks/datasets/ocean-npp-timeseries-analysis.ipynb'
- label: View example notebook
- title: 'Static view in VEDA documentation'
- - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https://github.com/NASA-IMPACT/veda-docs&branch=main&urlpath=lab/tree/veda-docs/notebooks/datasets/ocean-npp-timeseries-analysis.ipynb"
- label: Run example notebook
- title: 'Interactive session in VEDA 2i2c JupyterHub (requires account)'
-media:
- src: ::file ./media/ocean-production--dataset-cover.jpg
- alt: Rocky ocean shore
- author:
- name: Karl Callwood
- url: https://unsplash.com/photos/Ko1sGLhZm5w
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Quality
- - Hydrology
- - name: Source
- values:
- - Oregon State University
-
-infoDescription: |
- ::markdown
- Find information at the [Ocean Productivity website](https://sites.science.oregonstate.edu/ocean.productivity/index.php)
-layers:
- - id: MO_NPP_npp_vgpm
- stacCol: MO_NPP_npp_vgpm
- name: Ocean Net Primary Production
- type: raster
- description: "Ocean Net Primary Production (NPP)"
- zoomExtent:
- - 0
- - 16
- sourceParams:
- colormap_name: jet
- rescale:
- - 0
- - 1500
- legend:
- type: gradient
- min: 0
- max: 1500
- stops:
- - "#000083"
- - "#003caa"
- - "#05ffff"
- - "#ffff00"
- - "#fa0000"
- - "#800000"
- info:
- source: Oregon State University
- spatialExtent: Global
- temporalResolution: Monthly
- unit: Mg C/m²/day
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 1 - December 31, 2020
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 5 km
- - **Data Units:** Mg C/m²/day
- - **Data Type:** Research
-
-
-
-
- Gulf of America net primary productivity during January 2020.
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
- ## License
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
-
-
- ## Additional Resources
- Find information at the [Ocean Productivity website](https://sites.science.oregonstate.edu/ocean.productivity/index.php)
-
-
diff --git a/datasets/modis-aerosol-optical-depth-aod.data.mdx b/datasets/modis-aerosol-optical-depth-aod.data.mdx
deleted file mode 100644
index 4c9253a4b1..0000000000
--- a/datasets/modis-aerosol-optical-depth-aod.data.mdx
+++ /dev/null
@@ -1,280 +0,0 @@
----
-id: modis-aod
-name: "MODIS Aerosol Optical Depth (AOD) (Select Events)"
-description: "Using MODIS MCD19A2 to Analyze Impacts of Aerosols in Urban Areas"
-media:
- src: ::file ./media/smog-city.png
- alt: Smog Located In City.
- author:
- name: Nick van den Berg
- url: https://unsplash.com/photos/2vb-_3t6YCM
-taxonomy:
- - name: Topics
- values:
- - Air Quality
- - name: Subtopics
- values:
- - Urban
- - name: Source
- values:
- - MODIS
-infoDescription: |
- ::markdown
- The MCD19A2 product represents a dataset that offers insights into aerosol optical thickness over land surfaces, grounded in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Originating from both the Terra and Aqua MODIS satellites, this dataset is remarkable for its fusion of information from multiple satellite platforms. Generated daily, the data has a high spatial resolution of 1 km per pixel, allowing detailed observiations.
-layers:
- - id: houston-aod
- stacCol: houston-aod
- name: Mean AOD
- type: raster
- description: "The average Aerosol Optical Depth in our atmosphere. Note that these are unitless values."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylbu_r
- rescale:
- - 0.1
- - 0.311
- nodata: 0
- legend:
- type: gradient
- min: "0"
- max: "0.311"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffbf"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- compare:
- datasetId: houston-aod
- layerId: houston-aod
- info:
- source: NASA
- spatialExtent: Houston, Texas
- temporalResolution: Annual
- unit: Unitless
-
- - id: houston-aod-diff
- stacCol: houston-aod-diff
- name: AOD Difference (2010-2019) - (2000-2009)
- type: raster
- description: "This figure shows the difference in AOD in the form of a raster when subtracting the two decades from the original AOD Dataset"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - -0.1
- - 0.1
- nodata: 0
- compare:
- datasetId: nlcd-annual-conus
- layerId: nlcd-new-urbanization
- mapLabel: |
- ::js ({dateFns, datetime, compareDatetime}) => {
- return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- legend:
- type: gradient
- min: "-0.1"
- max: "0.1"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffff"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- info:
- source: NASA
- spatialExtent: Regional
- temporalResolution: Annual
- unit: Percent Difference
-
- - id: modis-derecho
- stacCol: modis-derecho
- name: MODIS AOD - May 12th, 2022
- type: raster
- description: "Mosaiced MODIS infrared satellite imagery of AOD for May 12th, 2022 derecho."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: ylorbr
- rescale:
- - 0
- - 0.2
- legend:
- type: gradient
- min: 2.5e-05
- max: 0.1855
- stops:
- - "#ffffe5" # Pale Yellow
- - "#feeba2" # Pastel Yellow-Orange
- - "#febb47" # Bright Orange-Yellow
- - "#f07818" # Deep Orange
- - "#b84203" # Dark Burnt Orange
- - "#662506" # Dark Brown
- info:
- source: MODIS
- spatialExtent: Regional
- temporalResolution: Daily
- unit: N/A
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2000 - 2019
- - **Spatial Extent:** Select sites across CONUS
- - **Spatial Resolution:** 1 km
- - **Data Type:** Research
-
-
-
-
- Aerosol Optical Depth Compared Decadally from 2000-2009 & 2010-2019. The map shown shows the change in AOD over the last 20 years over the Houston metropolitan area.
-
-
-
-
-
-
-
-
-
-
-
- ### About
-
- The MCD19A2 data products offer insights into aerosol optical (AOD) thickness over land surfaces, grounded in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Originating from both the Terra and Aqua MODIS satellites, this dataset is remarkable for its fusion of information from multiple satellite platforms. Generated daily, the data has a high spatial resolution of 1 km per pixel, allowing detailed observiations.
-
- The primary purpose of the MCD19A2 product is to provide a comprehensive set of atmospheric and geometric properties or parameters. These parameters are integral in producing the land surface Bidirectional Reflectance Factor, another important component derived usiung the MAIAC algorithm.
-
- ### Grid500m Group:
-
- This segment captures details primarily about aerosol concentrations and characteristics at a 500m resolution. It encompasses:
- * Aerosol Optical Depth (AOD) at 047 micron and 055 micron, which measures the degree to which aerosol particles prevent the transmission of light, giving an insight into air quality.
-
- * Uncertainty metrics for AOD at 047 micron to gauge the precision of measurements.
-
- * Fine-Mode Fraction for Ocean, indicating the proportion of small particles in aerosols over the ocean.
-
- * The Column Water Vapor in cm liquid water, offering details about atmospheric moisture.
-
- * AOD QA provides quality assurance metrics.
-
- * AOD Model shows the regional background model applied.
-
- * Injection Height provides data on the elevation of smoke introduction over the local surface height.
-
- ### Grid5km Group:
-
- This focuses on geometric and solar parameters at a 5km resolution.
-
- * Cosine of Solar Zenith Angle and View Zenith Angle, which provide information on the solar and observational angles respectively, crucial for understanding light reflection and absorption dynamics.
-
- * The Relative Azimuth Angle gives the position between the sun and the observing satellite.
-
- * The Relative Azimuth Angle gives the position between the sun and the observing satellite.
-
-
-
-
-
-
-
- ## Accessing the Data
- Visit the NASA [LAADS DAAC](https://ladsweb.modaps.eosdis.nasa.gov/search/) to explore options for data access.
-
- Alternatively, Google Earth Engine (GEE) provides an efficient way to harness the capabilities of the MODIS MCD19A2 dataset.
-
- 1. Initialize Google Earth Engine: Before you can access any datasets on GEE, ensure you've signed up for a Google Earth Engine account and initialized the GEE API in your programming environment.
-
- 2. Search for the Dataset: Navigate to the Google Earth Engine Data Catalog. In the search bar, type "MCD19A2" to locate the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset.
-
- 4. Scripting in GEE Code Editor: Open the GEE Code Editor and use the Correct Dataset ID. In this case, use "MODIS/006/MCD19A2/Optical_Depth_047"
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- - **Aerosols and Their Impacts on Houston, TX**
- - **NASA Data Fusion Analysis of Derechos and Their Impact on Rural America**
-
-
-
-
-
-
-
-
- ### Citing the Data
-
- Alexi Lyapustin - NASA GSFC, Yujie Wang - Univeristy of Maryland Baltimore County and MODAPS SIPS - NASA. (2015). MCD19A2 MODIS/Terra+Aqua Aerosol Optical Thickness Daily L2G Global 1km SIN Grid. NASA LP DAAC. http://doi.org/10.5067/MODIS/MCD19A2.006
-
-
-
-
-
-
-
-
-
- ### Key Publications
-
- Remer, L. A., and Coauthors, 2005: The MODIS Aerosol Algorithm, Products, and Validation. J. Atmos. Sci., 62, 947–973, https://doi.org/10.1175/JAS3385.1.
-
- Wei,J., Li, Z., Peng, Y., and Sun, L. (2019) MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison. Atmospheric Environment (201) 428-440. https://doi.org/10.1016/j.atmosenv.2018.12.004
-
- Mehta, M., Singh, R., Singh, A., Singh, N., and Anshumali. (2016) Recent global aerosol optical depth variations and trends — A comparative study using MODIS and MISR level 3 datasets. Remote Sensing of Environment (181) 137-150. https://doi.org/10.1016/j.rse.2016.04.004
-
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
diff --git a/datasets/modis-ndvi.data.mdx b/datasets/modis-ndvi.data.mdx
deleted file mode 100644
index 7f712a8fd0..0000000000
--- a/datasets/modis-ndvi.data.mdx
+++ /dev/null
@@ -1,90 +0,0 @@
----
-id: MODIS_Terra_L3_NDVI_16Day
-name: MODIS Terra 16-Day NDVI
-featured: true
-description: The Vegetation Index (L3, 16-Day) layer is created from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) data which are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product.
-media:
- src: ::file ./media/modis-ndvi.jpg
- alt: MODIS NDVI over the Eastern Corn Belt of the Midwestern USA.
- author:
- name: Andrew Blackford
-infoDescription: |
- ::markdown
- - Temporal Extent: 2000 Mar 5 - Present
- - Temporal Resolution: Daily
-layers:
- - id: MODIS_Terra_L3_NDVI_16Day
- stacCol: MODIS_Terra_L3_NDVI_16Day
- name: 16-Day MODIS Terra NDVI (L3) (from Worldview/GIBS)
- type: wmts
- description: The Vegetation Index (L3, 16-Day) layer is created from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) data which are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product.
- sourceParams:
- layers: 1
- version: 1.3.0
- crs: EPSG:3857
- styles:
- zoomExtent:
- - 0
- - 5
- analysis:
- exclude: true
- legend:
- type: gradient
- min: 0
- max: 1
- stops:
- - rgb(215, 210, 210)
- - rgb(187, 175, 159)
- - rgb(151, 129, 108)
- - rgb(144, 168, 63)
- - rgb(87, 133, 41)
- - rgb(46, 96, 28)
- - rgb(10, 26, 8)
- compare:
- datasetId: MODIS_Terra_L3_NDVI_16Day
- layerId: MODIS_Terra_L3_NDVI_16Day
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2000-Present
- - **Temporal Resolution:** Every 16 Days
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 250 m
- - **Data Type:** Research
-
-
-
-
- Example of MODIS Terra NDVI showing the 2011 Super Outbreak tornado paths in Alabama.
-
-
-
-
-
-
-
-
-## About MODIS NDVI
-
-The Vegetation Index (L3, 16-Day) layer is created from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) data which are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The algorithm chooses the best available pixel value from all the acquisitions from the 16-day period. The criteria used is low clouds, low view angle, and the highest NDVI value. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. The MODIS NDVI product is computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols.
-
-References: MOD13Q1 doi:10.5067/MODIS/MOD13Q1.061
-
-
-
-
diff --git a/datasets/mtbs-burn-severity.data.mdx b/datasets/mtbs-burn-severity.data.mdx
deleted file mode 100644
index 14853924aa..0000000000
--- a/datasets/mtbs-burn-severity.data.mdx
+++ /dev/null
@@ -1,150 +0,0 @@
----
-id: mtbs-burn-severity
-name: "MTBS Burn Severity"
-description: "Burn Severities and extents of fires"
-media:
- src: ::file media/mtbs_burn_severity.thumbnail.jpg
- alt: Forest Fire Scarred land
- author:
- name: Allison
- url: https://as2.ftcdn.net/v2/jpg/12/19/62/11/1000_F_1219621191_zftRvVT9W4qPSLXjLkeXlqDmTsb9DvSQ.jpg
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- MTBS is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico.
-layers:
- - id: mtbs-burn-severity
- stacCol: mtbs-burn-severity
- name: MTBS Burn Severity
- type: raster
- description: "Burn severities and extents of fires from Monitoring Trends in Burn Severity (MTBS) program during the years 2016-2021 over Western US"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylgn_r
- rescale:
- - 1
- - 4
- compare:
- datasetId: mtbs-burn-severity
- layerId: mtbs-burn-severity
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- legend:
- type: categorical
- stops:
- - color: "#94c772"
- label: "Unburned"
- - color: "#faf88e"
- label: "Low"
- - color: "#ea915e"
- label: "Moderate"
- - color: "#971d2b"
- label: "High"
- info:
- source: Interagency
- spatialExtent: Contiguous United States
- temporalResolution: Annual
- unit: Categorical
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2016 - 2020
- - **Spatial Extent:** Western United States
- - **Spatial Resolution:** ~ 100 meters
- - **Data Type:** Research
-
-
-
-
- Monitoring Trends in Burn Severity (MTBS) impacted areas during the year 2020 around Salem, Oregon.
-
-
-
-
-
-
-
-
-
- ## About
-
- Burn severities and extents of fires from Monitoring Trends in Burn Severity (MTBS) program during the years 2016-2020 over Western US.
-
- MTBS is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico.
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Hydrological Drivers and Impacts of Fire**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional Resources
-
- * [MTBS - Project overview](https://www.mtbs.gov/project-overview)
- * [Interactive Viewer for Continental US](https://www.mtbs.gov/viewer/?region=conus)
-
-
\ No newline at end of file
diff --git a/datasets/nceo_africa_2017.data.mdx b/datasets/nceo_africa_2017.data.mdx
deleted file mode 100644
index b9114b5571..0000000000
--- a/datasets/nceo_africa_2017.data.mdx
+++ /dev/null
@@ -1,125 +0,0 @@
----
-id: nceo_africa_2017
-name: "National Centre for Earth Observation (NCEO) Biomass"
-description: The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution
-usage:
- - url: "https://github.com/NASA-IMPACT/veda-docs/blob/main/notebooks/datasets/nceo-biomass-statistics.ipynb"
- label: View example notebook
- title: 'Static view in VEDA documentation'
- - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https://github.com/NASA-IMPACT/veda-docs&branch=main&urlpath=lab/tree/veda-docs/notebooks/datasets/nceo-biomass-statistics.ipynb"
- label: Run example notebook
- title: 'Interactive session in VEDA 2i2c JupyterHub (requires account)'
-media:
- src: ::file ./media/nceo-africa--dataset-cover.jpg
- alt: Green trees seen from above
- author:
- name: Olena Sergienko
- url: https://unsplash.com/photos/0Ws_-v4Y_wY
-taxonomy:
- - name: Topics
- values:
- - Biodiversity
- - name: Subtopics
- values:
- - Drought
- - Habitat
- - Land Use
-infoDescription: |
- ::markdown
- The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK’s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes. For more information see [CEOS biomass](https://ceos.org/gst/biomass.html).
-layers:
- - id: nceo_africa_2017
- stacCol: nceo_africa_2017
- name: NCEO Africa Aboveground Woody Biomass
- type: raster
- description: The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution
- zoomExtent:
- - 0
- - 16
- sourceParams:
- colormap_name: gist_earth_r
- rescale:
- - 0
- - 400
- legend:
- type: gradient
- min: Less
- max: More
- stops:
- - '#ffffff'
- - '#e5c7bb'
- - '#ceab84'
- - '#b5b65d'
- - '#86a954'
- - '#43974c'
- - '#30817d'
- - '#1f567b'
- - '#080e74'
- - '#000000'
- info:
- source: UK
- spatialExtent: Africa
- temporalResolution: Annual
- unit: N/A
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2017
- - **Spatial Extent:** Africa
- - **Spatial Resolution:** 100 meters
- - **Data Type:** Research
-
-
-
-
- Biomass amounts located near the Democratic Republic of the Congo in Africa.
-
-
-
-
-
-
-
- ## About
-
- The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK’s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes. For more information see [CEOS biomass](https://ceos.org/gst/biomass.html).
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
\ No newline at end of file
diff --git a/datasets/nlcd.data.mdx b/datasets/nlcd.data.mdx
deleted file mode 100644
index 76d2461b1d..0000000000
--- a/datasets/nlcd.data.mdx
+++ /dev/null
@@ -1,292 +0,0 @@
----
-id: nlcd-annual-conus
-name: 'National Land Cover Database LULC Classifications'
-description: "National Land Cover Database Land Use - Land Cover classifications for CONUS, 2001-2023 at 30 m resolution."
-
-media:
- src: ::file media/nlcd_land_cover_identification_2016_2019.thumbnail.jpg
- alt: Patchwork landscape with white clouds covering a little village.
- author:
- name: Alicia
- url: https://as1.ftcdn.net/v2/jpg/08/52/54/78/1000_F_852547838_eKPOpUg5gpBUWBJi900AGOPHWwzMPuIH.jpg
-taxonomy:
- - name: Topics
- values:
- - Agriculture
- - Biodiversity
- - name: Subtopics
- values:
- - Habitat
- - Land use
- - name: Source
- values:
- - MRLC
-layers:
- - id: nlcd-annual-conus
- stacApiEndpoint: https://dev.ghg.center/api/stac/
- tileApiEndpoint: https://dev.ghg.center/api/raster
- stacCol: nlcd-annual-conus-v2
- name: NLCD Land Use - Land Cover Classification
- type: wms
- description: "30 meter LULC classification provided by the NLCD."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- layers: mrlc_Land-Cover_conus_year_data:Land-Cover_conus_year_data
- version: 1.3.0
- srs: EPSG:3857
- legend:
- type: categorical
- min: "0"
- max: "255"
- stops:
- - color: "#486DA2"
- label: "Open Water"
- - color: "#E7EFFC"
- label: "Perennial Ice/Snow"
- - color: "#E1CDCE"
- label: "Developed, Open Space"
- - color: "#DC9881"
- label: "Developed, Low Intensity"
- - color: "#F10100"
- label: "Developed, Medium Intensity"
- - color: "#AB0101"
- label: "Developed High Intensity"
- - color: "#B3AFA4"
- label: "Barren Land (Rock/Sand/Clay)"
- - color: "#6BA966"
- # label: "Vegetation"
- label: "Deciduous Forest"
- - color: "#1D6533"
- label: "Evergreen Forest"
- - color: "#BDCC93"
- label: "Mixed Forest"
- - color: "#B29C46"
- label: "Dwarf Scrub"
- - color: "#D1BB82"
- label: "Shrub/Scrub"
- - color: "#EDECCD"
- label: "Grassland/Herbaceous"
- - color: "#D0D181"
- label: "Sedge/Herbaceous"
- - color: "#A4CC51"
- label: "Lichens"
- - color: "#82BA9D"
- label: "Moss"
- - color: "#DDD83E"
- label: "Pasture/Hay"
- - color: "#AE7229"
- label: "Cultivated Crops"
- # label: "Agriculture"
- - color: "#BBD7ED"
- label: "Woody Wetlands"
- - color: "#71A4C1"
- label: "Emergent Herbaceous Wetlands"
- compare:
- datasetId: nlcd-annual-conus
- layerId: nlcd-annual-conus
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- info:
- source: MRLC
- spatialExtent: United States
- temporalResolution: Annual
- unit: N/A
-
- - id: nlcd-new-urbanization
- stacCol: nlcd-new-urbanization
- name: Urbanization
- type: raster
- description: "This is a binary dataset derived from the National Land Cover Database (NLCD) to illustrate new urbanization from 2001-2021, where 0 is no new urbanization and 1 is new urbanization."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: reds
- nodata: 0
- assets: landcover
- rescale:
- - 0
- - 1
- legend:
- type: categorical
- stops:
- - color: "#d73027"
- label: New Urbanization
- info:
- source: EROS
- spatialExtent: CONUS
- temporalResolution: 20 Year Difference
- unit: Binary
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** 2001-2023
- - **Temporal Resolution:** Annual
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 30 m
- - **Data Type:** Research
-
-
-
-
- Comparison of the NLCD land cover classifications over Houston, TX between 2001 and 2021
-
-
-
-
-
-
-
- ### About
-
- The National Land Cover Database (NLCD) stands as a paramount dataset offering an in-depth overview of the land cover characteristics in the United States. Spearheaded by the Earth Resources Observation and Science (EROS) Center, this database is renewed every year to provide updated and accurate data for the nation.
-
- This is a collective effort between the U.S. Geological Survey (USGS) and the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC, composed of various federal agencies, has a rich legacy spanning over 30 years of generating consistent and pertinent land cover information on a national scale. The NLCD is a testament to their dedication and has emerged as one of the most frequently utilized geospatial datasets within the U.S., catering to an extensive audience ranging from scientists, land managers, city planners, to students.
-
- As of its latest release, the NLCD showcases land cover data and related changes starting from 2001 and culminating in 2023. These datasets are meticulously crafted, ensuring continuity and consistency with the past releases. This methodological consistency ensures that the datasets from the different epochs are directly comparable and well-suited for mult-temporal analyses.
-
-
-
-
-
-
-
-
- Newly urbanized areas per NLCD classifications in the Indianapolis, IN metropolitan area between 2001 and 2021.
-
-
-
-
-
- ### What NLCD Offers
-
- * Land Cover: This product details the land cover of the Conterminous U.S. at a 30-meter spatial resolution, employing a 16-class legend rooted in the modified Anderson Level II classification system.
-
- * Land Cover Change Index: This visualization tool portrays the transformations that have transpired across all the NLCD epochs, furnishing users with a holistic view of the evolving landscape.
-
- * Urban Imperviousness: A crucial dataset for urbanization studies, it highlights impervious surfaces in urban regions, showcasing them as a percentage of the developed surface at every 30-meter pixel.
-
- * Urban Impervious Descriptor: A more nuanced product that classifies specific urban developments, such as roads, wind tower sites, building locations, and energy production sites. This aids in a more granular analysis of urban features.
-
-
-
-
-
-
-
- ### Access the Data
-
- Visit the [Access Data](https://www.mrlc.gov/data) page to explore all of the options that NLCD offers.
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- U.S. Geological Survey (USGS) & Multi-Resolution Land Characteristics (MRLC) Consortium. (2021). National Land Cover Database (NLCD) 2021: Conterminous U.S. Land Cover. Earth Resources Observation and Science (EROS) Center. Retrieved from https://www.mrlc.gov/data
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., et al. (2007). Completion of the 2001 National Land Cover Database for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 73(4), 337–341.
-
- Homer, C., Fry, J. A., & Barnes, C. A. (2012). The national land cover database. US geological survey fact sheet, 3020(4), 1–4.
-
- Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., et al. (2015). Completion of the 2011 national land cover database for the conterminous United States – Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, 81(5), 345–354. https://doi.org/10.14358/PERS.81.5.345
-
-
-
-
-
-
-
- ### Other Publications
-
- Danielson, Patrick, Postma, Kory, Riegle, J., Dewitz, Jon A., Deep learning artificial intelligence (AI) for improving classification accuracy for the National Land Cover Database (NLCD) [abs.]
-
- Jin, Suming, Dewitz, Jon A., Sorenson, D., Shogib, Rakibul , Granneman, Brian J., Case, Adam, Li, Congcong, Zhe, Z., Danielson, Patrick, Costello, C., Gass, L., National Land Cover Database 2019—A comprehensive strategy for creating the 1986-2019 Forest Disturbance Date Product [abs.], v. Proceedings, at https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/960755
-
- Rigge, Matthew B., Homer, Collin G., Shi, Hua, Meyer, Debbie K., Bunde, Brett, Granneman, Brian, Postma, Kory, Danielson, Patrick, Case, Adam, Xian, George Z., Rangeland fractional components across the western United States from 1985 to 2018: Remote Sensing, v. 13, no. 4, at https://doi.org/10.3390/rs13040813
-
- Wickham, J., Stehman, S.V., Sorenson, D.G., Gass, L., Dewitz, Jon A., Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States: Remote Sensing of Environment, v. 257, at https://doi.org/10.1016/j.rse.2021.112357
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Implications for Heat Stress**
-
- **Aerosols and Their Impacts on Houston, TX**
-
- **Wildfires Affect Local Weather, Climate, and Hydrology**
-
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
diff --git a/datasets/nldas2.data.mdx b/datasets/nldas2.data.mdx
deleted file mode 100644
index ace98cca92..0000000000
--- a/datasets/nldas2.data.mdx
+++ /dev/null
@@ -1,130 +0,0 @@
----
-id: nldas2
-name: "NLDAS-2 Precipitation Forcing Dataset"
-description: "NLDAS-2 is a surface meteorological analysis and land-surface model dataset running in operations to produce outputs of soil moisture, snow, surface fluxes, streamflow, etc. for drought monitoring and other applications."
-media:
- src: ::file media/nldas_2_3_precipitation_forcing_dataset.thumbnail.jpg
- alt: Storm clouds moving over a grassland ecosystem
- author:
- name: chaiyadid
- url: https://as1.ftcdn.net/v2/jpg/10/07/76/04/1000_F_1007760486_BRA8NREAdma0FDGANopzbHI5kSZx3WxO.jpg
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Snow
- - Precipitation
- - Water Cycle
- - Hydrology
-infoDescription: |
- ::markdown
- NLDAS-2 is a surface meteorological analysis and land-surface model dataset running in operations to produce outputs of soil moisture, snow, surface fluxes, streamflow, etc. for drought monitoring and other applications.
-layers:
- - id: nldas2
- stacCol: nldas2
- name: Precipitation
- type: raster
- description: "Precipitation Dataset"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: magma
- rescale:
- - 0
- - 200
- nodata: 0
- compare:
- datasetId: nldas2
- layerId: nldas2
- mapLabel: |
- ::js ({dateFns, datetime, compareDatetime}) => {
- return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- legend:
- unit:
- label: mm/month
- type: gradient
- min: "0"
- max: "200"
- stops:
- - "#000004"
- - "#3B0F70"
- - "#8C2981"
- - "#F37C21"
- - "#FCFFA4"
- - "#fc8d59"
- info:
- spatialExtent: CONUS
- temporalResolution: Monthly
- unit: mm/month
----
-
-
- **Temporal Extent:** Jan 2003 to Dec 2021 (January 1979 to present available from the NASA GES DISC)
- **Temporal Resolution:** Monthly-averaged (Hourly data available from the NASA GES DISC)
- **Spatial Extent:** CONUS (25 to 53 North and -125 to -67 West)
- **Spatial Resolution:** 0.125° x 0.125°
- **Data type:** Research
-
- The precipitation from NLDAS-2 over CONUS is derived from a daily gridded precipitation analysis at 0.125-degrees from NOAA CPC. The data is temporally disaggregated to hourly using a variety of data sources, primarily radar-estimated precipitation amounts from Doppler Stage II data. Outside of CONUS, but still in the 25 to 53 North domain, different datasets are used to generate NLDAS-2 precipitation. See https://ldas.gsfc.nasa.gov/nldas/v2/forcing for details.
-
- ## Source Data Product Citation
- - https://doi.org/10.5067/THUF4J1RLSYG NLDAS Primary Forcing Data L4 Monthly 0.125 x 0.125 degree V2.0, Edited by David M. Mocko, NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed:[Data Access Date], 10.5067/2DPKB5B5N14O
-
- ## Version History
- V2.0
-
-
-
-
-
-
- ## Key Publications
- Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., Mocko, D., 2012. Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products: WATER AND ENERGY FLUX ANALYSIS. Journal of Geophysical Research: Atmospheres. Vol. 117, No. D3. https://doi.org/10.1029/2011JD016048
-
- ## Learn More
- - https://ldas.gsfc.nasa.gov/nldas/v2/forcing
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Mapping Water Availability over North America**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0)
-
-
diff --git a/datasets/nldas3.data.mdx b/datasets/nldas3.data.mdx
deleted file mode 100644
index 1720fc9348..0000000000
--- a/datasets/nldas3.data.mdx
+++ /dev/null
@@ -1,142 +0,0 @@
----
-id: nldas3
-name: "NLDAS-3 Precipitation Forcing Dataset"
-description: "NASA is co-developing high-resolution retrospective and real-time data for water resources and agricultural applications"
-media:
- src: ::file media/nldas_2_3_precipitation_forcing_dataset.thumbnail.jpg
- alt: Storm clouds moving over a grassland ecosystem
- author:
- name: chaiyadid
- url: https://as1.ftcdn.net/v2/jpg/10/07/76/04/1000_F_1007760486_BRA8NREAdma0FDGANopzbHI5kSZx3WxO.jpg
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Snow
- - Precipitation
- - Water Cycle
- - Hydrology
-infoDescription: |
- ::markdown
- NASA is co-developing high-resolution retrospective and real-time data for water resources and agricultural applications.
-layers:
- - id: nldas3
- stacCol: nldas3
- name: Precipitation
- type: raster
- description: "Precipitation Dataset"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: magma
- rescale:
- - 0
- - 200
- nodata: 0
- compare:
- datasetId: nldas2
- layerId: nldas2
- mapLabel: |
- ::js ({dateFns, datetime, compareDatetime}) => {
- return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- legend:
- unit:
- label: mm/month
- type: gradient
- min: "0"
- max: "200"
- stops:
- - "#000004"
- - "#3B0F70"
- - "#8C2981"
- - "#F37C21"
- - "#FCFFA4"
- - "#fc8d59"
- info:
- spatialExtent: North and Central America
- temporalResolution: Monthly
- unit: mm/month
----
-
-
- **Temporal Extent of sample NLDAS-3 data:** Jan 2001 to Dec 2021
- **Temporal Resolution of sample NLDAS-3 data:** Monthly
- **Spatial Extent:** North and Central America (7-72 North, 169-52 West)
- **Spatial Resolution:** 0.01° x 0.01°
- **Data type:** Research
-
- This sample dataset presents a fine scale precipitation analysis from the development of NLDAS-3. NLDAS-3 is currently under development and the final product will include precipitation fields (included here) as well as other surface meteorology such as temperature, radiation, surface pressure, and wind speed. NLDAS-3 represents the next generation (i.e., phase 3) of the North American Land Data Assimilation System phase 2 (NLDAS-2). NLDAS-3 precipitation has been derived by assimilating daily precipitation amounts at 4 km of the NASA Integrated Multi-SatellitE Retrievals for Global Precipitation Measurements GPM (IMERG) and the Environment and Climate Change Canada (ECCC) Canadian Precipitation Analysis (CaPA) into the NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). NLDAS-3 precipitation at 4 km and daily time steps was then downscaled to 1 km using a cloud-cover based algorithm based on datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) then converted to hourly using a temporal disaggregation which employs MERRA-2 and IMERG datasets. NLDAS-3 covers North and Central America (from latitude 7 to 72 North and longitude 169 to 52 West) at a resolution of 0.01. The sample data available from this page are monthly-averages and an initial version of the still-in-development of the NLDAS-3 precipitation.
-
- ## Source Data Product Citation
- Maina et al., (2024), NLDAS-3 surface meteorology 0.01 degree x 0.01 degree V1, Greenbelt, MD, USA, NASA Center for Climate Simulation (NCCS) DataPortal, Accessed: [Data Access Date]
-
- ## Version History
- V2.0
-
-
-
-
-
- ## Key Publications
- Maina et al., (2024), NLDAS-3 surface meteorology 0.01 degree x 0.01 degree V1, Greenbelt, MD, USA, NASA Center for Climate Simulation (NCCS) DataPortal, Accessed: July 24, 2024.
-
- Bratseth. (1986). Statistical interpolation by means of successive corrections - BRATSETH - 1986 - Tellus A - Wiley Online Library. Retrieved October 12, 2022, from https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0870.1986.tb00476.x
-
- Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., et al. (2003). Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. Journal of Geophysical Research: Atmospheres, 108(D22). https://doi.org/10.1029/2002JD003118
-
- Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., et al. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1
-
- Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
-
- Huffman, G. J., Bolvin, D. T., & Nelkin, E. J. (2015). Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation. NASA/GSFC Code, 612(2015), 47.
-
- Kemp, E. M., Wegiel, J. W., Kumar, S. V., Geiger, J. V., Mocko, D. M., Jacob, J. P., & Peters-Lidard, C. D. (2022). A NASA–Air Force Precipitation Analysis for Near-Real-Time Operations. Journal of Hydrometeorology, 23(6), 965–989. https://doi.org/10.1175/JHM-D-21-0228.1
-
- Lespinas, F., Fortin, V., Roy, G., Rasmussen, P., & Stadnyk, T. (2015). Performance Evaluation of the Canadian Precipitation Analysis (CaPA). Journal of Hydrometeorology, 16(5), 2045–2064. https://doi.org/10.1175/JHM-D-14-0191.1
-
- Maina et al., (2024), NLDAS-3 surface meteorology 0.01 degree x 0.01 degree V1, Greenbelt, MD, USA, NASA Center for Climate Simulation (NCCS) DataPortal, Accessed: July 24, 2024.
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Mapping Water Availability over North America**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0)
-
-
\ No newline at end of file
diff --git a/datasets/no2.data.mdx b/datasets/no2.data.mdx
deleted file mode 100644
index 5e1a2ae538..0000000000
--- a/datasets/no2.data.mdx
+++ /dev/null
@@ -1,271 +0,0 @@
----
-id: no2
-name: 'Nitrogen Dioxide'
-featured: true
-description: "Since the outbreak of the novel coronavirus, atmospheric concentrations of nitrogen dioxide have changed by as much as 60% in some regions."
-usage:
- - url: 'https://github.com/NASA-IMPACT/veda-docs/blob/main/notebooks/quickstarts/no2-map-plot.ipynb'
- label: View example notebook
- title: 'Static view in VEDA documentation'
- - url: "https://nasa-veda.2i2c.cloud/hub/user-redirect/git-pull?repo=https://github.com/NASA-IMPACT/veda-docs&branch=main&urlpath=lab/tree/veda-docs/notebooks/quickstarts/no2-map-plot.ipynb"
- label: Run example notebook
- title: 'Interactive session in VEDA 2i2c JupyterHub (requires account)'
-media:
- src: ::file ./media/no2--dataset-cover.jpg
- alt: Power plant shooting steam at the sky.
- author:
- name: Mick Truyts
- url: https://unsplash.com/photos/x6WQeNYJC1w
-taxonomy:
- - name: Topics
- values:
- - Air Quality
- - Greenhouse Gases
- - name: Subtopics
- values:
- - COVID-19
-infoDescription: |
- ::markdown
- OMI, which launched in 2004, preceded TROPOMI, which launched in 2017. While TROPOMI provides higher resolution information, the longer OMI data record provides context for the TROPOMI observations.
-layers:
- - id: no2-monthly
- stacCol: no2-monthly
- name: Nitrogen Dioxide (monthly)
- type: raster
- description: 'Global nitrogen dioxide (NO₂) data organized into monthly metrics'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- bidx: 1
- color_formula: gamma r 1.05
- colormap_name: rdbu_r
- rescale:
- - 0
- - 1.5e16
- compare:
- datasetId: no2
- layerId: no2-monthly
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- legend:
- type: gradient
- min: "Less"
- max: "More"
- stops:
- - "#3A88BD"
- - "#C9E0ED"
- - "#E4EEF3"
- - "#FDDCC9"
- - "#DD7059"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Monthly
- unit: N/A
-
- - id: no2-monthly-diff
- stacCol: no2-monthly-diff
- name: Nitrogen Dioxide (monthly difference)
- type: raster
- description: 'Global nitrogen dioxide (NO₂) data which displays the difference from the same time 1 month ago'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: rdbu_r
- rescale:
- - -3e15
- - 3e15
- compare:
- datasetId: no2
- layerId: no2-monthly-diff
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
- legend:
- type: gradient
- min: "< -3"
- max: "> 3"
- stops:
- - "#3A88BD"
- - "#C9E0ED"
- - "#E4EEF3"
- - "#FDDCC9"
- - "#DD7059"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Monthly
- unit: N/A
-
- - id: OMI_trno2-COG
- stacCol: OMI_trno2-COG
- name: Nitrogen Dioxide Total and Tropospheric Column (NASA OMI/Aura)
- type: raster
- description: "NASA OMI/Aura Nitrogen Dioxide (NO₂) Total and Tropospheric Column"
- zoomExtent:
- - 0
- - 16
- sourceParams:
- colormap_name: reds
- rescale:
- - 0
- - 30E14
- legend:
- unit:
- label: mol/cm2
- type: gradient
- min: 0
- max: 30e14
- stops:
- - '#ffffff'
- - '#fdd1bf'
- - '#e02d26'
- - '#67000c'
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Monthly
- unit: N/A
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** January 2016 - September 2023
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 10 km
- - **Data Type:** Research
-
-
-
-
-
-
-
-
-
-
- ## About
- Nitrogen dioxide (NO2) is a common air pollutant primarily emitted from the burning of fossil fuels in cars and power plants. Lower to the ground, nitrogen dioxide can directly irritate the lungs and contributes to the production of particulate pollution and smog when it reacts with sunlight.
-
- During the COVID-19 pandemic, scientists have observed considerable decreases in nitrogen dioxide levels around the world. These decreases are predominantly associated with changing human behavior in response to the spread of COVID-19. As communities worldwide have implemented lockdown restrictions in an attempt to stem the spread of the virus, the reduction in human transportation activity has resulted in less NO2 being emitted into the atmosphere.
-
- These changes are particularly apparent over large urban areas and economic corridors, which typically have high levels of automobile traffic, airline flights, and other related activity.
-
- NASA has observed subsequent rebounds in nitrogen dioxide levels as the lockdown restrictions ease.
-
-
-
-
-
-## Scientific research
-[Ongoing research](https://airquality.gsfc.nasa.gov/) by scientists in the Atmospheric Chemistry and Dynamics Laboratory at NASA’s Goddard Space Flight Center and [new research](https://science.nasa.gov/earth-science/rrnes-awards) funded by NASA's Rapid Response and Novel research in the Earth Sciences (RRNES) program element seek to better understand the atmospheric effects of the COVID-19 shutdowns.
-
-For nitrogen dioxide levels related to COVID-19, NASA uses data collected by the joint NASA-Royal Netherlands Meteorological Institute (KNMI) [Ozone Monitoring Instrument (OMI)](https://aura.gsfc.nasa.gov/omi.html) aboard the Aura satellite, as well as data collected by the Tropospheric Monitoring Instrument (TROPOMI) aboard the European Commission’s Copernicus Sentinel-5P satellite, built by the European Space Agency.
-
-OMI, which launched in 2004, preceded TROPOMI, which launched in 2017. While TROPOMI provides higher resolution information, the longer OMI data record provides context for the TROPOMI observations.
-
-Scientists will use these data to investigate how travel bans and lockdown orders related to the novel coronavirus are impacting regional air quality and chemistry, as well as why these restrictions may be having inconsistent effects on air quality around the world.
-
-
-
-
-
-
-
- NO2 levels over South America from the Ozone Monitoring Instrument. The dark green areas in the northwest indicate areas of no data, most likely associated with cloud cover or snow.
-
-
-
-## Interpreting the data
-Nitrogen dioxide has a relatively short lifespan in the atmosphere. Once it is emitted, it lasts only a few hours before it dissipates, so it does not travel far from its source.
-
-Because nitrogen dioxide is primarily emitted from burning fossil fuels, changes in its atmospheric concentration can be related to changes in human activity, if the data are properly processed and interpreted.
-
-Interpreting satellite NO2 data must be done carefully, as the quantity observed by satellite is not exactly the same as the abundance at ground level, and natural variations in weather (e.g., temperature, wind speed, solar intensity) influence the amount of NO2 in the atmosphere. In addition, the OMI and TROPOMI instruments cannot observe the NO2 abundance underneath clouds. For more information on processing and cautions related to interpreting this data, please click [here](https://airquality.gsfc.nasa.gov/caution-interpretation).
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Air Quality and COVID-19**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
-## Additional resources
-### NASA Features
-* [Airborne Nitrogen Dioxide Plummets Over China](https://earthobservatory.nasa.gov/images/146362/airborne-nitrogen-dioxide-plummets-over-china)
-* [Airborne Nitrogen Dioxide Decreases Over Italy](https://earthobservatory.nasa.gov/blogs/earthmatters/2020/03/13/airborne-nitrogen-dioxide-decreases-over-italy/)
-* [NASA Satellite Data Show 30 Percent Drop In Air Pollution Over Northeast U.S.](https://www.nasa.gov/feature/goddard/2020/drop-in-air-pollution-over-northeast)
-* [Airborne Particle Levels Plummet in Northern India](https://earthobservatory.nasa.gov/images/146596/airborne-particle-levels-plummet-in-northern-india)
-* [NASA Satellite Data Show Air Pollution Decreases over Southwest U.S. Cities](https://www.nasa.gov/feature/goddard/2020/nasa-satellite-data-show-air-pollution-decreases-over-southwest-us-cities)
-* [Nitrogen Dioxide Levels Rebound in China](https://earthobservatory.nasa.gov/images/146741/nitrogen-dioxide-levels-rebound-in-china?utm_source=card_2&utm_campaign=home)
-
-### Explore the Data
-* [How to Find and Visualize Nitrogen Dioxide Satellite Data](https://earthdata.nasa.gov/learn/articles/feature-articles/health-and-air-quality-articles/find-no2-data)
-* [COVID-19 Data Pathfinder](https://earthdata.nasa.gov/learn/pathfinders/covid-19)
-* [Reductions in Nitrogen Dioxide Associated with Decreased Fossil Fuel Use Resulting from COVID-19 Mitigation](https://svs.gsfc.nasa.gov/4810)
-
-### Explore the Missions
-* [Ozone Monitoring Instrument (OMI)](https://aura.gsfc.nasa.gov/omi.html)
-* [Tropospheric Emissions: Monitoring of Pollution (TEMPO)](http://tempo.si.edu/outreach.html)
-* [Pandora Project](https://pandora.gsfc.nasa.gov/)
-
-
diff --git a/datasets/oci_chla.data.mdx b/datasets/oci_chla.data.mdx
deleted file mode 100644
index b555b172bd..0000000000
--- a/datasets/oci_chla.data.mdx
+++ /dev/null
@@ -1,60 +0,0 @@
----
-id: OCI_PACE_Chlorophyll_a
-name: Chlorophyll a (L2)PACE / OCI
-featured: true
-description: The Chlorophyll a layer provides the near-surface concentration of chlorophyll a in milligrams of chlorophyll pigment per cubic meter (mg/m3) in the ocean.
-media:
- src: ::file ./media/pace-chlorophyll-background.jpg
- alt: Chlorophyll-A data off the coast of North Carolina.
-infoDescription: |
- ::markdown
- - Temporal Extent: 2024 Feb 25 - Present
- - Temporal Resolution: Daily
-layers:
- - id: OCI_PACE_Chlorophyll_a
- stacApiEndpoint: https://dev.openveda.cloud/api/stac/
- stacCol: OCI_PACE_Chlorophyll_a
- name: Chlorophyll a (L2) (from Worldview/GIBS)
- type: wmts
- description: The Chlorophyll a layer provides the near-surface concentration of chlorophyll a in milligrams of chlorophyll pigment per cubic meter (mg/m3) in the ocean.
- sourceParams:
- layers: 1
- version: 1.3.0
- crs: EPSG:3857
- styles:
- zoomExtent:
- - 0
- - 5
- analysis:
- exclude: true
- legend:
- type: gradient
- min: 0
- max: 20
- stops:
- - rgb(72, 0, 130)
- - rgb(0, 0, 255)
- - rgb(0, 255, 255)
- - rgb(0, 255, 0)
- - rgb(255, 255, 0)
- - rgb(255, 165, 0)
- - rgb(255, 0, 0)
- compare:
- datasetId: OCI_PACE_Chlorophyll_a
- layerId: OCI_PACE_Chlorophyll_a
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
----
-
-
-
-## About OCI
-The Ocean Color Instrument (OCI) is a spectrometer used to measure intensity of light over portions of the electromagnetic spectrum: ultraviolet (UV), visible, near infrared, and several shortwave infrared bands. It will enable continuous measurement of light at finer wavelength resolution than previous NASA ocean color sensors, providing detailed information on the global ocean. The color of the ocean is determined by the interaction of sunlight with substances or particles present in seawater such as chlorophyll, a green photosynthetic pigment found in phytoplankton and land plants.
-
-## About PACE
-PACE is NASA's Plankton, Aerosol, Cloud, ocean Ecosystem mission. The mission carries the Ocean Color Instrument (OCI), the Spectro-Polarimeter for Planetary Exploration (SPEXone), and the Hyper Angular Research Polarimeter (HARP2). OCI,PACE’s primary instrument, is an optical spectrometer that measures the intensity of light over portions of the electromagnetic spectrum. SPEXone and HARP2 are multi-angle polarimeters and they will be used to measure how the oscillation of sunlight within a geometric plane - known as its polarization - is changed by passing through clouds, aerosols, and the ocean. Measuring polarization states of UV-to-shortwave light at various angles provides detailed information on the atmosphere and ocean, such as particle size and composition. PACE will cover the entire globe every two days and at a spatial resolution of 1.2 km.
-
-
-
diff --git a/datasets/planetscope.data.mdx b/datasets/planetscope.data.mdx
deleted file mode 100644
index 23e1443edb..0000000000
--- a/datasets/planetscope.data.mdx
+++ /dev/null
@@ -1,678 +0,0 @@
----
-id: planetscope
-name: 'PlanetScope Satellite Imagery (Select Events)'
-description: "Commercial SmallSat Planet Satellite Imagery of select locations in the United States."
-media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
- author:
- name: NASA CSDA
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Air Quality
- - name: Source
- values:
- - Planet
-infoDescription: |
- ::markdown
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Select sites across CONUS
- - **Spatial Resolution:** 3 meters
- - **Data Type:** Research
-
-layers:
- - id: derecho-planet-rockvalley
- stacCol: derecho-planet-rockvalley
- name: Planet TrueColor Satellite Imagery (Rock Valley IA Derecho Damage)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of derecho damage in Rock Valley, Iowa on May 13th, 2022.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
-
- - id: derecho-planet-hartington
- stacCol: derecho-planet-hartington
- name: Planet TrueColor Satellite Imagery (Hartington NE Derecho Damage)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of derecho damage in Hartington, Nebraska on May 13th, 2022.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
-
-
- - id: ps-lakeview-winchester-tornadoes-2024
- stacCol: ps-lakeview-winchester-tornadoes-2024
- name: Planet TrueColor Satellite Imagery (Winchester IN, Lakeview OH Tornado Damage)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of tornado damage at Winchester, Indiana and Lakeview, Ohio in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,2500
- resampling: bilinear
- asset_bidx: cog_default|3,2,1
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/tornado-2024-cover.png
- alt: Wedge tornado passing southeast of Wapakoneta, Ohio on March 14, 2024.
-
- - id: ps-greenfield-pre-tornadoes-2024
- stacCol: ps-greenfield-pre-tornadoes-2024
- name: Planet TrueColor Satellite Imagery (Greenfield IA Tornado Damage - Pre)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of tornado damage at Greenfield, Iowa in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,2500
- resampling: bilinear
- asset_bidx: cog_default|3,2,1
- compare:
- datasetId: planetscope
- layerId: ps-greenfield-post-tornadoes-2024
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/tornado-2024-cover.png
- alt: Wedge tornado passing southeast of Wapakoneta, Ohio on March 14, 2024.
-
- - id: ps-greenfield-post-tornadoes-2024
- stacCol: ps-greenfield-post-tornadoes-2024
- name: Planet TrueColor Satellite Imagery (Greenfield IA Tornado Damage - Post)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of tornado damage at Greenfield, Iowa in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- compare:
- datasetId: planetscope
- layerId: ps-greenfield-pre-tornadoes-2024
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/tornado-2024-cover.png
- alt: Wedge tornado passing southeast of Wapakoneta, Ohio on March 14, 2024.
-
- - id: ps-barnsdall-tornadoes-2024
- stacCol: ps-barnsdall-tornadoes-2024
- name: Planet TrueColor Satellite Imagery (Barnsdall OK Tornado Damage)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of tornado damage at Barnsdall, Oklahoma in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/tornado-2024-cover.png
- alt: Wedge tornado passing southeast of Wapakoneta, Ohio on March 14, 2024.
-
- - id: ps-portage-tornadoes-2024
- stacCol: ps-portage-tornadoes-2024
- name: Planet TrueColor Satellite Imagery (Portage MI Tornado Damage)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of tornado damage at Portage, Michigan in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/tornado-2024-cover.png
- alt: Wedge tornado passing southeast of Wapakoneta, Ohio on March 14, 2024.
-
- - id: ps-tornadoes-2024-difference
- stacCol: ps-tornadoes-2024-difference
- name: Planet TrueColor Satellite Imagery Difference (Greenfield IA Tornado Damage)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery difference of tornado damage at Greenfield, Iowa in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,255
- resampling: bilinear
- asset_bidx: cog_default|4,3,2
- compare:
- datasetId: planetscope
- layerId: tornadoes-2024-dow-vmax-greenfield
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Change in Radiance
- media:
- src: ::file ./media/tornado-2024-cover.png
- alt: Wedge tornado passing southeast of Wapakoneta, Ohio on March 14, 2024.
-
- - id: marsh-ida
- stacCol: marsh-ida
- name: Salt Marsh (Southern Louisiana)
- type: raster
- description: 'Salt Marsh Classification Pre-Ida (Southern Louisiana)'
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: reds
- nodata: 0
- rescale:
- - 0
- - 1
- legend:
- type: categorical
- stops:
- - color: "#ffffff"
- label: Non-Salt Marsh
- - color: "#d73027"
- label: Salt Marsh
- compare:
- datasetId: planetscope
- layerId: marsh-ida
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- info:
- source: UNEP-WCMC
- spatialExtent: Southern Louisiana
- temporalResolution: Monthly
- unit: Binary
- media:
- src: ::file ./media/louisiana-marsh.jpg
- alt: Wetland landscape across southern Louisiana.
-
- - id: marsh-difference
- stacCol: marsh-difference
- name: Salt Marsh Difference (Southern Louisiana)
- type: raster
- description: "Difference in Salt Marshes Pre- and Post-Ida"
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- nodata: 0
- rescale:
- - -1
- - 1
- legend:
- type: categorical
- stops:
- - color: "#FF0000"
- label: Loss of Marsh
- - color: "#0000FF"
- label: Gain of Marsh
- media:
- src: ::file ./media/louisiana-marsh.jpg
- alt: Wetland landscape across southern Louisiana.
-
-
- - id: ida-ndvi
- stacCol: ida-ndvi
- name: NDVI (Salt Marsh Southern Louisiana)
- type: raster
- description: 'Planet NDVI (Southern Louisiana)'
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylgn
- nodata: -999
- rescale:
- - -1
- - 1
- legend:
- type: gradient
- min: "-1"
- max: "1"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee08b"
- - "#d9ef8b"
- - "#66bd63"
- - "#006837"
- compare:
- datasetId: planetscope
- layerId: ida-ndvi
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- info:
- source: PlanetScope
- spatialExtent: Southern Louisiana
- temporalResolution: Monthly
- unit: Binary
- media:
- src: ::file ./media/louisiana-marsh.jpg
- alt: Wetland landscape across southern Louisiana.
-
- - id: ida-ndwi
- stacCol: ida-ndwi
- name: NDWI (Salt Marsh Southern Louisiana)
- type: raster
- description: 'Planet NDWI (Southern Louisiana)'
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylbu
- nodata: -999
- rescale:
- - "-1"
- - "1"
- legend:
- type: gradient
- min: "0"
- max: "1"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee08b"
- - "#d9ef8b"
- - "#66bd63"
- - "#006837"
- compare:
- datasetId: planetscope
- layerId: ida-ndwi
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- info:
- source: PlanetScope
- spatialExtent: Southern Louisiana
- temporalResolution: Monthly
- unit: Binary
- media:
- src: ::file ./media/louisiana-marsh.jpg
- alt: Wetland landscape across southern Louisiana.
-
- - id: ida-ndwi-difference
- stacCol: ida-ndwi-difference
- name: NDWI Difference (Salt Marsh Southern Louisiana)
- type: raster
- description: 'Planet NDWI Difference (Southern Louisiana)'
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdbu
- rescale:
- - -1
- - 1
- legend:
- type: gradient
- min: "-1"
- max: "1"
- stops:
- - "#67001f"
- - "#d6604d"
- - "#fddbc7"
- - "#d1e5f0"
- - "#4393c3"
- - "#053061"
- info:
- source: PlanetScope
- spatialExtent: Southern Louisiana
- temporalResolution: Monthly
- unit: Binary
- media:
- src: ::file ./media/louisiana-marsh.jpg
- alt: Wetland landscape across southern Louisiana.
-
- - id: ida-ndvi-difference
- stacCol: ida-ndvi-difference
- name: NDVI Difference (Salt Marsh Southern Louisiana)
- type: raster
- description: 'Planet NDVI Difference (Southern Louisiana)'
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdbu
- rescale:
- - -1
- - 1
- legend:
- type: gradient
- min: "-1"
- max: "1"
- stops:
- - "#67001f"
- - "#d6604d"
- - "#fddbc7"
- - "#d1e5f0"
- - "#4393c3"
- - "#053061"
- info:
- source: PlanetScope
- spatialExtent: Southern Louisiana
- temporalResolution: Monthly
- unit: Binary
- media:
- src: ::file ./media/louisiana-marsh.jpg
- alt: Wetland landscape across southern Louisiana.
-
- - id: la-fires-planet
- stacCol: la-fires-planet
- name: Planet TrueColor Satellite Imagery (2025 Eaton Fire)
- type: raster
- description: 'Commercial SmallSat Planet Satellite Imagery of the ongoing Eaton Fire in Los Angeles County, California on January 9, 2025.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0
- - 255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/LA-Fires-background.png
- alt: Planet Labs Commercial Satellite Imagery of the Eaton Fire (January 9, 2025).
-
- - id: delta-disasters-planet-flood
- stacCol: delta-disasters-planet-flood
- name: Planet TrueColor Satellite Imagery (2019 MS Delta Flood)
- type: raster
- description: 'False Color Composite (FCC) commercial satellite imagery from Planet Labs of the Mississippi Delta area from the peak of the 2019 backwater flooding. This data was made available through the NASA Commercial Satellite Data Acquisition (CSDA) Program.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 100
- - 4500
- resampling: bilinear
- asset_bidx: cog_default|4,2,1
- compare:
- datasetId: planetscope
- layerId: delta-disasters-planet-tornado
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/delta-disasters-background.jpg
- alt: HLS-derived NDWI from May 26, 2019 with the 2023 Rolling Fork tornado track overlaid.
-
- - id: delta-disasters-planet-tornado
- stacCol: delta-disasters-planet-tornado
- name: PlanetScope Satellite Imagery (2023 Rolling Fork Tornado)
- type: raster
- description: 'False Color Composite (FCC) commercial satellite imagery from Planet Labs of the Rolling Fork, Mississippi area after an EF-4 tornado strike on March 24, 2023. This data was made available through the NASA Commercial Satellite Data Acquisition (CSDA) Program.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 100
- - 2500
- resampling: bilinear
- asset_bidx: cog_default|8,7,5
- compare:
- datasetId: nighttime-lights
- layerId: delta-disasters-planet-flood
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: text
- info:
- source: Planet
- spatialExtent: Local
- temporalResolution: N/A
- unit: Radiance
- media:
- src: ::file ./media/delta-disasters-background.jpg
- alt: HLS-derived NDWI from May 26, 2019 with the 2023 Rolling Fork tornado track overlaid.
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** May 23, 2019 - January 10, 2025
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Select sites across CONUS
- - **Spatial Resolution:** 3 meters
- - **Data Type:** Research
-
-
-
-
- A downed 311-ft cell tower in Hartington, Nebraska as a result of the Serial Derecho on May 12th, 2022.
-
- Blue tarp detections in Jefferson Parish, LA on February 12, 2022.
-
-
-
- ## Interpreting the data
- Machine learning-based detections of blue tarps are
- displayed as red pixels on the basemap and are available for both Hurricane
- Maria (2017) and Hurricane Ida (2021). The input true color imagery used to
- produce the detections are also added as an additional layer and can be used
- for qualitative validation of the detections.
-
-
-
-
-
-
- ## Credits
-
- 1. Source data made available through the NASA Commercial Smallsat Data Acquisition (CSDA) Program
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
-
- ## Additional resources
- 1. [Machine Learning activities at NASA IMPACT](https://impact.earthdata.nasa.gov/project/ml.html)
- 2. [Commercial Smallsat Data Acquisition Program](https://www.earthdata.nasa.gov/esds/csda)
-
-
diff --git a/datasets/snow-projections-cmip6.data.mdx b/datasets/snow-projections-cmip6.data.mdx
deleted file mode 100644
index 0f473773ea..0000000000
--- a/datasets/snow-projections-cmip6.data.mdx
+++ /dev/null
@@ -1,280 +0,0 @@
----
-id: snow-projections-diff
-name: 'Projections of Western United States Snow Water'
-description: "Snow water equivalent and percent-change modeled using the Land Information System framework and CMIP6 projections"
-media:
- src: ::file ./media/snow-projections-median.jpg
- alt: Photo of Matterhorn glacier field
- author:
- name: Justin Pflug
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Snow
- - Climate Projections
- - Hydrology
- - name: Source
- values:
- - NASA EIS
- - CMIP6
-infoDescription: |
- ::markdown
- Snow water equivalent (SWE) is defined as the amount of water in the snow. Here, we present the projected percent-change to projected snow in future periods, relative to the historical period (1995 - 2014).
-layers:
- - id: snow-projections-diff-scenario-245
- stacCol: snow-projections-diff-245
- name: 'SWE Losses, SSP2-4.5'
- type: raster
- description: 'Snow water equivalent (SWE) losses modeled using the Land Information System framework and CMIP6 SSP2-4.5 scenario projections'
- sourceParams:
- resampling: bilinear
- bidx: 1
- nodata: nan
- colormap_name: rdbu
- rescale:
- - -100
- - 100
- compare:
- datasetId: snow-projections-diff
- layerId: snow-projections-diff-scenario-245
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- label: Snow Water Equivalent change
- min: "-100%"
- max: "+100%"
- stops:
- - "#670220"
- - "#D65F4D"
- - "#FCDBC7"
- - "#D1E5F0"
- - "#4393C3"
- - "#0D2F60"
- info:
- source: NASA
- spatialExtent: Western United States
- temporalResolution: Annual
- unit: Percent Diff
-
- - id: snow-projections-diff-scenario-585
- stacCol: snow-projections-diff-585
- name: 'SWE Losses, SSP5-8.5'
- type: raster
- description: 'Snow water equivalent (SWE) losses modeled using the Land Information System framework and CMIP SSP5-8.5 scenario projections'
- sourceParams:
- resampling: bilinear
- bidx: 1
- nodata: nan
- colormap_name: rdbu
- rescale:
- - -100
- - 100
- compare:
- datasetId: snow-projections-diff
- layerId: snow-projections-diff-scenario-585
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- label: Snow Water Equivalent change
- min: "-100%"
- max: "+100%"
- stops:
- - "#670220"
- - "#D65F4D"
- - "#FCDBC7"
- - "#D1E5F0"
- - "#4393C3"
- - "#0D2F60"
- info:
- source: NASA
- spatialExtent: Western United States
- temporalResolution: Annual
- unit: Percent Diff
-
- - id: snow-projections-median-scenario-245
- stacCol: snow-projections-median-245
- name: 'SWE, SSP2-4.5'
- type: raster
- description: 'Snow water equivalent modeled using the Land Information System framework and CMIP6 SSP2-4.5 scenario projections'
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: blues
- rescale:
- - 0
- - 1000
- compare:
- datasetId: snow-projections-median
- layerId: snow-projections-median-scenario-245
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: mm
- type: gradient
- label: Snow Water Equivalent [mm]
- min: "0"
- max: "1000"
- stops:
- - "#F7FBFF"
- - "#D0E1F2"
- - "#94C4DF"
- - "#4A98C9"
- - "#2164AB"
- - "#0E316B"
- info:
- source: NASA
- spatialExtent: Western United States
- temporalResolution: Annual
- unit: mm
-
- - id: snow-projections-median-scenario-585
- stacCol: snow-projections-median-585
- name: 'SWE, SSP5-8.5'
- type: raster
- description: 'Snow water equivalent modeled using the Land Information System framework and CMIP6 SSP5-8.5 scenario projections'
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: blues
- rescale:
- - 0
- - 1000
- compare:
- datasetId: snow-projections-median
- layerId: snow-projections-median-scenario-585
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- legend:
- unit:
- label: mm
- type: gradient
- label: Snow Water Equivalent [mm]
- min: "0"
- max: "1000"
- stops:
- - "#F7FBFF"
- - "#D0E1F2"
- - "#94C4DF"
- - "#4A98C9"
- - "#2164AB"
- - "#0E316B"
- info:
- source: NASA
- spatialExtent: Western United States
- temporalResolution: Annual
- unit: mm
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2025-2085
- - **Spatial Extent:** Western United States
- - **Spatial Resolution:** 25 kilometers
- - **Data Units:** Percentage (%)
- - **Data Type:** Research
-
-
-
-
- Median projected changes in snow water equivalent modeled using the Land Information System framework and CMIP6 SSP5-8.5 scenario projections for years 2065-2085.
-
-
-
-
-
-
-
-
-
- ## About
- Snow water equivalent (SWE) is defined as the amount of water in the snow. Here, we present the projected SWE and percent-change to projected snow in future periods, relative to the historical period (1995 - 2014).
-
- We have examined two different Shared Socioeconomic Pathways (SSPs) which describe potential future scenarios based on decisions in climate policies and actions as well as human infrastructure and governance. We have gathered the SSP2-4.5 which represents an intermediate radiative forcing pathway and the SSP5-8.5 representing a future with a high radiative forcing.
-
-
-
-
-
-
- ## Scientific Details
- SWE is modeled using Noah-MP land surface model (LSM) within LIS framework. A baseline simulation was developed to represent 20-year median snow evolution between 1995 and 2014. The baseline simulation was then perturbed using monthly climate change signals to air temperature, precipitation, humidity, radiation, wind, and surface pressure. SWE projections in future timeframes represent the median of a 23-member ensemble of simulations performed using climate change signals from the Coupled Model Intercomparison Project, phase 6 (CMIP6) with downscaling from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The modeled SWE is produced at 1 km spatial resolutions.
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Future Projections of Western US Montane Snowpack**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional Resources
-
- [EIS Freshwater](https://freshwater.eis.smce.nasa.gov/)
- [Global Daily Downscaled Projections](https://www.nasa.gov/nex/gddp)
- [Land Information System](https://lis.gsfc.nasa.gov/)
- [Shared Socioeconomic Pathways](https://earth.gov/sealevel/faq/124/what-are-shared-socioeconomic-pathways-or-ssps/#:~:text=Shared%20Socioeconomic%20Pathways%20(SSPs)%20are,gas%20emissions%20and%20climate%20change.)
-
-
-
\ No newline at end of file
diff --git a/datasets/so2.data.mdx b/datasets/so2.data.mdx
deleted file mode 100644
index 567c121cad..0000000000
--- a/datasets/so2.data.mdx
+++ /dev/null
@@ -1,190 +0,0 @@
----
-id: so2
-name: 'Sulfur Dioxide'
-description: "NASA OMI/Aura Sulfur Dioxide (SO2) Total Column"
-media:
- src: ::file ./media/so2--dataset-cover.jpg
- alt: OMI SO2 data visualized in a map
- author:
- name: NASA
- url:
-taxonomy:
- - name: Topics
- values:
- - Air Quality
- - name: Subtopics
- values:
- - Surface Meteorology
- - Urban
- - name: Source
- values:
- - OMI
- - Aura
-
-infoDescription: |
- ::markdown
- The OMI Sulfur Dioxide (SO2) Total Column layer indicates the column density of sulfur dioxide and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. This information is used by the Volcanic Ash Advisory Centers in advisories to airlines for operational decision
-layers:
- - id: OMSO2PCA-COG
- stacCol: OMSO2PCA-COG
- name: 'Sulfur Dioxide'
- type: raster
- description: 'NASA OMI/Aura Sulfur Dioxide (SO2) Total Column'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: rdylbu_r
- rescale:
- - 0
- - 1
- compare:
- datasetId: so2
- layerId: OMSO2PCA-COG
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- legend:
- type: gradient
- label: SO2 Total column (DU)
- min: "0"
- max: "1"
- stops:
- - "#313695"
- - "#74add1"
- - "#e0f3f8"
- - "#fee090"
- - "#f46d43"
- - "#a50026"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2005 - 2021
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 30 km
- - **Data Type:** Research
-
-
-
-
- Comparison of total column SO2 for India for 2005 and 2021
-
-
-
-
-
-
-
-
-
-
-
-
- ## Scientific Details
- The OMI Sulfur Dioxide (SO2) Total Column layer indicates the column density of sulfur dioxide and is measured in Dobson Units (DU). Sulfur Dioxide and Aerosol Index products are used to monitor volcanic clouds and detect pre-eruptive volcanic degassing globally. This information is used by the Volcanic Ash Advisory Centers in advisories to airlines for operational decisions.
-
- Sulfur/Sulphur Dioxide (SO2) is one of the US Environmental Protection Agency's (EPA) six major regulated criteria pollutants (Tropospheric Ozone, Nitrogen Dioxide, Sulfur Dioxide, Lead, PM2.5 and PM 10 particulates). It irritates the eyes, nose, and lungs. High concentrations of SO2 can result in temporary breathing impairment. It is produced by combustion of coal, fuel oil, and gasoline, since these fuels contain sulfur in the combustion, and in the oxidation of naturally occurring sulfur gases. It is a precursor to sulfuric acid, which is a major constituent of acid rain. SO2 is injected into the stratosphere by volcanic eruptions. SO2 also is a major precursor to PM2.5, which is a significant health concern, and a main contributor to poor visibility.
-
-
-
-
-
-
-
- Comparison of total column SO2 for China for 2006 and 2021
-
-
-
-## Interpreting the data
-Global total column SO2 observations are generated using a principal component analysis algorithm using a single observation in the planetary boundary layer. The single observation is obtained from the "best pixel" of the level-2 OMI/Aura SO2 total column observations for the observation day. SO2 observations in support of air quality are available from 2005-2021 at 0.25 x 0.25 degree spatial resolution. Higher concentrations of SO2 are shaded in yellow and lower concentrations in purple.
-
-
-
-
-
-## Credits
-1. Can Li, Nickolay A. Krotkov, and Peter Leonard (2020), OMI/Aura Sulfur Dioxide (SO2) Total Column L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), 10.5067/Aura/OMI/DATA3008
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Monitoring Volcanic Sulfur Dioxide Emissions**
-
- **Air Quality and COVID-19**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional resources
- [OMI Aura Sulfur Dioxide (SO2) Total Column Dataset Access](https://disc.gsfc.nasa.gov/datasets/OMSO2e_003/summary)
-
- ### Explore the Missions
- [SO2 Monitoring Home Page](https://so2.gsfc.nasa.gov/)
-
- [Aura Project Home Page](https://aura.gsfc.nasa.gov/)
-
-
diff --git a/datasets/soil-texture.data.mdx b/datasets/soil-texture.data.mdx
deleted file mode 100644
index bfacef32e7..0000000000
--- a/datasets/soil-texture.data.mdx
+++ /dev/null
@@ -1,473 +0,0 @@
----
-id: soil-texture
-name: 'ISRIC World Soil Texture Classifications'
-description: "250 meter resolution global soil texture dataset from ISRIC, produced in 2017. Available at seven soil layer depths."
-media:
- src: ::file ./media/soil-texture-background.jpeg
- alt: Examples of two different soil types
- author:
- name: Soil Sensor
- url: https://soilsensor.com/articles/soil-textures/
-taxonomy:
- - name: Topics
- values:
- - Agriculture
- - name: Subtopics
- values:
- - Habitat
- - Land Use
- - name: Source
- values:
- - ISRIC
-layers:
- - id: soil-texture-0cm
- stacCol: soil-texture
- name: Soil Texture at the Surface (0 cm Depth)
- type: raster
- description: 'ISRIC Soil Texture Classification at 0 cm'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: soil_texture_0cm_250m
- colormap_name: soil_texture
- nodata: 255
- compare:
- datasetId: nlcd-annual-conus
- layerId: nlcd-annual-conus
- mapLabel: |
- ::js ({dateFns, datetime, compareDatetime}) => {
- return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`;
- }
-
- legend:
- type: categorical
- stops:
- - color: "#F89E61"
- label: "Clay"
- - color: "#BA8560"
- label: "Silty Clay"
- - color: "#D8D2B4"
- label: "Sandy Clay"
- - color: "#AE734C"
- label: "Clay Loam"
- - color: "#9E8478"
- label: "Silty Clay Loam"
- - color: "#C6A365"
- label: "Sandy Clay Loam"
- - color: "#B4A67D"
- label: "Loam"
- - color: "#E1D4C4"
- label: "Silty Loam"
- - color: "#BEB56D"
- label: "Sandy Loam"
- - color: "#777C7A"
- label: "Silt"
- - color: "#A89B6F"
- label: "Loamy Sand"
- - color: "#E9E2AF"
- label: "Sand"
- info:
- source: ISRIC
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
- - id: soil-texture-5cm
- stacCol: soil-texture
- name: Soil Texture at 5 cm Depth
- type: raster
- description: 'ISRIC Soil Texture Classification at 5 cm'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: soil_texture_5cm_250m
- colormap_name: soil_texture
- nodata: 255
-
- legend:
- type: categorical
- stops:
- - color: "#F89E61"
- label: "Clay"
- - color: "#BA8560"
- label: "Silty Clay"
- - color: "#D8D2B4"
- label: "Sandy Clay"
- - color: "#AE734C"
- label: "Clay Loam"
- - color: "#9E8478"
- label: "Silty Clay Loam"
- - color: "#C6A365"
- label: "Sandy Clay Loam"
- - color: "#B4A67D"
- label: "Loam"
- - color: "#E1D4C4"
- label: "Silty Loam"
- - color: "#BEB56D"
- label: "Sandy Loam"
- - color: "#777C7A"
- label: "Silt"
- - color: "#A89B6F"
- label: "Loamy Sand"
- - color: "#E9E2AF"
- label: "Sand"
- info:
- source: ISRIC
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
- - id: soil-texture-15cm
- stacCol: soil-texture
- name: Soil Texture at 15 cm Depth
- type: raster
- description: 'ISRIC Soil Texture Classification at 15 cm'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: soil_texture_15cm_250m
- colormap_name: soil_texture
- nodata: 255
-
- legend:
- type: categorical
- stops:
- - color: "#F89E61"
- label: "Clay"
- - color: "#BA8560"
- label: "Silty Clay"
- - color: "#D8D2B4"
- label: "Sandy Clay"
- - color: "#AE734C"
- label: "Clay Loam"
- - color: "#9E8478"
- label: "Silty Clay Loam"
- - color: "#C6A365"
- label: "Sandy Clay Loam"
- - color: "#B4A67D"
- label: "Loam"
- - color: "#E1D4C4"
- label: "Silty Loam"
- - color: "#BEB56D"
- label: "Sandy Loam"
- - color: "#777C7A"
- label: "Silt"
- - color: "#A89B6F"
- label: "Loamy Sand"
- - color: "#E9E2AF"
- label: "Sand"
- info:
- source: ISRIC
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
- - id: soil-texture-30cm
- stacCol: soil-texture
- name: Soil Texture at 30 cm Depth
- type: raster
- description: 'ISRIC Soil Texture Classification at 30 cm'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: soil_texture_30cm_250m
- colormap_name: soil_texture
- nodata: 255
-
- legend:
- type: categorical
- stops:
- - color: "#F89E61"
- label: "Clay"
- - color: "#BA8560"
- label: "Silty Clay"
- - color: "#D8D2B4"
- label: "Sandy Clay"
- - color: "#AE734C"
- label: "Clay Loam"
- - color: "#9E8478"
- label: "Silty Clay Loam"
- - color: "#C6A365"
- label: "Sandy Clay Loam"
- - color: "#B4A67D"
- label: "Loam"
- - color: "#E1D4C4"
- label: "Silty Loam"
- - color: "#BEB56D"
- label: "Sandy Loam"
- - color: "#777C7A"
- label: "Silt"
- - color: "#A89B6F"
- label: "Loamy Sand"
- - color: "#E9E2AF"
- label: "Sand"
- info:
- source: ISRIC
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
- - id: soil-texture-60cm
- stacCol: soil-texture
- name: Soil Texture at 60 cm Depth
- type: raster
- description: 'ISRIC Soil Texture Classification at 60 cm'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: soil_texture_60cm_250m
- colormap_name: soil_texture
- nodata: 255
-
- legend:
- type: categorical
- stops:
- - color: "#F89E61"
- label: "Clay"
- - color: "#BA8560"
- label: "Silty Clay"
- - color: "#D8D2B4"
- label: "Sandy Clay"
- - color: "#AE734C"
- label: "Clay Loam"
- - color: "#9E8478"
- label: "Silty Clay Loam"
- - color: "#C6A365"
- label: "Sandy Clay Loam"
- - color: "#B4A67D"
- label: "Loam"
- - color: "#E1D4C4"
- label: "Silty Loam"
- - color: "#BEB56D"
- label: "Sandy Loam"
- - color: "#777C7A"
- label: "Silt"
- - color: "#A89B6F"
- label: "Loamy Sand"
- - color: "#E9E2AF"
- label: "Sand"
- info:
- source: ISRIC
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
- - id: soil-texture-100cm
- stacCol: soil-texture
- name: Soil Texture at 100 cm Depth
- type: raster
- description: 'ISRIC Soil Texture Classification at 100 cm'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: soil_texture_100cm_250m
- colormap_name: soil_texture
- nodata: 255
-
- legend:
- type: categorical
- stops:
- - color: "#F89E61"
- label: "Clay"
- - color: "#BA8560"
- label: "Silty Clay"
- - color: "#D8D2B4"
- label: "Sandy Clay"
- - color: "#AE734C"
- label: "Clay Loam"
- - color: "#9E8478"
- label: "Silty Clay Loam"
- - color: "#C6A365"
- label: "Sandy Clay Loam"
- - color: "#B4A67D"
- label: "Loam"
- - color: "#E1D4C4"
- label: "Silty Loam"
- - color: "#BEB56D"
- label: "Sandy Loam"
- - color: "#777C7A"
- label: "Silt"
- - color: "#A89B6F"
- label: "Loamy Sand"
- - color: "#E9E2AF"
- label: "Sand"
- info:
- source: ISRIC
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
- - id: soil-texture-200cm
- stacCol: soil-texture
- name: Soil Texture at 200 cm Depth
- type: raster
- description: 'ISRIC Soil Texture Classification at 200 cm'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- assets: soil_texture_200cm_250m
- colormap_name: soil_texture
- nodata: 255
-
- legend:
- type: categorical
- stops:
- - color: "#F89E61"
- label: "Clay"
- - color: "#BA8560"
- label: "Silty Clay"
- - color: "#D8D2B4"
- label: "Sandy Clay"
- - color: "#AE734C"
- label: "Clay Loam"
- - color: "#9E8478"
- label: "Silty Clay Loam"
- - color: "#C6A365"
- label: "Sandy Clay Loam"
- - color: "#B4A67D"
- label: "Loam"
- - color: "#E1D4C4"
- label: "Silty Loam"
- - color: "#BEB56D"
- label: "Sandy Loam"
- - color: "#777C7A"
- label: "Silt"
- - color: "#A89B6F"
- label: "Loamy Sand"
- - color: "#E9E2AF"
- label: "Sand"
- info:
- source: ISRIC
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
----
-
-
- ## Dataset Details
- - **Temporal Extent:** 2017
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 250 m
- - **Data Type:** Research
-
-
-
-
- ISRIC surface soil texture classifications (0 cm depth) in the Ohio River Valley.
-
-
-
-
-
-
-
-
- ISRIC Soil Texture Classification Triangle showing the percentage of clay, silt, and sand in each type.
-
-
-
-
- ### About
- The ISRIC Soil Texture dataset (SoilGrids 250) provides detailed soil texture classifications, which are critical for understanding soil properties, water retention, and agricultural potential. Maintained by the International Soil Reference and Information Centre (ISRIC), this dataset has been instrumental in various environmental and agricultural studies across the globe. This classification system breaks soils down into different categories based on their percentage of sand, silt, and clay, providing an essential resource for researchers, land managers, and farmers.
-
-
-
-
-
- ### What the ISRIC Soil Texture Dataset Offers
- - Soil Texture Classes: This dataset classifies soils into twelve texture categories, ranging from sand, silt, and clay, to more complex combinations such as loam, clay loam, and silty clay. Each category offers a distinct combination of sand, silt, and clay content, helping users determine soil characteristics.
-
- - Soil Texture Triangle: A visualization tool used to depict the relationships between the percentage of sand, silt, and clay in soil. It allows users to easily locate and interpret soil types based on their texture composition.
-
- - Applications in Agriculture and Environmental Studies: The soil texture classification is pivotal in determining soil fertility, drainage capabilities, and erosion potential, making it crucial for land use planning, crop management, and climate studies.
-
-
-
-
-
-
-
- ### Access the Data
-
- Visit the [SoilGrids](https://www.soilgrids.org) webpage to explore all of the data options that SoilGrids 250 offers.
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- Hengl, T., de Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagotić, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publication
-
- Hengl, T., de Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagotić, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748
-
-
-
-
-
-
-
- ### Other Publications
-
- Rossiter, D. G. (2001). Methodology for soil resource inventories. Computers and Electronics in Agriculture, 35(2), 189–214.
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
\ No newline at end of file
diff --git a/datasets/sport-lis.data.mdx b/datasets/sport-lis.data.mdx
deleted file mode 100644
index 5a2d655b6f..0000000000
--- a/datasets/sport-lis.data.mdx
+++ /dev/null
@@ -1,188 +0,0 @@
----
-id: soil-moisture
-name: SPoRT Land Information System
-description: SPoRT’s real-time instance of the Land Information System provides low-latency soil moisture analyses that help diagnose rapid drought onset and elevated wildfire threat.
-media:
- src: ::file ./media/soil-moisture-main.jpg
- alt: Dry Clay Wall with Cracks
- author:
- name: Clay Banks
- url: https://unsplash.com/photos/EdscD_R28bM
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - Agriculture
- - name: Subtopics
- values:
- - Drought
- - Hydrology
- - Soil Moisture
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications.
-layers:
- - sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: rdylbu
- rescale:
- - 0
- - 100
- nodata: 9999.0
- name: 0-100 cm Soil Moisture Percentile of normal
- legend:
- type: gradient
- min: "0"
- max: "100"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- type: raster
- id: sport-lis-vsm0_100cm-percentile
- description: "The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Real-Time Land Information System (LIS). The Volumetric Soil Moisture product represents the actual moisture in a soil column from 0-100cm in depth."
- stacCol: sport-lis-vsm0_100cm-percentile
- zoomExtent:
- - 2
- - 16
- compare:
- datasetId: soil-moisture
- layerId: sport-lis-vsm0_100cm-percentile
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'MMM yyyy')} VS ${dateFns.format(compareDatetime, 'MMM yyyy')}`;
- }
- info:
- source: NASA
- spatialExtent: Contiguous United States
- temporalResolution: Sub-Annual
- unit: cm
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** Sept. 6, 2016 and Nov. 29, 2016
- - **Spatial Extent:** Contiguous United States
- - **Spatial Resolution:** 3 km
- - **Data Type:** Research
-
-
-
-
- Comparison of soil moisture percentile over Utah between September 6, 2016 and November 29, 2016.
-
-
-
-
-
-
-
- ## About
- The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications.
-
- SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2. The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific time of year. Soil moisture percentile maps are constructed for the model integrated layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product.
-
- The SPoRT-LIS bridges the 4-day latency gap in NLDAS-2 atmospheric forcing input by incorporating forcing from the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates. The real-time component of the SPoRT-LIS updates every 6 hours, and also includes a forecast component that produces soil moisture percentiles out to 2 weeks. Another unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model.
-
-
-
-
-
-
- ## Interpreting the Data
-
- A variety of output variables are used to depict soil moisture in the SPoRT-LIS analyses. Volumetric soil moisture shows the fraction of volume of water occupying the total soil volume and typically ranges from ~0.03 to 0.45, depending on the defined sand-silt-clay soil classification defined at a given grid point. The volumetric soil moisture is often spatially correlated to the pattern of soil classification; therefore, the relative soil moisture (or percent saturation) can be a preferred method to analyze the soil moisture based on the water-holding capacity of the soils. The relative soil moisture (RSM) is defined as the ratio of the modeled volumetric soil moisture (θ) between the wilting and saturation reference values for a given soil classification:
-
- *RSM=((θ - θwilt)/(θsat - θwilt )) x 100%. ... (1)*
-
- The RSM essentially normalizes the volumetric soil moisture based on the specified characteristics of each unique soil texture classification (sand-silt-clay composition).
-
-Meanwhile, the soil moisture percentiles depict where the current analysis value lies within the histogram distribution of historical soil moisture values. An example of the 0-2 meter soil moisture percentile over Napa County, California shows how the wet 2022-23 winter over central California has contributed to historically high soil moisture values, averaged over all SPoRT-LIS points lying within Napa County, yielding a soil moisture percentile value of 92.9 for 23 March 2023:
-
-
-
- The national soil moisture percentile map on 23 March 2023 is shown below:
-
-
-
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **The Value of Data in Monitoring The Health of Crops**
-
- **Mapping Water Availability over North America**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
-
- ## Additional Resources
-
- - [Real-time SPoRT-LIS viewer](https://weather.ndc.nasa.gov/sport/viewer/?dataset=lis_conus&product=rsoim0-100)
- - [Access to real-time rolling archive of digital data and in various
- formats](https://geo.nsstc.nasa.gov/SPoRT/modeling/lis/conus3km/)
- - [Daily animations of experimental 2-week forecasts of SPoRT-LIS soil moisture
- percentiles](https://geo.nsstc.nasa.gov/SPoRT/modeling/lis/conus3km/forecasts/)
- - [NASA Land Information System project page](https://lis.gsfc.nasa.gov/)
-
-
-
diff --git a/datasets/thomasfire.data.mdx b/datasets/thomasfire.data.mdx
deleted file mode 100644
index c513e9ea1f..0000000000
--- a/datasets/thomasfire.data.mdx
+++ /dev/null
@@ -1,215 +0,0 @@
----
-id: thomasfire
-isHidden: true
-name: "Thomas Fire Analysis"
-description: "Maximum Fire Radiative Power and Burned Area Reflectance for the Thomas Fire of 2017"
-media:
- src: ::file ./media/mtbs-burn-severity--dataset-cover.jpg
- alt: Hillside engulfed by wildfire
- author:
- name: Mike Newbry
- url: https://unsplash.com/photos/DwtX9mMHBJ0
-taxonomy:
- - name: Topics
- values:
- - Wildfires
- - name: Subtopics
- values:
- - Habitat
- - Agriculture
- - Natural Disasters
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- Maximum Fire Radiative Power recorded by the Suomi NPP VIIRS sensor per 12hr fire line segment for the Thomas Fire of 2017
-layers:
- - id: frp-max-thomasfire
- stacCol: frp-max-thomasfire
- name: Maximum Fire Radiative Power for Thomas Fire
- type: raster
- description: "Maximum Fire Radiative Power recorded by the Suomi NPP VIIRS sensor per 12hr fire line segment for the Thomas Fire of 2017"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- asset_bidx: cog_default|1
- colormap_name: inferno_r
- rescale:
- - 1.0
- - 1080
- nodata: nan
- compare:
- datasetId: thomasfire
- layerId: barc-thomasfire
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `FRP: ${dateFns.format(datetime, 'yyyy')} VS BARC: ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- legend:
- type: gradient
- label: Maximum FRP
- min: "1"
- max: "1100"
- stops:
- - "#FBB41A"
- - "#BB3754"
- - "#781D6D"
- - "#34095F"
- info:
- source: NASA
- spatialExtent: Thomas Fire Area
- temporalResolution: Annual
- unit: Watts
-
- - id: barc-thomasfire
- stacCol: barc-thomasfire
- name: Burn Area Reflectance Classification for Thomas Fire
- type: raster
- description: "Burn Area Reflectance Classification (BARC) from the Burned Area Emergency Response (BAER) program for the Thomas Fire of 2017"
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdylgn_r
- rescale:
- - 1
- - 4
- nodata: nan
- legend:
- type: categorical
- stops:
- - color: "#94c772"
- label: "Unburned"
- - color: "#faf88e"
- label: "Low"
- - color: "#ea915e"
- label: "Moderate"
- - color: "#971d2b"
- label: "High"
- info:
- source: NASA
- spatialExtent: Thomas Fire Area
- temporalResolution: Annual
- unit: Categorical
- compare:
- datasetId: thomasfire
- layerId: barc-thomasfire
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `FRP: ${dateFns.format(datetime, 'yyyy')} VS BARC: ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** December 2017
- - **Spatial Extent:** California counties: Ventura and Santa Barbara
- - **Spatial Resolution:** 20 meters
- - **Data Type:** Research
-
-
-
-
-
-
-
-
- ## About
-
- Maximum Fire Radiative Power recorded by the Suomi NPP VIIRS sensor per 12hr fire line segment for the Thomas Fire of 2017.
-
- Burn Area Reflectance Classification (BARC) from the Burned Area Emergency Response (BAER) program for the Thomas Fire of 2017.
-
-
-
-
-
-
-
-
- ## Scientific Details
-
- BARC maps are made by comparing satellite near and mid infrared reflectance values. The logic behind the process is as follows:
-
- Near infrared light is largely reflected by healthy green vegetation. That means that near infrared bands will be very high in areas of healthy green vegetation and low in areas where there is little vegetation. Mid infrared light is largely reflected by rock and bare soil. That means that mid infrared band values will be very high in bare, rocky areas with little vegetation and low in areas of healthy green vegetation. Imagery collected over a forest in a pre-fire condition will have very high near infrared band values and very low mid infrared band values. Imagery collected over a forest after a fire will have very low near infrared band values and very high mid infrared band values.
-
- It is the relationship between these two bands that the BARC attempts to exploit. The best way to do this is to measure the relationship between these bands prior to the fire and then again post fire. The areas where the relationship between the two bands has changed the most are most likely to be severely burned. The areas where that relationship has changed little are likely to be unburned or very lightly burned. To determine this relationship, analysts perform a band ratio between the mid and near infrared bands. The result is a classification of burned areas.
-
- Fire Radiative Power is the rate of emitted radiative energy by the fire at the time of the observations.
-
-
-
-
-
-
-
-
-
- ## Limitations of use
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
- ## License
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Key Publications
- Li, F., Zhang, X., Kondragunta, S., & Csiszar, I. (2018). Comparison of fire radiative power estimates from VIIRS and MODIS observations. Journal of Geophysical Research: Atmospheres, 123, 4545–4563. https://doi.org/10.1029/2017JD027823
-
- Fu, Y.; Li, R.; Wang, X.; Bergeron, Y.; Valeria, O.; Chavardès, R.D.; Wang, Y.; Hu, J. Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products. Remote Sens. 2020, 12, 2870. https://doi.org/10.3390/rs12182870
-
- Oliva, P., Schroeder, W., (2015). Assessment of VIIRS 375 m active fire detection product for direct burned area mapping. Remote Sensing of Environment. 160. 144-155. http://doi.org/10.1016/j.rse.2015.01.010.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Hydrological Drivers and Impacts of Fire**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
- [Fire Radiative Power](https://www.star.nesdis.noaa.gov/star/documents/seminardocs/2022/20220420_VAWS_Csiszar_VIIRS_fire.pdf)
-
- [Suomi NPP VIIRS](https://ncc.nesdis.noaa.gov/VIIRS/)
-
-
\ No newline at end of file
diff --git a/datasets/tornadoes2024-dow.data.mdx b/datasets/tornadoes2024-dow.data.mdx
deleted file mode 100644
index 565f886a5c..0000000000
--- a/datasets/tornadoes2024-dow.data.mdx
+++ /dev/null
@@ -1,579 +0,0 @@
----
-id: tornadoes-2024-dow
-name: "Spring 2024 Tornadoes DOW Data"
-description: "Research-grade radar data collected by DOW7 in the field during the Spring 2024 tornado season."
-media:
- src: ::file ./media/tornadoes-2024-dow-background.jpg
- alt: DOW6 parked in front of a tornadic mesocyclone in the Plains.
- author:
- name: Ryan McGinnis
- url: https://static.wikia.nocookie.net/stormchasers/images/1/1b/DOW.jpg/revision/latest?cb=20100115235828
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - name: Source
- values:
- - DOW
-infoDescription: |
- ::markdown
- - **Temporal Extent:** April 26 and May 21, 2024
- - **Temporal Resolution:** 7 Seconds
- - **Spatial Extent:** Harlan and Greenfield, Iowa
- - **Spatial Resolution:** Varies
- - **Data Units:** Varies
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-layers:
- - id: tornadoes-2024-dow-vmax-greenfield
- stacCol: tornadoes-2024-dow-vmax-greenfield
- name: Derived Maximum Velocity (Greenfield, IA; DOW7)
- type: raster
- description: "Derived maximum velocities of the EF-4 Greenfield, IA tornado on May 21, 2024 collected by DOW7."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: gist_ncar
- nodata: -999
- rescale:
- - 55
- - 250
- legend:
- type: gradient
- min: "55 mph"
- max: "310 mph"
- stops:
- - '#000080' # dark navy
- - '#000ad5' # deep blue
- - '#00edff' # bright cyan
- - '#00fd39' # neon green
- - '#74e800' # lime green
- - '#d8ff22' # yellow-green
- - '#ffce05' # orange-yellow
- - '#ff3700' # red-orange
- - '#f107ff' # magenta
- - '#ec82ef' # light purple
- - '#fef8fe' # very light pink
-
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: dBZ
-
- - id: tornadoes-2024-dow-refl-greenfield
- stacCol: tornadoes-2024-dow-refl-greenfield
- name: DOW7 Reflectivity (Greenfield, IA)
- type: raster
- description: "Horizontal reflectivity values (dBZ) from the EF-4 Greenfield, IA tornado on May 21, 2024 collected by DOW7. Scan taken at 20:41:08."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: turbo
- nodata: -32768
- rescale:
- - -10
- - 50
- legend:
- type: gradient
- min: "-10 dBZ"
- max: "50 dBZ"
- stops:
- - "#30123B" # Deep purple (lowest reflectivity)
- - "#443983" # Dark blue
- - "#31678E" # Cyan
- - "#35B779" # Green
- - "#FDE725" # Yellow
- - "#FF9900" # Orange
- - "#FF0000" # Red (highest reflectivity)
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: dBZ
-
- - id: tornadoes-2024-dow-vg-greenfield
- stacCol: tornadoes-2024-dow-vg-greenfield
- name: DOW7 Velocity (Ground; Greenfield, IA)
- type: raster
- description: "Ground-based velocities (m/s) of the EF-4 Greenfield, IA tornado on May 21, 2024 collected by DOW7. Scan taken at 20:41:08."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: seismic
- nodata: -32768
- rescale:
- - -75
- - 75
- legend:
- type: gradient
- min: "-75 m/s"
- max: "75 m/s"
- stops:
- - "#0000FF" # Deep blue (negative extreme)
- - "#3399FF" # Light blue
- - "#66CCFF" # Very light blue
- - "#FFFFFF" # White (zero velocity)
- - "#FF9999" # Light pink
- - "#FF6666" # Light red
- - "#FF0000" # Deep red (positive extreme)
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: m/s
- compare:
- datasetId: tornadoes-2024-dow
- layerId: tornadoes-2024-dow-rhohv-greenfield
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
-
- - id: tornadoes-2024-dow-rhohv-greenfield
- stacCol: tornadoes-2024-dow-rhohv-greenfield
- name: DOW7 Correlation Coefficient (Greenfield, IA)
- type: raster
- description: "Correlation Coefficient values from the EF-4 Greenfield, IA tornado on May 21, 2024 collected by DOW7. Scan taken at 20:41:08."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: turbo
- nodata: -32768
- rescale:
- - 0
- - 1
- legend:
- type: gradient
- min: "0"
- max: "1"
- stops:
- - "#30123B" # Deep purple (low correlation)
- - "#443983" # Dark blue
- - "#31678E" # Cyan
- - "#35B779" # Green
- - "#FDE725" # Yellow
- - "#FF0000" # Red (high correlation)
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: unitless
- compare:
- datasetId: tornadoes-2024-dow
- layerId: tornadoes-2024-dow-vg-greenfield
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
-
- - id: tornadoes-2024-dow-sw-greenfield
- stacCol: tornadoes-2024-dow-sw-greenfield
- name: DOW7 Spectrum Width (Greenfield, IA)
- type: raster
- description: "Spectrum Width values (m/s) from the EF-4 Greenfield, IA tornado on May 21, 2024 collected by DOW7. Scan taken at 20:41:08."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdpu
- nodata: -32768
- rescale:
- - 0
- - 25
- legend:
- type: gradient
- min: "0 m/s"
- max: "25 m/s"
- stops:
- - "#f7f4f9" # Light pink (low spectrum width)
- - "#fddbc7" # Very light pink
- - "#f4a582" # Pinkish-orange
- - "#d6604d" # Orange-red
- - "#b2182b" # Deep red
- - "#67001f" # Deepest purple (high spectrum width)
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: m/s
-
- - id: tornadoes-2024-dow-zdr-greenfield
- stacCol: tornadoes-2024-dow-zdr-greenfield
- name: DOW7 Differential Reflectivity (Greenfield, IA)
- type: raster
- description: "Differential Reflectivity values (dB) from the EF-4 Greenfield, IA tornado on May 21, 2024 collected by DOW7. Scan taken at 20:41:08."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: nipy_spectral
- nodata: -32768
- rescale:
- - -5
- - 7
- legend:
- type: gradient
- min: "-5 dB"
- max: "7 dB"
- stops:
- - "#000080" # Dark blue (low ZDR)
- - "#0000FF" # Blue
- - "#00FFFF" # Cyan
- - "#00FF00" # Green
- - "#FFFF00" # Yellow
- - "#FFA500" # Orange
- - "#FF0000" # Red
- - "#800080" # Purple (high ZDR)
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: dB
-
- - id: tornadoes-2024-dow-refl-harlan
- stacCol: tornadoes-2024-dow-refl-harlan
- name: DOW7 Reflectivity (Harlan, IA)
- type: raster
- description: "Horizontal reflectivity values (dBZ) from the EF-3 Harlan, IA tornado on April 26, 2024 collected by DOW7. Scan taken at 23:08:12."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: turbo
- nodata: -32768
- rescale:
- - -10
- - 50
- legend:
- type: gradient
- min: "-10 dBZ"
- max: "50 dBZ"
- stops:
- - "#30123B" # Deep purple (lowest reflectivity)
- - "#443983" # Dark blue
- - "#31678E" # Cyan
- - "#35B779" # Green
- - "#FDE725" # Yellow
- - "#FF9900" # Orange
- - "#FF0000" # Red (highest reflectivity)
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: dBZ
- compare:
- datasetId: tornadoes-2024-dow
- layerId: tornadoes-2024-dow-v-harlan
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
-
- - id: tornadoes-2024-dow-v-harlan
- stacCol: tornadoes-2024-dow-v-harlan
- name: DOW7 Velocity (Ground; Harlan, IA)
- type: raster
- description: "Velocities (m/s) of the EF-3 Harlan, IA tornado on April 26, 2024 collected by DOW7. Scan taken at 23:08:12."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: seismic
- nodata: -32768
- rescale:
- - -75
- - 75
- legend:
- type: gradient
- min: "-75 m/s"
- max: "75 m/s"
- stops:
- - "#0000FF"
- - "#3399FF"
- - "#66CCFF"
- - "#FFFFFF"
- - "#FF9999"
- - "#FF6666"
- - "#FF0000"
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: m/s
- compare:
- datasetId: tornadoes-2024-dow
- layerId: tornadoes-2024-dow-refl-harlan
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
-
- - id: tornadoes-2024-dow-rhohv-harlan
- stacCol: tornadoes-2024-dow-rhohv-harlan
- name: DOW7 Correlation Coefficient (Harlan, IA)
- type: raster
- description: "Correlation Coefficient values from the EF-3 Harlan, IA tornado on April 26, 2024 collected by DOW7. Scan taken at 23:08:12."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: turbo
- nodata: -32768
- rescale:
- - 0
- - 1
- legend:
- type: gradient
- min: "0"
- max: "1"
- stops:
- - "#30123B"
- - "#443983"
- - "#31678E"
- - "#35B779"
- - "#FDE725"
- - "#FF0000"
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: unitless
-
- - id: tornadoes-2024-dow-sw-harlan
- stacCol: tornadoes-2024-dow-sw-harlan
- name: DOW7 Spectrum Width (Harlan, IA)
- type: raster
- description: "Spectrum Width values (m/s) from the EF-3 Harlan, IA tornado on April 26, 2024 collected by DOW7. Scan taken at 23:08:12."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: rdpu
- nodata: -32768
- rescale:
- - 0
- - 25
- legend:
- type: gradient
- min: "0 m/s"
- max: "25 m/s"
- stops:
- - "#f7f4f9"
- - "#fddbc7"
- - "#f4a582"
- - "#d6604d"
- - "#b2182b"
- - "#67001f"
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: m/s
-
- - id: tornadoes-2024-dow-zdr-harlan
- stacCol: tornadoes-2024-dow-zdr-harlan
- name: DOW7 Differential Reflectivity (Harlan, IA)
- type: raster
- description: "Differential Reflectivity values (dB) from the EF-3 Harlan, IA tornado on April 26, 2024 collected by DOW7. Scan taken at 23:08:12."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: nipy_spectral
- nodata: -32768
- rescale:
- - -5
- - 7
- legend:
- type: gradient
- min: "-5 dB"
- max: "7 dB"
- stops:
- - "#000080"
- - "#0000FF"
- - "#00FFFF"
- - "#00FF00"
- - "#FFFF00"
- - "#FFA500"
- - "#FF0000"
- - "#800080"
- info:
- source: Doppler on Wheels (DOW)
- spatialExtent: Local
- temporalResolution: N/A
- unit: dB
-
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** April 26 and May 21, 2024
- - **Temporal Resolution:** 7 Seconds
- - **Spatial Extent:** Harlan and Greenfield, Iowa
- - **Spatial Resolution:** Varies
- - **Data Units:** Varies
- - **Data Type:** Research
-
-
-
-
- Maximum DOW-derived velocities from the EF-4 Greenfield, Iowa tornado on May 21, 2024.
-
-
-
-
-
-
-
-
- Comparison of DOW-collected correlation coefficient values from the Minden-Harlan, Iowa EF-3 tornado on April 26, 2024 as it passed northwest of Harlan.
-
-
-
-
-
- ### About
-
- The Doppler on Wheels (DOW) is a mobile radar system that provides high-resolution, ground-based data for severe weather events, including tornadoes, hurricanes, and other extreme meteorological phenomena. The data from DOW7 offers detailed, real-time insights into storm structure and evolution, supporting research, forecasting improvements, and in-depth analysis of atmospheric dynamics. Operated by the Center for Severe Weather Research, DOW data is instrumental in providing accurate measurements of wind velocities, precipitation patterns, and radar reflectivity, all with an unmatched level of mobility and precision.
-
-
- This dataset provides valuable insights into tornado formation, storm lifecycle analysis, and tornado vortex structure evolution by offering detailed radar observations from mobile platforms positioned directly in the storm's path. Such data enhances scientific understanding of storm dynamics and supports both operational and research-driven forecasting improvements.
-
-
-
-
-
-
-
-
- ### What the DOW Offers
-
- * High-Resolution Radar Observations: Detailed data with fine spatial and temporal resolution, enabling granular storm analysis.
-
- * Deployable Mobile Sampling: Unique, in situ data collection within storm paths, providing precise observations of storm dynamics.
-
- * Real-Time Data Collection: Near-instantaneous in situ measurements that support rapid-response severe weather research and nowcasting applications.
-
-
-
-
-
-
-
- ### Access the Data
-
- DOW data is not currently publicly hosted, but can be requested for research or educational purposes. Additionally, DOW facilities can be requested [HERE](http://www.cswr.org/contents/requestdows.php).
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- Wurman, J. and K. Kosiba, 2024: Doppler on Wheels (DOW). Center for Severe Weather Research, Boulder, CO, USA.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Wurman, J., Kosiba, K., Pereira, B., Robinson, P., Frambach, A., Gilliland, A., White, T., Aikins, J., Trapp, R. J., Nesbitt, S., Hanshaw, M. N., & Lutz, J. (2021). "The Flexible Array of Radars and Mesonets (FARM)." Bulletin of the American Meteorological Society, 102(8), E1499–E1525.
-
- Wurman, J., & Kosiba, K. (2024). "Very preliminary analysis of DOW data show >250 mph peak winds, possibly as high as 290, at 44 m (144 ft) above ground in Greenfield, IA." Doppler on Wheels Facility.
-
-
-
-
-
-
-
- ### Other Publications
-
- Wurman, J., and C. Alexander, 2005: The 30 May 1998 Spencer, South Dakota, storm. Part II: Comparison of observed damage and radar-derived winds in the tornadoes. Monthly Weather Review, 133, 97–119.
-
- Wurman, J., 2001: The multiple-vortex structure of a tornado. Weather and Forecasting, 16, 349-360.
-
- Bluestein, H. B., et al., 2018: The structure of tornadic supercells: Insights from mobile radar observations. Bulletin of the American Meteorological Society, 99, 1499-1525.
-
- Kumjian, M. R., Richardson, Y. P., Meyer, T., Kosiba, K. A., & Wurman, J. (2018). "Resonance Scattering Effects in Wet Hail Observed with a Dual-X-Band-Frequency, Dual-Polarization Doppler on Wheels Radar." Journal of Applied Meteorology and Climatology, 57(12), 2713–2731.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **The Hyperactive Spring 2024 Tornado Season**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
-
diff --git a/datasets/tornadoes2024-paths.data.mdx b/datasets/tornadoes2024-paths.data.mdx
deleted file mode 100644
index e125310e7b..0000000000
--- a/datasets/tornadoes2024-paths.data.mdx
+++ /dev/null
@@ -1,236 +0,0 @@
----
-id: tornadoes-2024-tracks
-isHidden: true
-name: "Spring 2024 Tornado Tracks"
-description: "Utilizing NWS tornado track data to highlight the active Spring 2024 season."
-media:
- src: ::file ./media/tornado-2024-cover.png
- alt: Wedge tornado passing southeast of Wapakoneta, Ohio on March 14, 2024.
- author:
- name: Jonny Glessner
- url: https://x.com/JonnyGlessner/status/1768424574855610777/photo/4
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - Habitat
- - Surface Meteorology
- - name: Source
- values:
- - NWS
-infoDescription: |
- ::markdown
- - **Temporal Extent:** March 1 - May 31, 2024
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 50 meters
- - **Data Type:** Research
-
-layers:
- - id: tornadoes-2024-polygons
- stacCol: tornadoes-2024-polygons
- name: Spring 2024 Tornadoes (Polygons)
- type: raster
- description: "This dataset shows official NWS tornado paths categorized by EF rating for each point in the track."
- initialDatetime: newest
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: tornado_ef_scale
- nodata: 255
-
- legend:
- type: categorical
- stops:
- - color: "#add8e6" # Light blue for EF0
- label: EF0
- - color: "#90ee90" # Green for EF1
- label: EF1
- - color: "#ffe71f" # Yellow for EF2
- label: EF2
- - color: "#ffa500" # Orange for EF3
- label: EF3
- - color: "#ff0000" # Red for EF4
- label: EF4
- - color: "#ff00ff" # Pink for EF5
- label: EF5
- info:
- source: National Weather Service (NWS)
- spatialExtent: CONUS
- temporalResolution: N/A
- unit: N/A
-
-
- - id: tornadoes-2024-paths
- stacCol: tornadoes-2024-paths
- name: Spring 2024 Tornadoes (Paths)
- type: raster
- description: "This dataset shows official NWS tornado center path lines categorized by maximum EF rating."
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: tornado_ef_scale
- nodata: 255
- legend:
- type: categorical
- stops:
- - color: "#b3bcc9" # Grey for EFUNK
- label: EFUNK
- - color: "#add8e6" # Light blue for EF0
- label: EF0
- - color: "#90ee90" # Green for EF1
- label: EF1
- - color: "#ffe71f" # Yellow for EF2
- label: EF2
- - color: "#ffa500" # Orange for EF3
- label: EF3
- - color: "#ff0000" # Red for EF4
- label: EF4
- - color: "#ff00ff" # Pink for EF5
- label: EF5
- compare:
- datasetId: ps-tornadoes-2024
- layerId: ps-tornadoes-2024-difference
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'dd LLL yyyy')}`;
- }
- info:
- source: National Weather Service (NWS)
- spatialExtent: CONUS
- temporalResolution: N/A
- unit: N/A
-
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** March 1 - May 31, 2024
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** CONUS
- - **Spatial Resolution:** 50 meters
- - **Data Type:** Research
-
-
-
-
- EF-3 tornado path through Lakeview, Ohio, from a local outbreak on March 14, 2024.
-
-
-
-
-
-
-
- ### About
-
- The National Weather Service’s (NWS) Damage Assessment Toolkit (DAT) is a pivotal geographic information system (GIS)-hosted dataset designed to support post-storm damage surveys conducted by meteorologists. This toolkit plays a crucial role in documenting and analyzing tornado and significant straight-line wind damage across affected areas. Ground-based surveys are carried out to capture this information, which is then geospatially referenced and uploaded to the DAT database.
-
- This dataset encompasses comprehensive elements such as tornado track centerlines, polygons depicting Enhanced Fujita (EF) scale ratings along tornado paths, and detailed descriptions with meteorological statistics for each logged damage location. In some cases, it also includes imagery collected by survey teams, adding further context to damage assessments. The comprehensive information in this dataset makes it invaluable for researchers, planners, and emergency responders.
-
-
-
-
-
-
-
-
- ### What the DAT Offers
-
- * Tornado Track Centerlines: Geospatial data capturing the precise paths of tornadoes, providing insights into their trajectory and extent.
-
- * Enhanced Fujita (EF) Scale Polygons: Detailed polygons of the EF rating at each location along a tornado’s path, offering a better understanding of the severity of the storm across different points.
-
- * Location-Specific Damage Descriptions: Comprehensive descriptions of damage at each surveyed point, paired with relevant meteorological statistics to offer deeper insight into storm impacts.
-
- * Damage Imagery: When available, surveyor-captured images provide visual context to logged damage points, further enhancing data interpretation and analysis.
-
-
-
-
-
-
- ### Access the Data
-
- Visit the [Storm Damage Viewer](https://apps.dat.noaa.gov/StormDamage/DamageViewer/) page to explore a GIS-hosted page that contains the DAT dataset.
-
-
-
-
-
-
-
- ### Citing this Dataset
-
- J. Parks Camp, NWSFO, Tallahassee, FL; and P. Kirkwood, J. G. LaDue, L. A. Schultz, and N. Parikh., National Weather Service Damage Assessment Toolkit: Transitioning to Operations, Abstract 9.1 presented at 2017 Annual Meeting, AMS, Seattle, Washington, 26 Jan.
-
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
-
- ### Key Publications
-
- Leonardo, D., 2011: Damage Assessment Toolkit business case analysis: NWS OSIP Project 08-024. NWS Rep., 16 pp., https://osip.nws.noaa.gov/osip/projectDetail.php?document=23295.
-
- Stellman, K., T. Brice, D. Hansing, A. Foster, C. Pieper, and K. Lander, 2009: How geographic information system software is improving the effectiveness of the National Weather Service. 89th Annual Meeting, New Orleans, LA, Amer. Meteor. Soc., 5A.11, http://ams.confex.com/ams/89annual/webprogram/Paper148642.html.
-
-
-
-
-
-
-
- ### Other Publications
-
- National Wind Institute, 2006: A recommendation for an enhanced Fujita scale (EF-scale). Texas Tech University Wind Science and Engineering Center Rep., 111 pp., www.depts.ttu.edu/nwi/Pubs/EnhancedFujitaScale/EFScale.pdf.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **The Hyperactive Spring 2024 Tornado Season**
-
-
-
-
-
-
-
- ## License
-
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
-
-
diff --git a/datasets/twsanomaly.data.mdx b/datasets/twsanomaly.data.mdx
deleted file mode 100644
index ebbd56898c..0000000000
--- a/datasets/twsanomaly.data.mdx
+++ /dev/null
@@ -1,221 +0,0 @@
----
-id: tws-anomaly
-name: 'Terrestrial Water Storage Anomaly and Trend'
-description: "TWS anomalies and trends modeled using data assimilation within Land Information System framework"
-media:
- src: ::file ./media/twsanomaly-globe.png
- alt: TWS anomalies from LIS outputs.
- author:
- name: NASA LIS
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA EIS
-infoDescription: |
- ::markdown
- Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water.
-layers:
- - id: lis-tws-anomaly
- stacCol: lis-tws-anomaly
- name: 'TWS Anomaly'
- type: raster
- description: 'TWS anomalies modeled using data assimilation within Land Information System framework'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- resampling: bilinear
- bidx: 1
- colormap_name: rdylbu
- rescale:
- - -200
- - 200
- compare:
- datasetId: tws-anomaly
- layerId: lis-tws-anomaly
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'dd LLL yyyy')} VS ${dateFns.format(compareDatetime, 'dd LLL yyyy')}`;
- }
- legend:
- type: gradient
- label: TWS Anomaly
- min: "-200"
- max: "200"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Daily
- unit: N/A
-
-
- - id: lis-tws-trend
- stacCol: lis-tws-trend
- name: 'TWS Anomaly Trend'
- type: raster
- description: 'Trends in TWS anomalies from LIS outputs'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- bidx: 1
- colormap_name: rdylbu
- rescale:
- - -1
- - 1
- nodata: -9999
- legend:
- type: gradient
- label: Trend in TWS Anomaly
- min: "-1"
- max: "1"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
-
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** Sept. 1, 2002 - Dec. 1, 2021
- - **Temporal Resolution:** Daily
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 10 km
- - **Data Units:** Millimeters (mm)
- - **Data Type:** Research
-
-
-
-
- Depleting TWS over California between 2002 (left) and 2021 (right), captured by LIS modeled TWS anomalies.
-
-
-
-
-
-
- ## Overview
- Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water.
-
-
-
-
-
-
-## Modeling TWS
-TWS is modeled using Noah-MP land surface model (LSM) within LIS framework by assimilating NASA earth observations of soil moisture from Soil Moisture Active Passive (SMAP), leaf area index from MODIS sensor, and TWS from GRACE/GRACE-FO. The modeled TWS is produced over global domain at a resolution of 10 km.
-
-
-
-
-
-
-
- TWS anomalies over India compared between Dec 2002 (left) and Dec 2021 (right)
-
-
-
-## Interpreting the data
-The TWS anomalies are calculated as differences of raw TWS with the climatology obtained over 2002-2021. Negative anomalies (blue values) denote lower than normal TWS and positive anomalies (red) indicate higher than normal TWS. The trend over time is thus deseasonalized and reflective of changes due to human impacts.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
-
- **Unraveling the Components of Coastal Risk**
- **A New View of the Global Water Cycle**
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
- ## Additional resources
- * [EIS Freshwater](https://freshwater.eis.smce.nasa.gov/)
-
- * [Land Information System](https://lis.gsfc.nasa.gov/)
-
- ### Explore the Missions
- * [GRACE-FO](https://gracefo.jpl.nasa.gov/data/grace-fo-data/)
-
- * [SMAP](https://smap.jpl.nasa.gov/)
-
- * [MODIS](https://modis.gsfc.nasa.gov/)
-
-
diff --git a/datasets/twsnonstationarity.data.mdx b/datasets/twsnonstationarity.data.mdx
deleted file mode 100644
index 5d6cb9a144..0000000000
--- a/datasets/twsnonstationarity.data.mdx
+++ /dev/null
@@ -1,146 +0,0 @@
----
-id: lis-tws-nonstationarity-index
-name: 'Global TWS Non-Stationarity Index'
-description: "The global Terrestrial Water Storage (TWS) non-stationarity index integrates the trend, seasonal shifts, and variability change of TWS for the period of 2003 - 2020."
-media:
- src: ::file ./media/twsanomaly-globe.png
- alt: TWS trend of anomalies from LIS outputs.
- author:
- name: NASA LIS
- url:
-taxonomy:
- - name: Topics
- values:
- - Water Resources
- - name: Subtopics
- values:
- - Water Cycle
- - Hydrology
- - name: Source
- values:
- - NASA EIS
-
-infoDescription: |
- ::markdown
- Terrestrial water storage (TWS) is defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow, ice, water stored in the vegetation, river and lake water.
-layers:
- - id: lis-tws-nonstationarity-index
- stacCol: lis-tws-nonstationarity-index
- name: 'TWS Non-Stationarity Index'
- type: raster
- description: 'TWS Non-Stationarity Index'
- zoomExtent:
- - 0
- - 11
- sourceParams:
- bidx: 1
- colormap_name: rdylbu
- rescale:
- - -1
- - 1
- nodata: -9999
- legend:
- type: gradient
- label: TWS Non-Stationarity Index
- min: "-1"
- max: "1"
- stops:
- - "#a50026"
- - "#f46d43"
- - "#fee090"
- - "#e0f3f8"
- - "#74add1"
- - "#313695"
- info:
- source: NASA
- spatialExtent: Global
- temporalResolution: Annual
- unit: N/A
----
-
-
-
- ## Dataset Details
- - **Temporal Extent:** Jan. 1, 2003 - Jan. 1, 2020
- - **Spatial Extent:** Global
- - **Spatial Resolution:** 10 km
- - **Data Units:** Standardized anomaly
- - **Data Type:** Research
-
-
-
-
- Terrestrial water storage (TWS) and Gross Primary Productivity (GPP) trends between 2003-2020.
-
- Comparison of decadally-averaged daytime land surface temperature (LST) between 2000-2009 and 2010-2019 showing urban heating in the Houston Metropolitan Area.
-
-
-
-
-
-
-
-
- Comparison of decadally-averaged Normalized Difference Vegetation Index (NDVI) between 2000-2009 and 2010-2019 showing green space reduction in the Houston Metropolitan Area.
-
-
-
- ##### Normalized Difference Vegetation Index
- - **Temporal Extent:** 2000-2019
- - **Temporal Resolution:** Decadal
- - **Spatial Extent:** Houston, Texas
- - **Spatial Resolution:** 250 m
- - **Data Units:** Unitless
- - **Data Type:** Research
- - **Data Latency:** N/A
-
-
-
-
-
- ## Overview
-
- Urban heat islands (UHIs) are no longer merely academic concepts; they’re palpable urban challenges. In rapdily urbanizing cities such as Houston, understanding the dynamics of land surface temperature (LST) is not just about decoding satellite data, but comprehending its implications for urban planning, health, and socioeconomic dynamics. Leveraging data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS)) platform, we delve into Houston’s changing landscape over two decades by examining multi-decadal changes both LST and the Normalized Difference Vegetation Index (NDVI), offering a technical perspective on this urban phenomenon. LST is the temperature of the earth’s surface derived from the Terra satellite that houses the MODIS instrumentation, encompassing both natural terrains and man-made infrastructures. Unlike ambient air temperature, which gauges the immediate atmospheric conditions we feel, LST provides a granular temperature profile of surfaces from park greens to asphalt roads. Exmaining this in conjunction with NDVI gives an idea of the changing access to green space in sprawling urban spaces such as Houston.
-
-
-
-
-
- ## Data Acquisition
-
- Terra has been instrumental in capturing this data. This platform, orbiting Earth, scans our planet in multiple spectral bands, allowing for a detailed analysis of LST and NDVI. The decadal periods of 2000-20009 and 2010-2019 were examined specifically to study the growth of Houston's UHI.
-
- Comparative analysis of LST data from the two decades indicate a tangible uptick in surface temperatures, especially in Houston’s southwestern regions. Urban expansion is likely culprit, with infrastructural growth leading to increased heat absorption and radiation. This phenomenon, known as the urban heat island effect, can intensify local temperatures, leading to a cascade of socio-environmental effects.
-
- Users can access MODIS data for anywhere across the globe [here](https://modis.gsfc.nasa.gov/data/), or click 'Explore Data' at the top of this page for a quick examination of the specific data used in this study.
-
-
-
-
-
- ## Importance of Heat Stress Datasets
-
- MODIS LST and NDVI data serves as a crucial pointer for urban planners, environmentalists, and policymakers. By understanding the nexus of urban heat, infrastructure, and socioeconomics, we can shape urban features that are not only sustainable but also equitable. As Houston continues to rapidly urbanize, it has the potential to redefine urban resilience in the face of escalating heat challenges.
-
-
-
-
-
- ## Disclaimer
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
- ## Key Publications
-
- Didan, K., Munoz, A.B., Solano, R., and Huete, A. (2015). MODIS vegetatin index user's guide (MOD13 series). University of Arizona Veg. Index Phenol. Lab
-
- Wan, Z. (2014). New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. *Remote Sensing of the Environment, 140*, 36-45. https://doi.org/10.1016/j.rse.2013.08.027
-
- **The citation below is a peer-reviewed study that stemmed from this research, written by the authors of the associated data story:**
-
- Blackford, A., Cowan, T., Nair, U., Phillips, C., Kaulfus, A., and Freitag, B. (2024). Synergy of urban heat, pollution, and social vulnerability in one of America's most rapidly growing cities: Houston, we have a problem. *GeoHealth, 8*, e2024GH001079. https://doi.org/10.1029/2024GH001079
-
-
-
-
-
- ## Data Stories Using This Dataset
- **[Implications for Heat Stress](https://www.earthdata.nasa.gov/dashboard/stories/urban-heating)**
-
-
-
-
-
- ## License
- [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0)
-
-
-
diff --git a/datasets/viirs-blackmarble-lights.data.mdx b/datasets/viirs-blackmarble-lights.data.mdx
deleted file mode 100644
index 4d9782b0c6..0000000000
--- a/datasets/viirs-blackmarble-lights.data.mdx
+++ /dev/null
@@ -1,273 +0,0 @@
----
-id: nighttime-lights-SE
-name: 'NASA Black Marble Night Lights (Select Events)'
-description: "NASA’s Black Marble night lights dataset provides satellite images of Earth at night, capturing the light pollution from cities, roads, and other human activity"
-media:
- src: ::file ./media/nighttime-lights--dataset-cover.jpg
- alt: Satellite image of Earth at night.
- author:
- name: NASA Earth Observatory
- url: https://earthobservatory.nasa.gov/images/90008/night-light-maps-open-up-new-applications
-taxonomy:
- - name: Topics
- values:
- - Natural Disasters
- - name: Subtopics
- values:
- - COVID-19
- - Land Use
- - Urban
- - name: Source
- values:
- - Black Marble
-
-infoDescription: |
- ::markdown
- Nightlights data are collected by the [Visible Infrared Radiometer Suite (VIIRS) Day/Night Band (DNB)](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/) on the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) platform, a joint National Oceanic and Atmospheric Administration (NOAA) and NASA satellite. The images are produced by [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/) products suite. All data are calibrated daily, corrected, and validated with ground measurements for science-ready analysis.
-layers:
-
- - id: nightlights-derecho
- stacCol: nightlights-derecho
- name: Black Marble Night Lights (Northern Plains Derecho Damage)
- type: raster
- description: 'Black Marble night lights imagery of derecho damage in the Northern Plains region that occured May 12th, 2022.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - -255
- - 255
- legend:
- type: gradient
- min: "-255"
- max: "255"
- stops:
- - "#ff0000" #Pure Red
- - "#ff5454" #Bright Coral Red
- - "#ffaaaa" #Soft Pinkish-Red
- - "#fffefe" #Near White
- - "#aaaaff" #Pale Blue-Violet
- - "#5454ff" #Bright Periwinkle
- - "#0000ff" #Pure Blue
- info:
- source: NASA VIIRS
- spatialExtent: Regional
- temporalResolution: N/A
- unit: Artificial Light Emissions Difference (W cm^-2 sr^-1)
-
-
- - id: greenville-nightlights-tornadoes-2024
- stacCol: greenville-nightlights-tornadoes-2024
- name: Black Marble Night Lights (Greenville OH Tornado Damage)
- type: raster
- description: 'Black Marble night lights imagery of tornado damage at Greenville, Ohio in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- rescale:
- - 0,255
- resampling: bilinear
- asset_bidx: cog_default|1,2,3
- compare:
- datasetId: nighttime-lights
- layerId: greenville-nightlights-tornadoes-2024
- mapLabel: |
- ::js ({ dateFns, datetime, compareDatetime }) => {
- return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
- }
- info:
- source: NASA Black Marble
- spatialExtent: Regional
- temporalResolution: N/A
- unit: Artificial Light Emissions (W cm^-2 sr^-1)
-
-
- - id: lakeview-nightlights-tornadoes-2024
- stacCol: lakeview-nightlights-tornadoes-2024
- name: Black Marble Night Lights (Lakeview OH Tornado Damage; Difference)
- type: raster
- description: 'Black Marble night lights imagery of tornado damage at Lakeview, Ohio in the spring 2024 tornado season.'
- zoomExtent:
- - 0
- - 20
- sourceParams:
- colormap_name: bwr
- rescale:
- - -150
- - 150
- nodata: -999
- legend:
- type: gradient
- min: "-150"
- max: "150"
- stops:
- - "#4575b4"
- - "#91bfdb"
- - "#e0f3f8"
- - "#ffffff"
- - "#fee090"
- - "#fc8d59"
- - "#d73027"
- info:
- source: NASA Black Marble
- spatialExtent: Regional
- temporalResolution: N/A
- unit: Artificial Light Emissions Difference (W cm^-2 sr^-1)
-
----
-
-
-
-
- ## Dataset Details
- - **Temporal Extent:** 2022-2024
- - **Temporal Resolution:** Inconsistent
- - **Spatial Extent:** Select sites across CONUS
- - **Spatial Resolution:** 500 meters
- - **Data Units:** W cm^-2 sr^-1
- - **Data Type:** Research
-
-
-
-
- Comparison of artificial light emissions in Greenville, Ohio before and after a tornado strike on May 7, 2024.
-
-
-
-
-
-
-
- ## About
- Images of Earth at night give us an extraordinary view of human activity over time. The nighttime environment illuminates Earth features, including city infrastructure, lightning flashes, fishing boats navigating open water, gas flares, aurora, and natural hazards, such as lava flowing from an active volcano. Paired with the moonlight, researchers can also spot snow and ice, as well as other reflective surfaces that allow nighttime land and ocean analysis.
-
-
-
-
-
-
-
-
-
- ### What Night Lights Data Offers
-
- * High-Resolution Nighttime Imagery: Captures detailed views of light emissions at night, providing a clear picture of human activities and their distribution across the landscape.
-
- * Temporal Change Analysis: Enables examination of changes in night lights over time, which is crucial for understanding the impacts of disasters, urban growth, and other factors on nighttime visibility.
-
- * Disaster Impact Assessment: Particularly useful for assessing the effects of natural disasters, this dataset highlights areas affected by power outages, offering insights into recovery dynamics and areas in need of intervention.
-
-
-
-
-
-
-
- ## Interpreting the data
- Each spotlight city has a slider for turning night lights on and off. The darker purple indicates fewer night lights, while the lighter yellow indicates more. By comparing regions before and after guidelines to shelter-in-place began, researchers are able to visualize the extent to which social distancing measures affected various economic activities based on whether light pollution increased or decreased, which highways were shut down, and which cities stayed the same.
-
- The products featured are 500-meter (VNP46) and 30-meter Black Marble High Definition (HD) nighttime lights. Black Marble HD downscales radiances from the 500-meter product to street level using optical imagery from Landsat 8, a NASA and U.S. Geological Survey (USGS) satellite, along with OpenStreetMap ancillary layers. This helps visualize neighborhoods and commercial centers that have less activity – or closures – due to social distancing restrictions.
-
-
-
-
-
-
- ### Access the Data
-
- Visit NASA's [Black Marble home page](https://blackmarble.gsfc.nasa.gov) to explore options for data access.
-
-
-
-
-
-
-
-
- ### Key Publications
-
- Román, M. O., Z. Wang, Q. Sun, V. Kalb, S. D. Miller, A. Molthan, L. Schultz, J. Bell, E. C. Stokes, B. Pandey, K. C. Seto, D. Hall, T. Oda, R. E. Wolfe, G. Lin, N. Golpayegani, S. Devadiga, C. Davidson, S. Sarkar, C. Praderas, and E. J. Masuoka, 2018: NASA's Black Marble nighttime lights product suite. *Remote Sensing of Environment*, 210, 113–143. https://doi.org/10.1016/j.rse.2018.03.017
- Wang, Z., M. O. Román, V. L. Kalb, S. D. Miller, J. Zhang, and R. M. Shrestha, 2021: Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data. *Remote Sensing of Environment*, 267, 112557.
-
-
-
-
-
-
-
- ## Data Stories Using This Dataset
- - **The Hyperactive Spring 2024 Tornado Season**
-
- - **NASA Data Fusion Analysis of Derechos and Their Impact on Rural America**
-
-
-
-
-
-
-
-
-
- ## Credits
- Black Marble data courtesy of [Universities Space Research Association (USRA) Earth from Space Institute (EfSI)](https://www.usra.edu/efsi-our-mission) and NASA Goddard Space Flight Center’s [Terrestrial Information Systems Laboratory](https://science.gsfc.nasa.gov/earth/terrestrialinfo/) using VIIRS day-night band data from the Suomi National Polar-orbiting Partnership and Landsat-8 Operational Land Imager (OLI) data from the U.S. Geological Survey.
-
-
-
-
-
-
- ## Limitations of use
-
- NASA data and products are freely available to federal, state, public, non-profit and commercial users. This information can be experimental- or research-grade data products and may not be appropriate for operational use. These NASA data products and services are intended to aid decision makers and enhance situational awareness, but these data are not guaranteed to be consistently available or routinely updated. Please cite the information according to the direction provided in the metadata.
-
-
-
-
-
-
- ## Disclaimer
-
- All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
-
-
-
- ## License
-
- [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC0 1.0)
-
-
-
-
-
-
- ## Additional resources
- ##### NASA Features
-
- * [Nighttime Images Capture Change In China](https://earthobservatory.nasa.gov/images/146481/nighttime-images-capture-change-in-china)
-
- ##### Explore the data
-
- * [Nighttime Images Show Changes In Human Activity](https://www.earthdata.nasa.gov/news/feature-articles/nighttime-images-show-changes-human-activity)
-
- ##### Explore the Missions
-
- * [NASA’s Black Marble](https://blackmarble.gsfc.nasa.gov/)
- * [Suomi National Polar-orbiting Partnership (Suomi NPP)](https://www.nasa.gov/mission_pages/NPP/main/index.html)
-
-
\ No newline at end of file
diff --git a/package.json b/package.json
index 8de1749b17..ced6d459c6 100644
--- a/package.json
+++ b/package.json
@@ -2,7 +2,7 @@
"name": "veda-config",
"description": "Configuration for Veda",
"version": "0.21.2",
- "source": "./.veda/ui/apps/dashboard-parcel/index.html",
+ "source": "./.veda/ui/app/index.html",
"license": "Apache-2.0",
"scripts": {
"dev": "yarn clean && yarn serve",
@@ -15,7 +15,7 @@
},
"targets": {
"veda-app": {
- "source": "./.veda/ui/apps/dashboard-parcel/index.html",
+ "source": "./.veda/ui/app/index.html",
"context": "browser"
}
},
@@ -50,7 +50,7 @@
"react": "./.veda/ui/node_modules/react",
"@mdx-js/react": "./.veda/ui/node_modules/@mdx-js/react",
"$veda-ui": "./.veda/ui/node_modules",
- "$veda-ui-scripts": "./.veda/ui/packages/veda-ui/src"
+ "$veda-ui-scripts": "./.veda/ui/app/scripts"
},
"parcelIgnore": [
".*/meta/"
diff --git a/veda.config.js b/veda.config.js
index f106ff56e9..ca8af815b3 100644
--- a/veda.config.js
+++ b/veda.config.js
@@ -67,4 +67,11 @@ module.exports = {
title: "Cookie Consent",
copy: "We use cookies to enhance your browsing experience and to help us understand how our website is used. These cookies allow us to collect data on site usage and improve our services based on your interactions. To learn more about it, see our [Privacy Policy](https://www.nasa.gov/privacy/#cookies).",
},
+ siteAlert: {
+ content: `Placeholder banner text.`,
+ expires: '2025-11-12T12:00:00-04:00',
+ type: 'emergency',
+ slim: true,
+ showIcon: true
+ },
};
+ 
+ 
+