diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..9d34897
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,195 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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diff --git a/config.yml b/config.yml
index 4d6416d..f90a1e9 100644
--- a/config.yml
+++ b/config.yml
@@ -7,8 +7,6 @@ DATASETS:
- cci_biomass_sd
- gedi_l4b
- gedi_l4b_se
- - nasa_jpl
- - nasa_jpl_se
- paraguay_forest_mask
- paraguay_tree_cover
- paraguay_estimated_biomass
@@ -27,5 +25,4 @@ PRODUCTS:
- cci_biomass
- gedi_l4b
- icesat2_boreal
- - nasa_jpl
- nceo_africa
diff --git a/country_pilots/japan/country_pilot.json b/country_pilots/japan/country_pilot.json
index df211b7..71924ed 100644
--- a/country_pilots/japan/country_pilot.json
+++ b/country_pilots/japan/country_pilot.json
@@ -43,11 +43,8 @@
{
"id": "cci_biomass"
},
- {
- "id": "nasa_jpl"
- },
{
"id": "gedi_l4b"
}
]
-}
\ No newline at end of file
+}
diff --git a/country_pilots/japan/summary.html b/country_pilots/japan/summary.html
index d4f31d9..eeadd61 100644
--- a/country_pilots/japan/summary.html
+++ b/country_pilots/japan/summary.html
@@ -15,7 +15,7 @@
- As part of the biomass harmonization collaboration, JAXA provides the global ALOS (for the time period 2007-2010) and ALOS-2 (2015-present) radar imagery that constitute the key input data for the CCI Biomass, JPL, and NCEO Africa biomass products.
+ As part of the biomass harmonization collaboration, JAXA provides the global ALOS (for the time period 2007-2010) and ALOS-2 (2015-present) radar imagery that constitute the key input data for the CCI Biomass and NCEO Africa biomass products.
\ No newline at end of file
+
diff --git a/country_pilots/paraguay/country_pilot.json b/country_pilots/paraguay/country_pilot.json
index e8f06d2..cf5c10c 100644
--- a/country_pilots/paraguay/country_pilot.json
+++ b/country_pilots/paraguay/country_pilot.json
@@ -52,11 +52,8 @@
{
"id": "cci_biomass"
},
- {
- "id": "nasa_jpl"
- },
{
"id": "gedi_l4b"
}
]
-}
\ No newline at end of file
+}
diff --git a/country_pilots/peru/country_pilot.json b/country_pilots/peru/country_pilot.json
index a075743..8a4ac47 100644
--- a/country_pilots/peru/country_pilot.json
+++ b/country_pilots/peru/country_pilot.json
@@ -43,9 +43,6 @@
{
"id": "cci_biomass"
},
- {
- "id": "nasa_jpl"
- },
{
"id": "gedi_l4b"
}
diff --git a/country_pilots/solomon_islands/country_pilot.json b/country_pilots/solomon_islands/country_pilot.json
index 0ac9667..f588896 100644
--- a/country_pilots/solomon_islands/country_pilot.json
+++ b/country_pilots/solomon_islands/country_pilot.json
@@ -43,11 +43,8 @@
{
"id": "cci_biomass"
},
- {
- "id": "nasa_jpl"
- },
{
"id": "gedi_l4b"
}
]
-}
\ No newline at end of file
+}
diff --git a/country_pilots/wales/country_pilot.json b/country_pilots/wales/country_pilot.json
index b2fc07f..ae19614 100644
--- a/country_pilots/wales/country_pilot.json
+++ b/country_pilots/wales/country_pilot.json
@@ -43,11 +43,8 @@
{
"id": "cci_biomass"
},
- {
- "id": "nasa_jpl"
- },
{
"id": "gedi_l4b"
}
]
-}
\ No newline at end of file
+}
diff --git a/datasets/CCI_BIOMASS.json b/datasets/CCI_BIOMASS.json
index 0fbbc5b..ab925d4 100644
--- a/datasets/CCI_BIOMASS.json
+++ b/datasets/CCI_BIOMASS.json
@@ -9,7 +9,7 @@
"source": {
"type": "raster",
"tiles": [
- "https://titiler.maap-project.org/mosaics/f343756d-bf15-4095-a8f3-f4fcbb26b5f9/tiles/{z}/{x}/{y}?rescale=0,400&bidx=1&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
+ "https://titiler-pgstac.maap-project.org/mosaic/4f2b21b1d359e0169117e84c35ed5f7d/tiles/{z}/{x}/{y}?assets=estimates&rescale=0,400&bidx=1&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
]
},
"legend": {
diff --git a/datasets/CCI_BIOMASS_SD.json b/datasets/CCI_BIOMASS_SD.json
index 18db9cc..ecc44fa 100644
--- a/datasets/CCI_BIOMASS_SD.json
+++ b/datasets/CCI_BIOMASS_SD.json
@@ -9,7 +9,7 @@
"source": {
"type": "raster",
"tiles": [
- "https://titiler.maap-project.org/mosaics/f343756d-bf15-4095-a8f3-f4fcbb26b5f9/tiles/{z}/{x}/{y}?rescale=0,500&bidx=2&colormap_name=reds&color_formula=gamma r {gamma}"
+ "https://titiler-pgstac.maap-project.org/mosaic/4f2b21b1d359e0169117e84c35ed5f7d/tiles/{z}/{x}/{y}?assets=standard_deviation&rescale=0,500&bidx=2&colormap_name=reds&color_formula=gamma r {gamma}"
]
},
"legend": {
diff --git a/datasets/GEDI_L4B.json b/datasets/GEDI_L4B.json
index b91f058..11b62c6 100644
--- a/datasets/GEDI_L4B.json
+++ b/datasets/GEDI_L4B.json
@@ -9,7 +9,7 @@
"source": {
"type": "raster",
"tiles": [
- "https://titiler.dit.maap-project.org/cog/tiles/{z}/{x}/{y}.png?url=s3://ornl-cumulus-prod-protected/gedi/GEDI_L4B_Gridded_Biomass/data/GEDI04_B_MW019MW138_02_002_05_R01000M_MU.tif&rescale=0,400&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
+ "https://titiler.maap-project.org/cog/tiles/{z}/{x}/{y}.png?url=s3://ornl-cumulus-prod-protected/gedi/GEDI_L4B_Gridded_Biomass_V2_1/data/GEDI04_B_MW019MW223_02_002_02_R01000M_MU.tif&rescale=0,400&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
]
},
"legend": {
diff --git a/datasets/GEDI_L4B_SE.json b/datasets/GEDI_L4B_SE.json
index 63beb54..b9ef47c 100644
--- a/datasets/GEDI_L4B_SE.json
+++ b/datasets/GEDI_L4B_SE.json
@@ -9,7 +9,7 @@
"source": {
"type": "raster",
"tiles": [
- "https://titiler.dit.maap-project.org/cog/tiles/{z}/{x}/{y}.png?url=s3://ornl-cumulus-prod-protected/gedi/GEDI_L4B_Gridded_Biomass/data/GEDI04_B_MW019MW138_02_002_05_R01000M_SE.tif&rescale=0,310&colormap_name=reds&color_formula=gamma r {gamma}"
+ "https://titiler.maap-project.org/cog/tiles/{z}/{x}/{y}.png?url=s3://ornl-cumulus-prod-protected/gedi/GEDI_L4B_Gridded_Biomass_V2_1/data/GEDI04_B_MW019MW223_02_002_02_R01000M_SE.tif&rescale=0,310&colormap_name=reds&color_formula=gamma r {gamma}"
]
},
"legend": {
diff --git a/datasets/ICESat2_boreal_biomass.json b/datasets/ICESat2_boreal_biomass.json
index 104ca93..9cc8786 100644
--- a/datasets/ICESat2_boreal_biomass.json
+++ b/datasets/ICESat2_boreal_biomass.json
@@ -10,7 +10,7 @@
"type": "raster",
"minzoom": 7,
"tiles": [
- "https://titiler.maap-project.org/mosaics/e7027568-3a10-472d-a96b-3d0c81aadc6d/tiles/{z}/{x}/{y}.png?bidx=1&rescale=0,400&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
+ "https://titiler.maap-project.org/mosaics/cb39d9f9-4b7b-4812-a350-629f9f27fa3a/tiles/{z}/{x}/{y}.png?bidx=1&rescale=0,400&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
]
},
"legend": {
diff --git a/datasets/ICESat2_boreal_biomass_reduced.json b/datasets/ICESat2_boreal_biomass_reduced.json
index 79d955a..29153d8 100644
--- a/datasets/ICESat2_boreal_biomass_reduced.json
+++ b/datasets/ICESat2_boreal_biomass_reduced.json
@@ -10,7 +10,7 @@
"type": "raster",
"maxzoom": 7,
"tiles": [
- "https://titiler.maap-project.org/cog/tiles/{z}/{x}/{y}.png?url=s3://maap-ops-workspace/shared/nathanmthomas/DPS_tile_lists/AGB/c2020/map_boreal_2022_rh_noground_v4/AGB_tindex_average_4500m.tif&bidx=1&rescale=0,400&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
+ "https://titiler.maap-project.org/cog/tiles/{z}/{x}/{y}.png?url=s3://nasa-maap-data-store/file-staging/dashboard-datasets/icesat2-boreal/AGB_tindex_average_4500m.tif&bidx=1&rescale=0,400&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
]
},
"legend": {
diff --git a/datasets/ICESat2_boreal_biomass_se.json b/datasets/ICESat2_boreal_biomass_se.json
index 4562e4b..d27ddea 100644
--- a/datasets/ICESat2_boreal_biomass_se.json
+++ b/datasets/ICESat2_boreal_biomass_se.json
@@ -10,7 +10,7 @@
"type": "raster",
"minzoom": 8,
"tiles": [
- "https://titiler.maap-project.org/mosaics/e7027568-3a10-472d-a96b-3d0c81aadc6d/tiles/{z}/{x}/{y}.png?bidx=2&rescale=0%2C20&colormap_name=reds&color_formula=gamma r {gamma}"
+ "https://titiler.maap-project.org/mosaics/cb39d9f9-4b7b-4812-a350-629f9f27fa3a/tiles/{z}/{x}/{y}.png?bidx=2&rescale=0%2C20&colormap_name=reds&color_formula=gamma r {gamma}"
]
},
"legend": {
diff --git a/datasets/nasa_jpl.json b/datasets/nasa_jpl.json
deleted file mode 100644
index 007fb0e..0000000
--- a/datasets/nasa_jpl.json
+++ /dev/null
@@ -1,36 +0,0 @@
-{
- "id": "nasa_jpl",
- "name": "NASA JPL Global Above Ground Biomass Mean Prediction 2020",
- "type": "raster",
- "swatch": {
- "color": "#189C54",
- "name": "Dark Green"
- },
- "source": {
- "type": "raster",
- "tiles": [
- "https://titiler.maap-project.org/mosaics/f0e6f941-4e50-46b3-9463-11b3c2181662/tiles/{z}/{x}/{y}?bidx=1&max_size=1024&resampling_method=nearest&rescale=0,400&return_mask=true&colormap_name=gist_earth_r&color_formula=gamma r {gamma}"
- ]
- },
- "legend": {
- "type": "gradient-adjustable",
- "min": 0,
- "max": 400,
- "units": "Mg/ha",
- "stops": [
- "#fdfbfb",
- "#e3c3b5",
- "#c9a87a",
- "#bab060",
- "#9db059",
- "#76a652",
- "#45994a",
- "#3a8c66",
- "#2e7c7f",
- "#1f567b",
- "#0f2577",
- "#000000"
- ]
- },
- "info": "Global aboveground biomass (AGB) density estimates (unit: Mg/ha) derived from a combination of Landsat-8 composites, ALOS-2 PALSAR-2 backscatter (2019-2020) data and SRTM DEM at 100m spatial resolution, trained with samples from the spaceborne lidar data of ICESAT-1, airborne laser scanning (ALS) data, and field inventory data. The boosting tree machine learning model was applied to predict wall-to-wall AGB inferred from satellite data."
-}
diff --git a/datasets/nasa_jpl_se.json b/datasets/nasa_jpl_se.json
deleted file mode 100644
index b34be93..0000000
--- a/datasets/nasa_jpl_se.json
+++ /dev/null
@@ -1,36 +0,0 @@
-{
- "id": "nasa_jpl_se",
- "name": "NASA JPL Global Above Ground Biomass Mean Prediction 2020 SE",
- "type": "raster",
- "swatch": {
- "color": "#c0392b",
- "name": "Pomegranate"
- },
- "source": {
- "type": "raster",
- "tiles": [
- "https://titiler.maap-project.org/mosaics/210e8e5d-35c0-4549-8fc1-2b2145ad6ecf/tiles/{z}/{x}/{y}?bidx=1&max_size=1024&resampling_method=nearest&rescale=0,150&return_mask=true&colormap_name=reds&nodata=0&color_formula=gamma r {gamma}"
- ]
- },
- "legend": {
- "type": "gradient-adjustable",
- "min": 0,
- "max": 150,
- "units": "Mg/ha",
- "stops": [
- "#fff5f0",
- "#fee6da",
- "#fdcfbc",
- "#fcb498",
- "#fc9676",
- "#fb7959",
- "#f7593f",
- "#ec382b",
- "#d21e20",
- "#b61319",
- "#940b13",
- "#67000d"
- ]
- },
- "info": "The uncertainty map in the AGB 2020 product was produced using quantile regression to calculate the lower- and upper-bound of the estimation for each pixel, such that the 2.5-percentile and 97.5-percentile of the AGB estimation were predicted. The error map was produced by assuming that the error distribution is Gaussian. Therefore 95% of the predictions are approximately ±2 SD."
-}
diff --git a/products/GEDI_L4B/summary.html b/products/GEDI_L4B/summary.html
index 37f3849..e605a3d 100644
--- a/products/GEDI_L4B/summary.html
+++ b/products/GEDI_L4B/summary.html
@@ -2,10 +2,10 @@
The Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km estimates of mean aboveground biomass density (AGBD). NASA's GEDI mission is a full waveform lidar instrument aboard the ISS. It launched in December 2018 and has been collecting 3D Earth surface and vegetation samples of temperate and tropical forests since April, 2019. GEDI is the first satellite lidar designed specifically to measure vegetation structure and biomass, filling a critical data gap. Each laser illuminates ~25 m on the ground, and measures tree heights and volumes within those 25 m areas. GEDI estimates biomass by relating lidar measurements to biomass collected in forest plots across the globe.
- The GEDI L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 138 ending on 2021-08-04. The GEDI L4A parametric footprint biomass models convert each high-quality waveform to an AGBD prediction, and the L4B algorithm uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD and the standard error of the mean. The GEDI L4B product is 1 km spatial resolution and the gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) and Dubayah et al. (2022) describe the hybrid model-based mode of inference used, where estimates of the standard error of the mean account for both GEDI L4A modeling uncertainty and uncertainty related to how the 1 km cells are sampled by GEDI's observations (as opposed to making wall-to-wall observations).
+ The Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 223 ending on 2023-03-16. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) and describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.
- The JPL 2020 map is the global aboveground biomass (AGB) density estimates (unit: Mg/ha) derived from a combination of Landsat-8 composites, ALOS PALSAR backscatter (2019-2020) data and SRTM DEM at 100m spatial resolution, trained with samples from the spaceborne lidar data of ICESAT-1, airborne laser scanning (ALS) data, and field inventory data. We applied the boosting tree machine learning model to predict wall-to-wall AGB inferred from satellite data.
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- The product shared on the Multi-Mission Algorithm and Analysis Platform (MAAP) is separated continentally, including Africa, Asia, Europe, N America, Oceania, and S America. One can merge the 6 files to obtain the global mosaic.
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