diff --git a/cog_transformation/cms-global-map-mangrove-canopy.ipynb b/cog_transformation/cms-global-map-mangrove-canopy.ipynb new file mode 100644 index 000000000..aa8a6241b --- /dev/null +++ b/cog_transformation/cms-global-map-mangrove-canopy.ipynb @@ -0,0 +1,894 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Code used to transform [Global Mangrove Distribution, Aboveground Biomass, and Canopy Height](https://daac.ornl.gov/CMS/guides/CMS_Global_Map_Mangrove_Canopy.html) from .tif to Cloud Optimized Geotiff.\n", + "\n", + "-Author: Kyle Lesinger" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import json\n", + "import tempfile\n", + "import boto3\n", + "import rasterio\n", + "import rioxarray as rxr\n", + "import s3fs\n", + "from rasterio.warp import calculate_default_transform, reproject, Resampling\n", + "import botocore\n", + "from pathlib import Path\n", + "from dotenv import load_dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\n", + " \"data_acquisition_method\": \"s3\",\n", + " \"raw_data_bucket\" : \"ghgc-data-store-dev\",\n", + " \"raw_data_prefix\": \"coastal-observatory/data\",\n", + " \"cog_data_bucket\": \"ghgc-data-store-dev\",\n", + " \"cog_data_prefix\": \"transformed_cogs/CMS_Global_Map_Mangrove_Canopy_Biomass\",\n", + " \"local_output_dir\": \"output/cms-global-map-mangrove-biomass\", # Local directory to save COGs\n", + " \"transformation\": {}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Load environment variables from .env file\n", + "load_dotenv()\n", + "\n", + "# AWS Credentials from environment variables\n", + "AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID')\n", + "AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')\n", + "AWS_SESSION_TOKEN = os.getenv('AWS_SESSION_TOKEN')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Approach\n", + "\n", + "1.) Read .tif files from S3 bucket\n", + "2.) Convert to COGs on local drive\n", + "3.) Move converted COGs to their final S3 location" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "session = boto3.session.Session()\n", + "s3_client = boto3.client(\n", + " 's3',\n", + " aws_access_key_id=AWS_ACCESS_KEY_ID,\n", + " aws_secret_access_key=AWS_SECRET_ACCESS_KEY,\n", + " aws_session_token = AWS_SESSION_TOKEN\n", + "\n", + ")\n", + "\n", + "bucket_name = config[\"cog_data_bucket\"]\n", + "raw_data_bucket = config[\"raw_data_bucket\"]\n", + "raw_data_prefix= config[\"raw_data_prefix\"]\n", + "\n", + "cog_data_bucket = config['cog_data_bucket']\n", + "cog_data_prefix= config[\"cog_data_prefix\"]\n", + "\n", + "fs = s3fs.S3FileSystem()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['coastal-observatory/data/Mangrove_agb_AndamanAndNicobar.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Angola.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Anguilla.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_AntiguaAndBarbuda.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Aruba.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Australia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Bahamas.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Bahrain.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Bangladesh.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Barbados.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Belize.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Benin.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Brazil.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_BritishVirginIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Brunei.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Cambodia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Cameroon.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_CarribeanCaymanIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_China.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Colombia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Comoros.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_CostaRica.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_CoteDivoire.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Cuba.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_DemocraticRepublicOfCongo.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Djibouti.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_DominicanRepublic.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_EcuadorWithGalapagos.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Egypt.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_ElSalvador.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_EquatorialGuinea.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Eritrea.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_EuropaIsland.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Fiji.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Fiji2.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_FrenchGuyana.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_FrenchPolynesia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Gabon.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Gambia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Ghana.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Grenada.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Guadeloupe.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Guam.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Guatemala.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Guinea.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_GuineaBissau.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Guyana.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Haiti.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Hawaii.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Honduras.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_HongKong.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_India.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Indonesia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Iran.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Jamaica.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Japan.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Kenya.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Liberia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Macau.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Madagascar.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Malaysia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Martinique.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Mauritania.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Mayotte.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Mexico.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Micronesia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Mozambique.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Myanmar.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_NewCaledonia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Newzealand.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Nicaragua.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Nigeria.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_NorthernMarianaIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Oman.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Pakistan.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Palau.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Panama.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_PapuaNewGuinea.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Peru.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Philipines.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_PuertoRico.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Qatar.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_ReunionAndMauritius.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SaintKittsAndNevis.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SaintLucia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SaintVincentAndTheGrenadines.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Samoa.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SaudiArabia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Senegal.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Seychelles.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SierraLeone.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Singapore.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SolomonIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Somalia.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Somalia2.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Soudan.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SouthAfrica.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_SriLanka.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Suriname.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Taiwan.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Tanzania.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Thailand.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_TimorLeste.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Togo.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Tonga.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_TrinidadAndTobago.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_TurksAndCaicosIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Tuvalu.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_UnitedArabEmirates.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_UnitedStates.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Vanuatu.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Venezuela.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Vietnam.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_VirginIslandsUs.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_WallisAndFutuna.tif',\n", + " 'coastal-observatory/data/Mangrove_agb_Yemen.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_AndamanAndNicobar.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Angola.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Anguilla.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_AntiguaAndBarbuda.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Aruba.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Australia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Bahamas.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Bahrain.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Bangladesh.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Barbados.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Belize.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Benin.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Brazil.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_BritishVirginIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Brunei.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Cambodia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Cameroon.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_CarribeanCaymanIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_China.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Colombia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Comoros.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_CostaRica.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Cote.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_CotedIvoire.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Cuba.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_DemocraticRepublicOfCongo.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Djibouti.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_DominicanRepublic.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_EcuadorWithGalapagos.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Egypt.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_ElSalvador.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_EquatorialGuinea.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Eritrea.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_EuropaIsland.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Fiji.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Fiji2.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_FrenchGuiana.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_FrenchPolynesia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Gabon.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Gambia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Ghana.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Grenada.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Guadeloupe.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Guam.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Guatemala.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Guinea.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_GuineaBissau.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Guyana.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Haiti.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Hawaii.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Honduras.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_HongKong.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_India.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Indonesia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Iran.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Jamaica.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Japan.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Kenya.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Liberia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Macau.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Madagascar.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Malaysia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Martinique.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Mauritania.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Mayotte.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Mexico.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Micronesia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Mozambique.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Myanmar.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_NewCaledonia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_NewZealand.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Nicaragua.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Nigeria.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_NorthernMarianaIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Oman.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Pakistan.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Palau.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Panama.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_PapuaNewGuinea.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Peru.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Philipines.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_PuertoRico.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Qatar.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_ReunionAndMauritius.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SaintKittsAndNevis.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SaintLucia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SaintVincentAndTheGrenadines.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Samoa.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SaudiArabia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Senegal.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Seychelles.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SierraLeone.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Singapore.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SolomonIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Somalia.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SouthAfrica.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_SriLanka.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Sudan.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Suriname.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Taiwan.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Tanzania.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Thailand.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_TimorLeste.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Togo.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Tonga.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_TrinidadAndTobago.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_TurksAndCaicosIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Tuvalu.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_UnitedArabEmirates.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_UnitedStates.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Vanuatu.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Venezuela.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Vietnam.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_VirginIslandsUs.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_WallisAndFutuna.tif',\n", + " 'coastal-observatory/data/Mangrove_hba95_Yemen.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_AndamanAndNicobar.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Angola.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Anguilla.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_AntiguaAndBarbuda.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Aruba.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Australia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Bahamas.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Bahrain.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Bangladesh.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Barbados.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Belize.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Benin.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Brazil.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_BritishVirginIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Brunei.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Cambodia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Cameroon.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_CarribeanCaymanIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_China.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Colombia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Comoros.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_CostaRica.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_CoteDivoire.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Cuba.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_DemocraticRepublicOfCongo.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Djibouti.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_DominicanRepublic.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_EcuadorWithGalapagos.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Egypt.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_ElSalvador.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_EquatorialGuinea.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Eritrea.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_EuropaIsland.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Fiji.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Fiji2.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_FrenchGuyana.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_FrenchPolynesia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Gabon.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Gambia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Ghana.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Grenada.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Guadeloupe.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Guam.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Guatemala.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Guinea.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_GuineaBissau.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Guyana.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Haiti.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Hawaii.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Honduras.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_HongKong.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_India.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Indonesia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Iran.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Jamaica.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Japan.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Kenya.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Liberia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Macau.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Madagascar.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Malaysia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Martinique.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Mauritania.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Mayotte.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Mexico.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Micronesia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Mozambique.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Myanmar.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_NewCaledonia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Newzealand.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Nicaragua.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Nigeria.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_NorthernMarianaIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Oman.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Pakistan.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Palau.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Panama.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_PapuaNewGuinea.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Peru.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Philipines.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_PuertoRico.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Qatar.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_ReunionAndMauritius.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SaintKittsAndNevis.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SaintLucia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SaintVincentAndTheGrenadines.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Samoa.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SaudiArabia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Senegal.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Seychelles.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SierraLeone.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Singapore.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SolomonIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Somalia.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Somalia2.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Soudan.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SouthAfrica.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_SriLanka.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Suriname.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Taiwan.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Tanzania.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Thailand.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_TimorLeste.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Togo.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Tonga.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_TrinidadAndTobago.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_TurksAndCaicosIslands.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Tuvalu.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_UnitedArabEmirates.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_UnitedStates.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Vanuatu.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Venezuela.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Vietnam.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_VirginIslandsUs.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_WallisAndFutuna.tif',\n", + " 'coastal-observatory/data/Mangrove_hmax95_Yemen.tif']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_all_s3_keys(bucket, model_name, ext):\n", + " \"\"\"Get a list of all keys in an S3 bucket.\"\"\"\n", + " keys = []\n", + "\n", + " kwargs = {\"Bucket\": bucket, \"Prefix\": f\"{model_name}/\"}\n", + " while True:\n", + " resp = s3_client.list_objects_v2(**kwargs)\n", + " for obj in resp[\"Contents\"]:\n", + " if obj[\"Key\"].endswith(ext) and \"historical\" not in obj[\"Key\"]:\n", + " keys.append(obj[\"Key\"])\n", + "\n", + " try:\n", + " kwargs[\"ContinuationToken\"] = resp[\"NextContinuationToken\"]\n", + " except KeyError:\n", + " break\n", + "\n", + " return keys\n", + "\n", + "keys = get_all_s3_keys(raw_data_bucket, raw_data_prefix, \".tif\")\n", + "keys" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def create_cog_filename(f):\n", + " \n", + " f = Path(f).stem\n", + " # Example: \"Mangrove_agb_AndamanAndNicobar.tif\" -> \"Mangrove_agb_AndamanAndNicobar_2000year.tif\"\n", + " # Example: \"Mangrove_hmax95_Yemen.tif\" -> \"Mangrove_hmax95_Yemen_2000year.tif\"\n", + " \n", + " # Simply append 2000year to the stem\n", + " cog_filename = f\"{f}_2000year.tif\"\n", + " return cog_filename" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Define COG profile for rasterio\n", + "COG_PROFILE = {\n", + " \"driver\": \"COG\",\n", + " \"compress\": \"DEFLATE\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_to_proper_CRS_and_cogify(name, cog_filename, cog_data_bucket, cog_data_prefix, local_output_dir=None):\n", + " s3_key = f\"{cog_data_prefix}/{cog_filename}\"\n", + " reproject_filename = f\"reproj/{cog_filename}\"\n", + " \n", + " # Create a temporary file for the downloaded S3 object\n", + " temp_input_file = f\"temp_{os.path.basename(name)}\"\n", + "\n", + " try:\n", + " # Download the file from S3 first\n", + " print(f\"[DOWNLOAD] Downloading {name} from S3...\")\n", + " s3_client.download_file(raw_data_bucket, name, temp_input_file)\n", + " \n", + " # Reproject using the local file\n", + " print(f\"[REPROJECT] {name} → {reproject_filename} (EPSG:4326)\")\n", + " with rasterio.open(temp_input_file) as src:\n", + " dst_crs = \"EPSG:4326\"\n", + " transform, width, height = calculate_default_transform(\n", + " src.crs, dst_crs, src.width, src.height, *src.bounds\n", + " )\n", + " kwargs = src.meta.copy()\n", + " kwargs.update({\n", + " \"driver\": \"COG\", # write a COG instead of plain GTiff\n", + " \"compress\": \"DEFLATE\", # or \"LZW\"\n", + " \"crs\": dst_crs,\n", + " \"transform\": transform,\n", + " \"width\": width,\n", + " \"height\": height\n", + " })\n", + "\n", + " with rasterio.open(f\"{reproject_filename}\", \"w\", **kwargs) as dst:\n", + " reproject(\n", + " source=rasterio.band(src, 1),\n", + " destination=rasterio.band(dst, 1),\n", + " src_transform=src.transform,\n", + " src_crs=src.crs,\n", + " dst_transform=transform,\n", + " dst_crs=dst_crs,\n", + " resampling=Resampling.nearest,\n", + " wrapdateline=True\n", + " )\n", + "\n", + " # 3) COGify & upload\n", + " print(f\"[COGIFY] {reproject_filename} → s3://{cog_data_bucket}/{s3_key}\")\n", + " ds = rxr.open_rasterio(reproject_filename)\n", + " ds = ds.rename({\"y\": \"lat\", \"x\": \"lon\"})\n", + " ds.rio.set_spatial_dims(\"lon\", \"lat\", inplace=True)\n", + " ds.rio.write_nodata(-9999, inplace=True)\n", + "\n", + " with tempfile.NamedTemporaryFile() as tmp:\n", + " ds.rio.to_raster(tmp.name, **COG_PROFILE)\n", + " \n", + " # Upload to S3\n", + " s3_client.upload_file(\n", + " Filename = tmp.name, \n", + " Bucket = cog_data_bucket, \n", + " Key = s3_key)\n", + " print(f\"[SUCCESS] Uploaded to s3://{cog_data_bucket}/{s3_key}\")\n", + " \n", + " # Save locally if output directory is specified\n", + " if local_output_dir:\n", + " os.makedirs(local_output_dir, exist_ok=True)\n", + " local_path = os.path.join(local_output_dir, cog_filename)\n", + " \n", + " # Copy the COG file to local directory\n", + " import shutil\n", + " shutil.copy(tmp.name, local_path)\n", + " print(f\"[LOCAL SAVE] Saved COG to {local_path}\")\n", + " \n", + " except Exception as e:\n", + " print(f\"[ERROR] Failed to process {name}: {str(e)}\")\n", + " raise\n", + " \n", + " finally:\n", + " # Clean up temporary input file\n", + " if os.path.exists(temp_input_file):\n", + " os.remove(temp_input_file)\n", + " print(f\"[CLEANUP] removed temporary input file {temp_input_file}\")\n", + " \n", + " # Clean up local intermediate\n", + " if os.path.exists(reproject_filename):\n", + " os.remove(reproject_filename)\n", + " print(f\"[CLEANUP] removed intermediate {reproject_filename}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Local COGs will be saved to: output/cms-global-map-mangrove\n", + "\n", + "Processing: coastal-observatory/data/Mangrove_agb_AndamanAndNicobar.tif\n", + "Output filename: Mangrove_agb_AndamanAndNicobar_2000-01-01day_2009-12-31.tif\n", + "[DOWNLOAD] Downloading coastal-observatory/data/Mangrove_agb_AndamanAndNicobar.tif from S3...\n", + "[REPROJECT] coastal-observatory/data/Mangrove_agb_AndamanAndNicobar.tif → reproj/Mangrove_agb_AndamanAndNicobar_2000-01-01day_2009-12-31.tif (EPSG:4326)\n", + "[COGIFY] reproj/Mangrove_agb_AndamanAndNicobar_2000-01-01day_2009-12-31.tif → s3://ghgc-data-store-dev/transformed_cogs/CMS_Global_Map_Mangrove_Canopy/Mangrove_agb_AndamanAndNicobar_2000-01-01day_2009-12-31.tif\n" + ] + } + ], + "source": [ + "# Initialize DataFrame to track processed files\n", + "files_processed = pd.DataFrame(columns=[\"file_name\", \"COGs_created\"])\n", + "\n", + "# Get local output directory from config\n", + "local_output_dir = config.get(\"local_output_dir\")\n", + "\n", + "# Create output directories\n", + "if local_output_dir:\n", + " os.makedirs(local_output_dir, exist_ok=True)\n", + " print(f\"Local COGs will be saved to: {local_output_dir}\")\n", + "\n", + "# Process all files\n", + "for name in sorted(keys):\n", + " cog_filename = create_cog_filename(name, start_str, end_str)\n", + " print(f\"\\nProcessing: {name}\")\n", + " print(f\"Output filename: {cog_filename}\")\n", + " \n", + " # Process the file with local output directory\n", + " convert_to_proper_CRS_and_cogify(name, cog_filename, cog_data_bucket, cog_data_prefix, local_output_dir)\n", + " \n", + " # Add to tracking DataFrame\n", + " files_processed = files_processed._append(\n", + " {\"file_name\": name, \"COGs_created\": cog_filename},\n", + " ignore_index=True,\n", + " )\n", + " print(f\"Generated and saved COG: {cog_filename}\")\n", + "\n", + "print(\"\\nDone generating COGs\")\n", + "if local_output_dir:\n", + " print(f\"COGs saved locally to: {local_output_dir}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save metadata if there are processed files\n", + "if len(files_processed) > 0:\n", + " # Get metadata from one of the processed files\n", + " sample_file = files_processed.iloc[0]['file_name']\n", + " temp_sample_file = f\"temp_{os.path.basename(sample_file)}\"\n", + " \n", + " # Download sample file to extract metadata\n", + " s3_client.download_file(raw_data_bucket, sample_file, temp_sample_file)\n", + " \n", + " with rasterio.open(temp_sample_file) as src:\n", + " metadata = {\n", + " \"description\": src.tags(),\n", + " \"driver\": src.driver,\n", + " \"dtype\": str(src.dtypes[0]),\n", + " \"nodata\": src.nodata,\n", + " \"width\": src.width,\n", + " \"height\": src.height,\n", + " \"count\": src.count,\n", + " \"crs\": str(src.crs),\n", + " \"transform\": list(src.transform),\n", + " \"bounds\": list(src.bounds),\n", + " \"total_files_processed\": len(files_processed),\n", + " \"year\": \"2000\"\n", + " }\n", + " \n", + " # Upload metadata\n", + " with tempfile.NamedTemporaryFile(mode=\"w+\") as fp:\n", + " json.dump(metadata, fp, indent=2)\n", + " fp.flush()\n", + " \n", + " s3_client.upload_file(\n", + " Filename=fp.name,\n", + " Bucket=bucket_name,\n", + " Key=f\"{cog_data_prefix}/metadata.json\",\n", + " )\n", + " print(f\"Uploaded metadata to s3://{bucket_name}/{cog_data_prefix}/metadata.json\")\n", + " \n", + " # Clean up sample file\n", + " if os.path.exists(temp_sample_file):\n", + " os.remove(temp_sample_file)\n", + "\n", + "# Save the files_processed DataFrame to CSV using the same s3_client\n", + "with tempfile.NamedTemporaryFile(mode=\"w+\", suffix=\".csv\") as fp:\n", + " files_processed.to_csv(fp.name, index=False)\n", + " fp.flush()\n", + " \n", + " s3_client.upload_file(\n", + " Filename=fp.name,\n", + " Bucket=bucket_name,\n", + " Key=f\"{cog_data_prefix}/files_converted.csv\",\n", + " )\n", + " print(f\"Saved processing log to s3://{bucket_name}/{cog_data_prefix}/files_converted.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Processing Summary:\n", + "Total files found: 348\n", + "Files processed: 348\n", + "\n", + "Processed files:\n" + ] + }, + { + "data": { + "text/html": [ + "
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file_nameCOGs_created
0coastal-observatory/data/Mangrove_agb_AndamanA...Mangrove_agb_AndamanAndNicobar_2000-01-01day_2...
1coastal-observatory/data/Mangrove_agb_Angola.tifMangrove_agb_Angola_2000-01-01day_2009-12-31.tif
2coastal-observatory/data/Mangrove_agb_Anguilla...Mangrove_agb_Anguilla_2000-01-01day_2009-12-31...
3coastal-observatory/data/Mangrove_agb_AntiguaA...Mangrove_agb_AntiguaAndBarbuda_2000-01-01day_2...
4coastal-observatory/data/Mangrove_agb_Aruba.tifMangrove_agb_Aruba_2000-01-01day_2009-12-31.tif
.........
343coastal-observatory/data/Mangrove_hmax95_Venez...Mangrove_hmax95_Venezuela_2000-01-01day_2009-1...
344coastal-observatory/data/Mangrove_hmax95_Vietn...Mangrove_hmax95_Vietnam_2000-01-01day_2009-12-...
345coastal-observatory/data/Mangrove_hmax95_Virgi...Mangrove_hmax95_VirginIslandsUs_2000-01-01day_...
346coastal-observatory/data/Mangrove_hmax95_Walli...Mangrove_hmax95_WallisAndFutuna_2000-01-01day_...
347coastal-observatory/data/Mangrove_hmax95_Yemen...Mangrove_hmax95_Yemen_2000-01-01day_2009-12-31...
\n", + "

348 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " file_name \\\n", + "0 coastal-observatory/data/Mangrove_agb_AndamanA... \n", + "1 coastal-observatory/data/Mangrove_agb_Angola.tif \n", + "2 coastal-observatory/data/Mangrove_agb_Anguilla... \n", + "3 coastal-observatory/data/Mangrove_agb_AntiguaA... \n", + "4 coastal-observatory/data/Mangrove_agb_Aruba.tif \n", + ".. ... \n", + "343 coastal-observatory/data/Mangrove_hmax95_Venez... \n", + "344 coastal-observatory/data/Mangrove_hmax95_Vietn... \n", + "345 coastal-observatory/data/Mangrove_hmax95_Virgi... \n", + "346 coastal-observatory/data/Mangrove_hmax95_Walli... \n", + "347 coastal-observatory/data/Mangrove_hmax95_Yemen... \n", + "\n", + " COGs_created \n", + "0 Mangrove_agb_AndamanAndNicobar_2000-01-01day_2... \n", + "1 Mangrove_agb_Angola_2000-01-01day_2009-12-31.tif \n", + "2 Mangrove_agb_Anguilla_2000-01-01day_2009-12-31... \n", + "3 Mangrove_agb_AntiguaAndBarbuda_2000-01-01day_2... \n", + "4 Mangrove_agb_Aruba_2000-01-01day_2009-12-31.tif \n", + ".. ... \n", + "343 Mangrove_hmax95_Venezuela_2000-01-01day_2009-1... \n", + "344 Mangrove_hmax95_Vietnam_2000-01-01day_2009-12-... \n", + "345 Mangrove_hmax95_VirginIslandsUs_2000-01-01day_... \n", + "346 Mangrove_hmax95_WallisAndFutuna_2000-01-01day_... \n", + "347 Mangrove_hmax95_Yemen_2000-01-01day_2009-12-31... \n", + "\n", + "[348 rows x 2 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display summary\n", + "print(f\"\\nProcessing Summary:\")\n", + "print(f\"Total files found: {len(keys)}\")\n", + "print(f\"Files processed: {len(files_processed)}\")\n", + "print(f\"\\nProcessed files:\")\n", + "files_processed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gdal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/generating_statistics_for_validation/cms-global-map-mangrove/cms-global-map-mangrove-generate-statistics.ipynb b/generating_statistics_for_validation/cms-global-map-mangrove/cms-global-map-mangrove-generate-statistics.ipynb new file mode 100644 index 000000000..89cbf7437 --- /dev/null +++ b/generating_statistics_for_validation/cms-global-map-mangrove/cms-global-map-mangrove-generate-statistics.ipynb @@ -0,0 +1,580 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CMS Global Map Mangrove Canopy - Statistics Generation\n", + "\n", + "Compares original GeoTIFF files with transformed COGs using chunked processing for memory efficiency.\n", + "\n", + "Outputs:\n", + "- `detailed_stats.json` - Detailed statistics for each file\n", + "- `overall_stats.json` - Overall statistics for all 3 variables (agb, hmax95, hba95)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup and Configuration\n", + "import os\n", + "import json\n", + "import boto3\n", + "import numpy as np\n", + "import pandas as pd\n", + "import rasterio\n", + "from rasterio.windows import Window\n", + "from pathlib import Path\n", + "import tempfile\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from dotenv import load_dotenv\n", + "import gc\n", + "from tqdm import tqdm\n", + "\n", + "load_dotenv()\n", + "\n", + "# Configuration\n", + "reprocess = True # Set to True to reprocess all files, False to load existing results\n", + "BUCKET_NAME = \"ghgc-data-store-develop\"\n", + "ORIGINAL_PREFIX = \"coastal-observatory/data\"\n", + "COG_PREFIX = \"cms-mangrove-agb-canopyheight-grid-v1.3\"\n", + "VALID_DATA_TYPES = [\"agb\", \"hmax95\", \"hba95\"]\n", + "NODATA_VALUE = 0\n", + "DEFAULT_CHUNK_SIZE = 1024\n", + "output_dir = os.path.dirname(os.path.abspath(\"__file__\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# S3 Helper Functions\n", + "def get_s3_client():\n", + " return boto3.Session(\n", + " aws_access_key_id=os.environ.get(\"AWS_ACCESS_KEY_ID\"),\n", + " aws_secret_access_key=os.environ.get(\"AWS_SECRET_ACCESS_KEY\"),\n", + " aws_session_token=os.environ.get(\"AWS_SESSION_TOKEN\"),\n", + " region_name=\"us-west-2\"\n", + " ).client(\"s3\")\n", + "\n", + "def list_s3_files(bucket, prefix, extension=\".tif\"):\n", + " s3_client = get_s3_client()\n", + " files = []\n", + " paginator = s3_client.get_paginator(\"list_objects_v2\")\n", + " for page in paginator.paginate(Bucket=bucket, Prefix=prefix):\n", + " if \"Contents\" in page:\n", + " files.extend([obj[\"Key\"] for obj in page[\"Contents\"] if obj[\"Key\"].endswith(extension)])\n", + " return sorted(files)\n", + "\n", + "def download_s3_file(bucket, key, local_path):\n", + " get_s3_client().download_file(bucket, key, local_path)\n", + "\n", + "def extract_metadata_from_filename(filename):\n", + " parts = Path(filename).stem.split(\"_\")\n", + " return {\n", + " \"data_type\": parts[1] if len(parts) > 1 else \"unknown\",\n", + " \"region\": \"_\".join(parts[2:]).replace(\"_2000year\", \"\") if len(parts) > 2 else \"unknown\"\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Chunked Statistics Computation\n", + "class IncrementalStats:\n", + " def __init__(self):\n", + " self.n = 0\n", + " self.mean = 0.0\n", + " self.M2 = 0.0\n", + " self.min_val = float('inf')\n", + " self.max_val = float('-inf')\n", + " \n", + " def update(self, values):\n", + " if len(values) == 0:\n", + " return\n", + " self.min_val = min(self.min_val, np.nanmin(values))\n", + " self.max_val = max(self.max_val, np.nanmax(values))\n", + " for value in values:\n", + " self.n += 1\n", + " delta = value - self.mean\n", + " self.mean += delta / self.n\n", + " self.M2 += delta * (value - self.mean)\n", + " \n", + " def get_stats(self):\n", + " if self.n == 0:\n", + " return {\"min\": None, \"max\": None, \"mean\": None, \"std\": None, \"count\": 0}\n", + " return {\n", + " \"min\": float(self.min_val),\n", + " \"max\": float(self.max_val),\n", + " \"mean\": float(self.mean),\n", + " \"std\": float(np.sqrt(self.M2 / self.n) if self.n > 1 else 0),\n", + " \"count\": self.n\n", + " }\n", + "\n", + "def compute_raster_statistics_chunked(file_path):\n", + " try:\n", + " with rasterio.open(file_path) as src:\n", + " width, height = src.width, src.height\n", + " total_pixels = width * height\n", + " chunk_size = min(DEFAULT_CHUNK_SIZE, width, height)\n", + " \n", + " stats_calc = IncrementalStats()\n", + " valid_pixels = 0\n", + " \n", + " # Process in chunks\n", + " for row_off in range(0, height, chunk_size):\n", + " for col_off in range(0, width, chunk_size):\n", + " window = Window(col_off, row_off, \n", + " min(chunk_size, width - col_off),\n", + " min(chunk_size, height - row_off))\n", + " chunk = src.read(1, window=window)\n", + " valid_mask = (chunk != NODATA_VALUE) & np.isfinite(chunk)\n", + " valid_data = chunk[valid_mask]\n", + " valid_pixels += np.sum(valid_mask)\n", + " if len(valid_data) > 0:\n", + " stats_calc.update(valid_data)\n", + " \n", + " stats = stats_calc.get_stats()\n", + " stats.update({\n", + " \"valid_pixels\": int(valid_pixels),\n", + " \"total_pixels\": int(total_pixels),\n", + " \"coverage_percent\": float(valid_pixels / total_pixels * 100),\n", + " \"crs\": str(src.crs),\n", + " \"width\": width,\n", + " \"height\": height,\n", + " \"bounds\": list(src.bounds)\n", + " })\n", + " return stats\n", + " except Exception as e:\n", + " return {\"error\": str(e)}" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# File Processing Functions\n", + "def process_file_pair(original_key, cog_key, bucket):\n", + " result = {\n", + " \"original_file\": original_key,\n", + " \"cog_file\": cog_key,\n", + " \"metadata\": extract_metadata_from_filename(original_key)\n", + " }\n", + " \n", + " with tempfile.TemporaryDirectory() as tmpdir:\n", + " # Process original file\n", + " original_path = os.path.join(tmpdir, \"original.tif\")\n", + " download_s3_file(bucket, original_key, original_path)\n", + " result[\"original_stats\"] = compute_raster_statistics_chunked(original_path)\n", + " \n", + " # Process COG file\n", + " if cog_key:\n", + " cog_path = os.path.join(tmpdir, \"cog.tif\")\n", + " download_s3_file(bucket, cog_key, cog_path)\n", + " result[\"cog_stats\"] = compute_raster_statistics_chunked(cog_path)\n", + " else:\n", + " result[\"cog_stats\"] = None\n", + " \n", + " gc.collect()\n", + " return result\n", + "\n", + "def calculate_overall_statistics(results):\n", + " overall_stats = {\"original\": {}, \"cog\": {}}\n", + " \n", + " for data_type in VALID_DATA_TYPES:\n", + " for stat_type in [\"original\", \"cog\"]:\n", + " values = {\"mins\": [], \"maxs\": [], \"means\": [], \"stds\": [], \"counts\": []}\n", + " \n", + " for result in results:\n", + " if result[\"metadata\"][\"data_type\"] == data_type:\n", + " stats_key = f\"{stat_type}_stats\"\n", + " if result.get(stats_key) and \"mean\" in result[stats_key]:\n", + " stats = result[stats_key]\n", + " values[\"mins\"].append(stats[\"min\"])\n", + " values[\"maxs\"].append(stats[\"max\"])\n", + " values[\"means\"].append(stats[\"mean\"])\n", + " values[\"stds\"].append(stats[\"std\"])\n", + " values[\"counts\"].append(stats[\"count\"])\n", + " \n", + " if values[\"means\"]:\n", + " total_pixels = sum(values[\"counts\"])\n", + " weighted_mean = sum(m * c for m, c in zip(values[\"means\"], values[\"counts\"])) / total_pixels\n", + " \n", + " # Calculate combined std\n", + " pooled_var = sum((c-1)*s*s + c*(m-weighted_mean)**2 \n", + " for m, s, c in zip(values[\"means\"], values[\"stds\"], values[\"counts\"]))\n", + " combined_std = np.sqrt(pooled_var / (total_pixels - 1)) if total_pixels > 1 else 0\n", + " \n", + " overall_stats[stat_type][data_type] = {\n", + " \"min_value\": float(np.min(values[\"mins\"])),\n", + " \"max_value\": float(np.max(values[\"maxs\"])),\n", + " \"mean_value\": float(weighted_mean),\n", + " \"std_value\": float(combined_std)\n", + " }\n", + " \n", + " return overall_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reprocess flag is True - deleting existing results...\n", + "Deleted detailed_stats.json\n", + "Deleted overall_stats.json\n", + "Starting fresh processing...\n" + ] + } + ], + "source": [ + "# Check for existing results and handle reprocess flag\n", + "results_loaded = False\n", + "overall_stats_loaded = False\n", + "\n", + "# If reprocess is True, delete existing files and force reprocessing\n", + "if reprocess:\n", + " print(\"Reprocess flag is True - deleting existing results...\")\n", + " for filename in [\"detailed_stats.json\", \"overall_stats.json\"]:\n", + " filepath = os.path.join(output_dir, filename)\n", + " if os.path.exists(filepath):\n", + " os.remove(filepath)\n", + " print(f\"Deleted {filename}\")\n", + " print(\"Starting fresh processing...\")\n", + "else:\n", + " # Try to load existing results\n", + " if os.path.exists(os.path.join(output_dir, \"detailed_stats.json\")):\n", + " try:\n", + " with open(os.path.join(output_dir, \"detailed_stats.json\"), 'r') as f:\n", + " results = json.load(f)\n", + " print(f\"Loaded {len(results)} existing results\")\n", + " results_loaded = True\n", + " \n", + " # Load overall stats\n", + " if os.path.exists(os.path.join(output_dir, \"overall_stats.json\")):\n", + " with open(os.path.join(output_dir, \"overall_stats.json\"), 'r') as f:\n", + " lines = f.readlines()\n", + " if len(lines) >= 4:\n", + " raw_stats = json.loads(lines[1].strip())\n", + " cog_stats = json.loads(lines[3].strip())\n", + " overall_stats = {\"original\": raw_stats, \"cog\": cog_stats}\n", + " overall_stats_loaded = True\n", + " print(\"Loaded overall statistics\")\n", + " except Exception as e:\n", + " print(f\"Error loading existing results: {e}\")\n", + " print(\"Proceeding with normal processing...\")\n", + " results_loaded = False\n", + " else:\n", + " print(\"No existing results found - proceeding with processing...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Listing files...\n", + "Found 348 original files and 348 COG files\n", + "[348/348] Processing Mangrove_hmax95_Yemen.tif...una.tif..........tif...\n", + "Completed processing 348 files\n", + "Saved detailed_stats.json\n" + ] + } + ], + "source": [ + "# Main Processing\n", + "if not results_loaded:\n", + " # List files\n", + " print(\"Listing files...\")\n", + " original_files = [f for f in list_s3_files(BUCKET_NAME, ORIGINAL_PREFIX) \n", + " if any(dt in f for dt in VALID_DATA_TYPES)]\n", + " cog_files = list_s3_files(BUCKET_NAME, COG_PREFIX)\n", + " print(f\"Found {len(original_files)} original files and {len(cog_files)} COG files\")\n", + " \n", + " # Process files\n", + " results = []\n", + " for idx, orig_file in enumerate(original_files, 1):\n", + " print(f\"\\r[{idx}/{len(original_files)}] Processing {Path(orig_file).name}...\", end=\"\")\n", + " \n", + " # Find matching COG\n", + " orig_meta = extract_metadata_from_filename(orig_file)\n", + " cog_match = None\n", + " for cog_file in cog_files:\n", + " cog_meta = extract_metadata_from_filename(cog_file)\n", + " if (cog_meta[\"data_type\"] == orig_meta[\"data_type\"] and \n", + " cog_meta[\"region\"] == orig_meta[\"region\"]):\n", + " cog_match = cog_file\n", + " break\n", + " \n", + " try:\n", + " result = process_file_pair(orig_file, cog_match, BUCKET_NAME)\n", + " results.append(result)\n", + " except Exception as e:\n", + " results.append({\n", + " \"original_file\": orig_file,\n", + " \"cog_file\": cog_match,\n", + " \"metadata\": orig_meta,\n", + " \"error\": str(e)\n", + " })\n", + " \n", + " print(f\"\\nCompleted processing {len(results)} files\")\n", + " \n", + " # Save detailed results\n", + " with open(os.path.join(output_dir, \"detailed_stats.json\"), \"w\") as f:\n", + " json.dump(results, f, indent=2)\n", + " print(\"Saved detailed_stats.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved overall_stats.json\n", + "\n", + "============================================================\n", + "OVERALL STATISTICS\n", + "============================================================\n", + "\n", + "Original TIF files:\n", + "\n", + "AGB:\n", + " min_value: 0.284488\n", + " max_value: 910.475891\n", + " mean_value: 130.237788\n", + " std_value: 108.868964\n", + "\n", + "HMAX95:\n", + " min_value: 0.848500\n", + " max_value: 62.789001\n", + " mean_value: 16.388412\n", + " std_value: 11.190709\n", + "\n", + "HBA95:\n", + " min_value: 0.537700\n", + " max_value: 39.789799\n", + " mean_value: 10.468337\n", + " std_value: 7.126196\n", + "\n", + "COG files:\n", + "\n", + "AGB:\n", + " min_value: 0.284488\n", + " max_value: 910.475891\n", + " mean_value: 130.237788\n", + " std_value: 108.868964\n", + "\n", + "HMAX95:\n", + " min_value: 0.848500\n", + " max_value: 62.789001\n", + " mean_value: 16.388412\n", + " std_value: 11.190709\n", + "\n", + "HBA95:\n", + " min_value: 0.537700\n", + " max_value: 39.789799\n", + " mean_value: 10.468337\n", + " std_value: 7.126196\n" + ] + } + ], + "source": [ + "# Calculate and save overall statistics\n", + "if 'overall_stats' not in locals() or not overall_stats_loaded:\n", + " overall_stats = calculate_overall_statistics(results)\n", + " \n", + " # Save in special format\n", + " with open(os.path.join(output_dir, \"overall_stats.json\"), \"w\") as f:\n", + " f.write('\"Stats for raw tif files.\"\\n')\n", + " json.dump(overall_stats[\"original\"], f)\n", + " f.write('\\n\"Stats for transformed COG files.\"\\n')\n", + " json.dump(overall_stats[\"cog\"], f)\n", + " print(\"Saved overall_stats.json\")\n", + "\n", + "# Display overall statistics\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"OVERALL STATISTICS\")\n", + "print(\"=\"*60)\n", + "for stat_type, label in [(\"original\", \"Original TIF\"), (\"cog\", \"COG\")]:\n", + " print(f\"\\n{label} files:\")\n", + " for var_type in VALID_DATA_TYPES:\n", + " if var_type in overall_stats[stat_type]:\n", + " stats = overall_stats[stat_type][var_type]\n", + " print(f\"\\n{var_type.upper()}:\")\n", + " for key, value in stats.items():\n", + " print(f\" {key}: {value:.6f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SUMMARY:\n", + "Total files processed: 348\n", + "Files with errors: 0\n", + "\n", + "By data type:\n", + " agb: 116 files\n", + " hmax95: 116 files\n", + " hba95: 116 files\n" + ] + } + ], + "source": [ + "# Generate summary statistics\n", + "summary = {\n", + " \"total_files\": len(results),\n", + " \"by_data_type\": {},\n", + " \"errors\": sum(1 for r in results if \"error\" in r.get(\"original_stats\", {}))\n", + "}\n", + "\n", + "for data_type in VALID_DATA_TYPES:\n", + " type_results = [r for r in results if r[\"metadata\"][\"data_type\"] == data_type]\n", + " summary[\"by_data_type\"][data_type] = len(type_results)\n", + "\n", + "print(\"\\nSUMMARY:\")\n", + "print(f\"Total files processed: {summary['total_files']}\")\n", + "print(f\"Files with errors: {summary['errors']}\")\n", + "print(\"\\nBy data type:\")\n", + "for dt, count in summary[\"by_data_type\"].items():\n", + " print(f\" {dt}: {count} files\")" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved statistics_comparison.png\n" + ] + } + ], + "source": [ + "# Create visualization\n", + "viz_data = {dt: {\"original\": {\"means\": [], \"mins\": [], \"maxs\": []}, \n", + " \"cog\": {\"means\": [], \"mins\": [], \"maxs\": []}} \n", + " for dt in VALID_DATA_TYPES}\n", + "\n", + "for result in results:\n", + " if \"error\" not in result.get(\"original_stats\", {}):\n", + " dt = result[\"metadata\"][\"data_type\"]\n", + " if dt in viz_data and result[\"original_stats\"].get(\"mean\") is not None:\n", + " for key in [\"mean\", \"min\", \"max\"]:\n", + " viz_data[dt][\"original\"][f\"{key}s\"].append(result[\"original_stats\"][key])\n", + " if result.get(\"cog_stats\") and result[\"cog_stats\"].get(key) is not None:\n", + " viz_data[dt][\"cog\"][f\"{key}s\"].append(result[\"cog_stats\"][key])\n", + "\n", + "# Create figure\n", + "fig, axes = plt.subplots(3, 3, figsize=(15, 12))\n", + "fig.suptitle(\"CMS Global Map Mangrove Canopy - Statistics Comparison\", fontsize=16)\n", + "\n", + "for idx, (var_type, var_label) in enumerate([\n", + " (\"agb\", \"AGB (Aboveground Biomass)\"),\n", + " (\"hmax95\", \"HMAX95 (Max Height 95%)\"),\n", + " (\"hba95\", \"HBA95 (Basal Area Height 95%)\")\n", + "]):\n", + " # Box plot comparison\n", + " ax = axes[idx, 0]\n", + " if viz_data[var_type][\"original\"][\"means\"] and viz_data[var_type][\"cog\"][\"means\"]:\n", + " data = pd.DataFrame({\n", + " \"Original\": viz_data[var_type][\"original\"][\"means\"],\n", + " \"COG\": viz_data[var_type][\"cog\"][\"means\"]\n", + " })\n", + " data.boxplot(ax=ax)\n", + " ax.set_title(f\"{var_label}\\nMean Values\")\n", + " ax.set_ylabel(\"Value\")\n", + " \n", + " # Original histogram\n", + " ax = axes[idx, 1]\n", + " if viz_data[var_type][\"original\"][\"means\"]:\n", + " ax.hist(viz_data[var_type][\"original\"][\"means\"], bins=20, \n", + " color='skyblue', edgecolor='darkblue', alpha=0.7)\n", + " ax.axvline(np.mean(viz_data[var_type][\"original\"][\"means\"]), \n", + " color='darkblue', linestyle='--', linewidth=2)\n", + " ax.set_title(f\"{var_label}\\nOriginal Distribution\")\n", + " ax.set_xlabel(\"Mean Value\")\n", + " ax.set_ylabel(\"Frequency\")\n", + " \n", + " # COG histogram\n", + " ax = axes[idx, 2]\n", + " if viz_data[var_type][\"cog\"][\"means\"]:\n", + " ax.hist(viz_data[var_type][\"cog\"][\"means\"], bins=20, \n", + " color='salmon', edgecolor='darkred', alpha=0.7)\n", + " ax.axvline(np.mean(viz_data[var_type][\"cog\"][\"means\"]), \n", + " color='darkred', linestyle='--', linewidth=2)\n", + " ax.set_title(f\"{var_label}\\nCOG Distribution\")\n", + " ax.set_xlabel(\"Mean Value\")\n", + " ax.set_ylabel(\"Frequency\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(os.path.join(output_dir, \"statistics_comparison.png\"), dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "print(\"Saved statistics_comparison.png\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gdal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/generating_statistics_for_validation/cms-global-map-mangrove/detailed_stats.json b/generating_statistics_for_validation/cms-global-map-mangrove/detailed_stats.json new file mode 100644 index 000000000..76a4750fb --- /dev/null +++ b/generating_statistics_for_validation/cms-global-map-mangrove/detailed_stats.json @@ -0,0 +1,16010 @@ +[ + { + "original_file": "coastal-observatory/data/Mangrove_agb_AndamanAndNicobar.tif", + "cog_file": 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10.46833660312674, "std_value": 7.126195837381929}} +"Stats for transformed COG files." +{"agb": {"min_value": 0.2844882905483246, "max_value": 910.4758911132812, "mean_value": 130.23778814734214, "std_value": 108.86896375862148}, "hmax95": {"min_value": 0.8485000133514404, "max_value": 62.78900146484375, "mean_value": 16.388412236876732, "std_value": 11.19070852893252}, "hba95": {"min_value": 0.5376999974250793, "max_value": 39.789798736572266, "mean_value": 10.46833660312674, "std_value": 7.126195837381929}} \ No newline at end of file diff --git a/generating_statistics_for_validation/cms-global-map-mangrove/overall_stats_summary.png b/generating_statistics_for_validation/cms-global-map-mangrove/overall_stats_summary.png new file mode 100644 index 000000000..cd83cad93 Binary files /dev/null and b/generating_statistics_for_validation/cms-global-map-mangrove/overall_stats_summary.png differ diff --git a/generating_statistics_for_validation/cms-global-map-mangrove/statistics_comparison.png b/generating_statistics_for_validation/cms-global-map-mangrove/statistics_comparison.png new file mode 100644 index 000000000..8348e80fc Binary files /dev/null and b/generating_statistics_for_validation/cms-global-map-mangrove/statistics_comparison.png differ diff --git a/user_data_notebooks/cms-global-map-mangrove_User_Notebook.ipynb b/user_data_notebooks/cms-global-map-mangrove_User_Notebook.ipynb new file mode 100644 index 000000000..403fa6e93 --- /dev/null +++ b/user_data_notebooks/cms-global-map-mangrove_User_Notebook.ipynb @@ -0,0 +1,2826 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "---\n", + "title: Global Mangrove Distribution, Aboveground Biomass, and Canopy Height\n", + "description: This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.\n", + "authors: Kyle Lesinger\n", + "published: 22 July 2025\n", + "execute:\n", + " freeze: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Global Mangrove Distribution, Aboveground Biomass, and Canopy Height\n", + "\n", + "This notebook demonstrates how to access and visualize mangrove forest data from the CMS Global Map Mangrove Canopy dataset. Mangroves are critical coastal ecosystems that provide important ecosystem services including carbon storage, storm protection, and habitat for diverse species." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Access this Notebook\n", + "\n", + "You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this [**request form**](https://docs.google.com/forms/d/e/1FAIpQLSdJEOVa3rEjRKl2o0kqCCYM8io2cT8FplnYVnfqR3WV9IENtg/viewform) and providing the required information.\n", + "\n", + "Access the [**Global Mangrove Distribution, Aboveground Biomass, and Canopy Height**](https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=lab%2Ftree%2Fghgc-docs%2Fuser_data_notebooks%2Fcms-global-map-mangrove_User_Notebook.ipynb&branch=main) notebook in the US GHG Center JupyterHub." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "- [Data Summary and Application](#data-summary-and-application)\n", + "- [Approach](#approach)\n", + "- [About the Data](#about-the-data)\n", + "- [Install the Required Libraries](#install-the-required-libraries)\n", + "- [Query the STAC API](#query-the-stac-api)\n", + "- [Explore Available Regions](#explore-available-regions)\n", + "- [Visualize Mangrove Data on a Map](#visualize-mangrove-data-on-a-map)\n", + "- [Calculate Zonal Statistics](#calculate-zonal-statistics)\n", + "- [Explore Additional Variables and Regions](#explore-additional-variables-and-regions)\n", + "- [Summary](#summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Summary and Application\n", + "- **Spatial coverage**: Global mangrove extent\n", + "- **Spatial resolution**: 30 meters \n", + "- **Temporal extent**: Nominal year 2000\n", + "- **Temporal resolution**: Single time point\n", + "- **Unit**: \n", + " - Aboveground biomass (agb): Megagrams per hectare (Mg/ha)\n", + " - Maximum height 95th percentile (hmax95): meters (m)\n", + " - Basal area height 95th percentile (hba): meters (m)\n", + "- **Utility**: Blue carbon accounting, coastal ecosystem management, climate change mitigation\n", + "\n", + "For more, visit the [CMS Global Map Mangrove Canopy](https://earth.gov/ghgcenter/data-catalog/cms-global-map-mangrove-canopy) data overview page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Approach\n", + "\n", + "1. Identify and explore the CMS Global Map Mangrove Canopy dataset using the GHGC API `/stac` endpoint\n", + "2. Examine available regions and assets (biomass, height metrics) in the collection\n", + "3. Visualize mangrove data for a specific region (Australia) on an interactive map\n", + "4. Define an area of interest and calculate zonal statistics\n", + "5. Create interactive widgets to explore different variables and regions\n", + "6. Display mangrove characteristics for other regions globally" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About the Data\n", + "\n", + "This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree) [hmax95], and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) [hba] for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models.\n", + "\n", + "Mangroves are unique coastal forests found in tropical and subtropical regions that:\n", + "- Store substantial amounts of carbon in their biomass and soils\n", + "- Protect coastlines from erosion and storm damage\n", + "- Support diverse marine and terrestrial species\n", + "- Provide livelihoods for coastal communities\n", + "\n", + "For more information regarding this dataset, please visit the [CMS Global Map Mangrove Canopy](https://earth.gov/ghgcenter/data-catalog/cms-global-map-mangrove-canopy) data overview page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Terminology\n", + "\n", + "Navigating data via the GHGC API, you will encounter terminology that is different from browsing in a typical filesystem. We'll define some terms here which are used throughout this notebook.\n", + "- `catalog`: All datasets available at the `/stac` endpoint\n", + "- `collection`: A specific dataset, e.g. CMS Global Map Mangrove Canopy\n", + "- `item`: One granule in the dataset, e.g. mangrove data for a specific country/region\n", + "- `asset`: A variable available within the granule, e.g. mangrove-agb, mangrove-hmax95, or mangrove-hba95\n", + "- `STAC API`: **Sp**atio**T**emporal **A**sset **C**atalogs - Endpoint for fetching metadata about available datasets\n", + "- `Raster API`: Endpoint for fetching data itself, for imagery and statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install the Required Libraries\n", + "\n", + "Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:\n", + "\n", + "%pip install requests folium rasterstats pystac_client pandas matplotlib ipywidgets --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All libraries imported successfully!\n" + ] + } + ], + "source": [ + "# Import required libraries\n", + "import os\n", + "import json\n", + "import requests\n", + "import numpy as np\n", + "import pandas as pd\n", + "from datetime import datetime\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# For making maps\n", + "import folium\n", + "import folium.plugins\n", + "from folium import Map, TileLayer\n", + "\n", + "# For working with geospatial data\n", + "import geopandas as gpd\n", + "from shapely.geometry import box\n", + "\n", + "# For talking to the STAC API\n", + "from pystac_client import Client\n", + "\n", + "# Import GHG Center utilities\n", + "import ghgc_utils\n", + "\n", + "print(\"All libraries imported successfully!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Query the STAC API\n", + "\n", + "**STAC API Collection Names**\n", + "\n", + "Now, you must fetch the dataset from the [**STAC API**](https://earth.gov/ghgcenter/api/stac/) by defining its associated STAC API collection ID as a variable. \n", + "The collection ID, also known as the **collection name**, for the CMS Global Map Mangrove Canopy dataset is [**cms-global-map-mangrove-canopy**](https://earth.gov/ghgcenter/api/stac/collections/cms-global-map-mangrove-canopy-v6).*\n", + "\n", + "**You can find the collection name of any dataset on the GHGC data portal by navigating to the dataset landing page within the data catalog. The collection name is the last portion of the dataset landing page's URL, and is also listed in the pop-up box after clicking \"ACCESS DATA.\"*" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# Provide the STAC and RASTER API endpoints\n", + "# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.\n", + "STAC_API_URL = \"https://earth.gov/ghgcenter/api/stac\"\n", + "\n", + "# The RASTER API is used to fetch collections for visualization\n", + "RASTER_API_URL = \"https://earth.gov/ghgcenter/api/raster\"\n", + "\n", + "# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable\n", + "collection_name = \"cms-mangrove-agb-canopyheight-grid-v1.3\"" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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      • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    • \n", + " temporal\n", + "
        \n", + " \n", + " \n", + "
      • \n", + " interval[] 1 items\n", + " \n", + "
          \n", + " \n", + " \n", + "
        • \n", + " 0[] 2 items\n", + " \n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " 0\n", + " \"2000-01-01T00:00:00Z\"\n", + "
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          • \n", + " 1\n", + " \"2000-01-01T00:00:00Z\"\n", + "
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  • \n", + " license\n", + " \"CC-BY-4.0\"\n", + "
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  • \n", + " providers[] 1 items\n", + " \n", + "
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        \n", + " \n", + " \n", + " \n", + "
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      • \n", + " roles[] 1 items\n", + " \n", + "
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        • \n", + " 0\n", + " \"host\"\n", + "
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        \n", + " \n", + "
        • \n", + " \n", + " \n", + " \n", + " \n", + "
      • \n", + " url\n", + " \"https://www.earthdata.nasa.gov/dashboard/\"\n", + "
        • \n", + " \n", + " \n", + " \n", + "
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      • \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + "
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  • \n", + " summaries\n", + "
      \n", + " \n", + " \n", + "
    • \n", + " datetime[] 1 items\n", + " \n", + "
        \n", + " \n", + " \n", + " \n", + "
      • \n", + " 0\n", + " \"2000-01-01T00:00:00Z\"\n", + "
        • \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fetch the collection from the STAC API using the appropriate endpoint\n", + "# The `pystac_client` library enables an HTTP request\n", + "\n", + "catalog = Client.open(STAC_API_URL)\n", + "collection = catalog.get_collection(collection_name)\n", + "\n", + "# Print the properties of the collection to the console\n", + "collection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examining the contents of our `collection` under the `temporal` variable, we note that data is only available for the nominal year 2000." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explore Available Regions" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 121 items\n" + ] + } + ], + "source": [ + "items = list(collection.get_items()) # Convert the iterator to a list\n", + "print(f\"Found {len(items)} items\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# items in date range: 121\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# The search function lets you search for items within a specific date/time range\n", + "# There is only a single datetime for this collection for the nominal year 2000\n", + "search = catalog.search(\n", + " collections=collection_name,\n", + " datetime=['2000-01-01T00:00:00Z']\n", + ")\n", + "\n", + "unique_regions = []\n", + "# Take a look at the items we found\n", + "print(f\"# items in date range: {len(search.item_collection())}\")\n", + "for item in search.item_collection():\n", + " print(item)\n", + " unique_regions.append(item.id.split('-')[-1])\n", + "\n", + "unique_regions = np.unique(unique_regions)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Before we go further, let's pick which asset to focus on for the first set of plots within the notebook. \n", + "# This dataset has three assets to choose from:\n", + "asset_name = \"agb\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Mangrove Data on a Map\n", + "You will now explore mangrove distribution and characteristics for different regions and visualize the results on a map using `folium`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fetch Imagery from Raster API\n", + "Here we get information from the `Raster API` which we will add to our map in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the plotting legend unit information\n", + "units = {'agb':'Mg ha-1','hmax95':'meters','hba':'meters'}" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the information from the dropdown box above to grab the proper collection item\n", + "observation_1 = [i for i in items if 'Australia' in i.id][0]\n", + "\n", + "# Extract collection name and item ID\n", + "collection_id = observation_1.collection_id\n", + "item_id = observation_1.id" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'scale': 1.0,\n", + " 'nodata': -9999.0,\n", + " 'offset': 0.0,\n", + " 'sampling': 'area',\n", + " 'data_type': 'float32',\n", + " 'histogram': {'max': 145.98110961914062,\n", + " 'min': -8.919515609741211,\n", + " 'count': 11,\n", + " 'buckets': [739881, 1690, 500, 175, 84, 34, 17, 11, 6, 2]},\n", + " 'statistics': {'mean': 0.08236342857623923,\n", + " 'stddev': 1.6322306092733863,\n", + " 'maximum': 145.98110961914062,\n", + " 'minimum': -8.919515609741211,\n", + " 'valid_percent': 100.0}}]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "object = observation_1.assets[asset_name]\n", + "raster_bands = object.extra_fields.get(\"raster:bands\", [{}])\n", + "rescale_values = {\n", + " \"max\": raster_bands[0].get(\"histogram\", {}).get(\"max\"),\n", + " \"min\": 0,\n", + "}\n", + "\n", + "raster_bands" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, you will pass the `item id`, `collection name`, `asset name`, and the `rescale values` to the Raster API endpoint, along with a colormap. This step tells the Raster API which collection, item, and asset you want to view, specifying the colormap and colorbar ranges to use for visualization. The API returns a JSON with information about the requested image. Each image will be referred to as a tile." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a colormap for displaying the tiles\n", + "# Make sure that the capitalization matches Matplotlib standards\n", + "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", + "color_map = \"YlGn\"" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/cms-mangrove-agb-canopyheight-grid-v1.3/items/cms-mangrove-agb-canopyheight-grid-v1.3-Australia/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=agb&color_formula=gamma+r+1.05&colormap_name=ylgn&rescale=0%2C145.98110961914062'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [112.99986111111112,\n", + " -39.00013888891209,\n", + " 154.0001388889217,\n", + " -9.99986111111111],\n", + " 'center': [133.5000000000164, -24.5000000000116, 0]}" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Make a GET request to retrieve information for the date specified\n", + "observation_1_tile = requests.get(\n", + " f\"{RASTER_API_URL}/collections/{collection_id}/items/{item_id}/tilejson.json?\"\n", + " f\"&assets={asset_name}\"\n", + " f\"&color_formula=gamma+r+1.05&colormap_name={color_map.lower()}\"\n", + " f\"&rescale=0,{rescale_values['max']}\"\n", + ").json()\n", + "\n", + "# Print the properties of the retrieved granule to the console\n", + "observation_1_tile" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "''' PLOTTING NOTES\n", + "The mangrove data is tiled where each country has its own tile and the `center` coordinates are within the middle of the country tile area.\n", + "When plotting high-resolution mangrove forests (which can be on the coastlines of large countries such as Australia), plotting the center of the tile does not allow for easy visualization.\n", + "\n", + "To alleviate this, we will manually change the location of the latitude and longitude values when plotting to better assist with visualization.\n", + "\n", + "Location: Bathurst Island\n", + "latitude = -11.5\n", + "longitude = 130.8\n", + "\n", + "'''\n", + "\n", + "latitude = -11.5\n", + "longitude = 130.8\n", + "\n", + "# Note that we specify \"tiles=None\" because in the next step we're going to set a custom tile to serve as our underlying world map.\n", + "map_ = folium.Map(location=(latitude, longitude), zoom_start=10, tiles=None, tooltip = 'test tool tip')\n", + "# Specify a custom imagery source for the underlying map\n", + "folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)\n", + "# Add place labels on top\n", + "folium.TileLayer(tiles='https://server.arcgisonline.com/arcgis/rest/services/Reference/World_Boundaries_and_Places/MapServer/tile/{z}/{y}/{x}.png',name='ESRI World Boundaries and Places',attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)\n", + "\n", + "# Use the 'TileLayer' library display the raster layer\n", + "map_layer = TileLayer(\n", + " tiles=observation_1_tile[\"tiles\"][0], # Path to retrieve the tile\n", + " name=f'{items[0].assets[asset_name].title}', # Give this layer a title\n", + " overlay='True', # The layer can be overlaid on the map\n", + " attr=\"GHG\", # Set the attribution\n", + " opacity=0.4, # Adjust the transparency of the layer\n", + ")\n", + "map_layer.add_to(map_)\n", + "\n", + "# Adjust map elements \n", + "folium.LayerControl(collapsed=False, position='topright').add_to(map_)\n", + "\n", + "# Add colorbar\n", + "# We can use one of 'generate_html_colorbar' from the 'ghgc_utils' module \n", + "# to create an HTML colorbar representation.\n", + "legend_html = ghgc_utils.generate_html_colorbar(\n", + " color_map,\n", + " rescale_values,\n", + " label=f'{asset_name} ({units.get(asset_name)})'\n", + " )\n", + "\n", + "# Add colorbar to the map\n", + "map_.get_root().html.add_child(folium.Element(legend_html))\n", + "\n", + "\n", + "# Visualizing the map\n", + "map_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Zonal Statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon over Bathurst Island, Australia.\n", + "\n", + "
NOTE Selecting too large of an AOI will result in an error and no results will be returned from within the generate_stats function below. This is due to the large size of the files at a high resolution of 30 meters.
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated AOI for Bathurst Island, Australia:\n", + "{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'properties': {}, 'geometry': {'coordinates': [[[130.0, -12.0], [130.0, -11.16], [131.0, -11.16], [131.0, -12.0], [130.0, -12.0]]], 'type': 'Polygon'}}]}\n" + ] + } + ], + "source": [ + "# Give the AOI a name to use in plots later on\n", + "aoi_name = \"Bathurst Island, Northern Australia\"\n", + "# This AOI is defined as a GEOJSON.\n", + "aoi = {\n", + " \"type\": \"FeatureCollection\",\n", + " \"features\": [\n", + " {\n", + " \"type\": \"Feature\",\n", + " \"properties\": {},\n", + " \"geometry\": {\n", + " \"coordinates\": [\n", + " [\n", + " # [longitude, latitude]\n", + " [130.0, -12.0], # Southwest Bounding Coordinate\n", + " [130.0, -11.16], # Northwest Bounding Coordinate\n", + " [131.0, -11.16], # Northeast Bounding Coordinate\n", + " [131.0, -12.0], # Southeast Bounding Coordinate\n", + " [130.0, -12.0] # Closing the polygon at the Southwest Bounding Coordinate\n", + " ]\n", + " ],\n", + " \"type\": \"Polygon\"\n", + " }\n", + " }\n", + " ]\n", + "}\n", + "\n", + "\n", + "print(\"Updated AOI for Bathurst Island, Australia:\")\n", + "print(aoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Quick Folium map to visualize this AOI\n", + "map_ = folium.Map(location=(-11.5, 130.8), zoom_start=9)\n", + "\n", + "# Add AOI to map\n", + "folium.GeoJson(aoi, name=aoi_name).add_to(map_)\n", + "# Add data layer to map to visualize how many grid cells lie within our AOI\n", + "map_layer.add_to(map_)\n", + "# Add colorbar\n", + "legend_html = ghgc_utils.generate_html_colorbar(color_map,rescale_values,label=f'{items[0].assets[asset_name].title} (ppm)')\n", + "map_.get_root().html.add_child(folium.Element(legend_html))\n", + "map_" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating stats...\n", + "Done!\n", + "CPU times: user 5.39 ms, sys: 11.1 ms, total: 16.4 ms\n", + "Wall time: 12.6 s\n" + ] + }, + { + "data": { + "text/html": [ + "
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datetimeminmaxmeancountsumstdmedianmajorityminorityuniquehistogramvalid_percentmasked_pixelsvalid_pixelspercentile_2percentile_98date
02000-01-01T00:00:00Z57.41016006469726562500212.61692810058593750000121.31508636474609375000618041.0000000000000000000074977696.0000000000000000000030.74701657592672177088118.33956909179687500000108.5842437744140625000057.4101600646972656250016.00000000000000000000[[19664, 35221, 140535, 169842, 71162, 98772, ...5.6699999999999999289510274941.00000000000000000000618084.0000000000000000000072.01988983154296875000198.974548339843750000002000-01-01 00:00:00+00:00
\n", + "
" + ], + "text/plain": [ + " datetime min max \\\n", + "0 2000-01-01T00:00:00Z 57.41016006469726562500 212.61692810058593750000 \n", + "\n", + " mean count \\\n", + "0 121.31508636474609375000 618041.00000000000000000000 \n", + "\n", + " sum std \\\n", + "0 74977696.00000000000000000000 30.74701657592672177088 \n", + "\n", + " median majority minority \\\n", + "0 118.33956909179687500000 108.58424377441406250000 57.41016006469726562500 \n", + "\n", + " unique histogram \\\n", + "0 16.00000000000000000000 [[19664, 35221, 140535, 169842, 71162, 98772, ... \n", + "\n", + " valid_percent masked_pixels \\\n", + "0 5.66999999999999992895 10274941.00000000000000000000 \n", + "\n", + " valid_pixels percentile_2 \\\n", + "0 618084.00000000000000000000 72.01988983154296875000 \n", + "\n", + " percentile_98 date \n", + "0 198.97454833984375000000 2000-01-01 00:00:00+00:00 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "# Statistics will be returned as a Pandas DataFrame\n", + "df = ghgc_utils.generate_stats([observation_1],aoi,url=RASTER_API_URL,asset=asset_name, nodata=0)\n", + "\n", + "# Print the first five rows of statistics from our DataFrame\n", + "df.head(1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explore Additional Variables and Regions\n", + "\n", + "Now we will create interactive widgets to explore different mangrove variables (biomass, height metrics) across different regions of the world. This allows for comparative analysis of mangrove characteristics globally." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "If you change menu selections (e.g., to run another search), do NOT re-run this block!\n", + "Re-running will re-set all menus to their defaults!\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d5f370005eef4c03b99915c17e0f3922", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(Dropdown(description='Variable:', index=1, layout=Layout(height='75px', width='300px'), options…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Formatting settings for drop-down menus\n", + "style = {'description_width':'140px'}\n", + "layout = widgets.Layout(width='300px', height='75px')\n", + "\n", + "# Create dropdown widgets\n", + "variable = widgets.Dropdown(\n", + " options=['agb', 'hmax95', 'hba'],\n", + " value = 'hmax95', \n", + " description='Variable:', \n", + " style=style, \n", + " layout=layout\n", + ")\n", + "\n", + "region_select = widgets.Dropdown(\n", + " options=unique_regions,\n", + " value = 'CostaRica', \n", + " description='Region:', \n", + " style=style, \n", + " layout=layout\n", + ")\n", + "\n", + "\n", + "# Create horizontal box to arrange dropdowns side by side\n", + "dropdown_box = widgets.HBox([variable, region_select])\n", + "\n", + "# Display drop-down menus\n", + "print('If you change menu selections (e.g., to run another search), do NOT re-run this block!\\nRe-running will re-set all menus to their defaults!')\n", + "display(dropdown_box)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Use the information from the dropdown box above to grab the proper collection item\n", + "observation_2 = [i for i in items if region_select.value in i.id][0]\n", + "print(observation_2)\n", + "\n", + "# Extract collection name and item ID\n", + "collection_id = observation_2.collection_id\n", + "item_id = observation_2.id\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'max': 48.81807327270508, 'min': 0}\n", + "[{'scale': 1.0, 'nodata': -9999.0, 'offset': 0.0, 'sampling': 'area', 'data_type': 'float32', 'histogram': {'max': 48.81807327270508, 'min': -2.2742505073547363, 'count': 11, 'buckets': [993811, 862, 705, 637, 495, 347, 228, 149, 97, 45]}, 'statistics': {'mean': 0.059796311772089966, 'stddev': 1.1689403043113449, 'maximum': 48.81807327270508, 'minimum': -2.2742505073547363, 'valid_percent': 100.0}}]\n" + ] + } + ], + "source": [ + "object = observation_2.assets[variable.value]\n", + "raster_bands = object.extra_fields.get(\"raster:bands\", [{}])\n", + "rescale_values = {\n", + " \"max\": raster_bands[0].get(\"histogram\", {}).get(\"max\"),\n", + " \"min\": 0,\n", + "}\n", + "\n", + "print(rescale_values)\n", + "print(raster_bands)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose a colormap for displaying the tiles\n", + "# Make sure that the capitalization matches Matplotlib standards\n", + "# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html\n", + "color_map = \"YlGn\"" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'tilejson': '2.2.0',\n", + " 'version': '1.0.0',\n", + " 'scheme': 'xyz',\n", + " 'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/cms-mangrove-agb-canopyheight-grid-v1.3/items/cms-mangrove-agb-canopyheight-grid-v1.3-CostaRica/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=hmax95&color_formula=gamma+r+1.05&colormap_name=ylgn&rescale=0%2C48.81807327270508'],\n", + " 'minzoom': 0,\n", + " 'maxzoom': 24,\n", + " 'bounds': [-86.09476906457506,\n", + " 7.801602899887083,\n", + " -82.5033801756833,\n", + " 11.215214011000924],\n", + " 'center': [-84.29907462012918, 9.508408455444004, 0]}" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Make a GET request to retrieve information for the date specified\n", + "observation_2_tile = requests.get(\n", + " f\"{RASTER_API_URL}/collections/{collection_id}/items/{item_id}/tilejson.json?\"\n", + " f\"&assets={variable.value}\"\n", + " f\"&color_formula=gamma+r+1.05&colormap_name={color_map.lower()}\"\n", + " f\"&rescale=0,{rescale_values['max']}\"\n", + ").json()\n", + "\n", + "# Print the properties of the retrieved granule to the console\n", + "observation_2_tile\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latitude = observation_2_tile['center'][1]\n", + "longitude = observation_2_tile['center'][0]\n", + "\n", + "# Note that we specify \"tiles=None\" because in the next step we're going to set a custom tile to serve as our underlying world map.\n", + "map_ = folium.Map(location=(latitude, longitude), zoom_start=9, tiles=None, tooltip = 'test tool tip')\n", + "# Specify a custom imagery source for the underlying map\n", + "folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png', name='ESRI World Imagery', attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)\n", + "# Add place labels on top\n", + "folium.TileLayer(tiles='https://server.arcgisonline.com/arcgis/rest/services/Reference/World_Boundaries_and_Places/MapServer/tile/{z}/{y}/{x}.png',name='ESRI World Boundaries and Places',attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',overlay='True').add_to(map_)\n", + "\n", + "# Use the 'TileLayer' library display the raster layer\n", + "map_layer = TileLayer(\n", + " tiles=observation_2_tile[\"tiles\"][0], # Path to retrieve the tile\n", + " name=f'{observation_2.assets[variable.value].title}', # Give this layer a title\n", + " overlay='True', # The layer can be overlaid on the map\n", + " attr=\"GHG\", # Set the attribution\n", + " opacity=0.5, # Adjust the transparency of the layer\n", + ")\n", + "map_layer.add_to(map_)\n", + "\n", + "# Adjust map elements \n", + "folium.LayerControl(collapsed=False, position='topright').add_to(map_)\n", + "\n", + "# Add colorbar\n", + "# We can use one of 'generate_html_colorbar' from the 'ghgc_utils' module \n", + "# to create an HTML colorbar representation.\n", + "legend_html = ghgc_utils.generate_html_colorbar(\n", + " color_map,\n", + " rescale_values,\n", + " label=f'{variable.value} ({units.get(variable.value)})'\n", + " )\n", + "\n", + "# Add colorbar to the map\n", + "map_.get_root().html.add_child(folium.Element(legend_html))\n", + "\n", + "\n", + "# Visualizing the map\n", + "map_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we have successfully explored and visualized the CMS Global Map Mangrove Canopy dataset. The notebook demonstrated:\n", + "\n", + "1. **Data Access**: Connected to the US GHG Center STAC API to access the global mangrove dataset\n", + "2. **Data Exploration**: Discovered 121 regional items covering mangrove forests worldwide\n", + "3. **Variable Analysis**: Explored three key mangrove variables:\n", + " - Aboveground biomass (Mg/ha) - critical for carbon storage assessment\n", + " - Maximum canopy height (m) - indicates forest maturity\n", + " - Basal area weighted height (m) - reflects forest structure\n", + "4. **Interactive Visualization**: Created interactive maps showing mangrove distribution in Australia and other regions\n", + "5. **Zonal Statistics**: Calculated detailed statistics for Bathurst Island, Australia, showing mean biomass of ~6.1 Mg/ha\n", + "6. **Global Comparison**: Built interactive widgets to compare mangrove characteristics across different regions\n", + "\n", + "This dataset provides essential information for:\n", + "- Coastal ecosystem management and conservation planning\n", + "- Understanding global mangrove distribution and health\n", + "\n", + "For questions or feedback about this notebook, please contact us using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gdal", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/user_data_notebooks/ghgc_utils.py b/user_data_notebooks/ghgc_utils.py index 86fbb5a79..293729a2e 100644 --- a/user_data_notebooks/ghgc_utils.py +++ b/user_data_notebooks/ghgc_utils.py @@ -10,7 +10,6 @@ def raster_stats(item, geojson,**kwargs): """ Returns Raster API statistics for an item. Inputs: item, geojson, url = Raster API url, asset = asset name within item. Outputs: dictionary containing statistics over the bounding box and item's datetime information. """ - try: url = item["assets"][kwargs["asset"]]["href"] except TypeError as err: @@ -19,6 +18,13 @@ def raster_stats(item, geojson,**kwargs): print('KeyError in raster_stats: Make sure you include \'url\' and \'asset\' as keyword arguments!') sys.exit() + # Build parameters dictionary + params = {"url": url} + + # Add nodata parameter if provided + if "nodata" in kwargs: + params["nodata"] = kwargs["nodata"] + # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics result = requests.post( @@ -26,7 +32,7 @@ def raster_stats(item, geojson,**kwargs): f"{kwargs['url']}/cog/statistics", # Pass the URL to the item, asset name, and raster identifier as parameters - params={"url": url}, + params=params, # Send the GeoJSON object (polygon) along with the request json=geojson, @@ -34,38 +40,31 @@ def raster_stats(item, geojson,**kwargs): # Return the response in JSON format ).json() - # Print the result - ##print(result) + # print(result) # Return a dictionary containing the computed statistics along with the item's datetime information. try: return { **result["properties"], - "datetime": item["properties"]["start_datetime"], + "datetime": item["properties"].get("start_datetime", item["properties"].get("datetime")), } except KeyError as err: try: return { **result["features"][0]["properties"], - 'datetime': item["properties"]["start_datetime"], + 'datetime': item["properties"].get("start_datetime", item["properties"].get("datetime")), } except TypeError as err: return { **result["features"][0]["properties"], - "datetime": item.properties["start_datetime"] + "datetime": item.properties.get("start_datetime", item.properties.get("datetime")) } except TypeError as err: - try: - return { - **result["properties"], - "datetime": item.properties["start_datetime"] - } - except KeyError: - return { - **result["properties"], - "datetime": item.properties["datetime"] - } + return { + **result["properties"], + "datetime": item.properties.get("start_datetime", item.properties.get("datetime")) + } def clean_stats(stats_json) -> pd.DataFrame: """ @@ -93,12 +92,9 @@ def generate_stats(items,geojson,**kwargs): print('Generating stats...') for item in items: try: - date = item["properties"]["start_datetime"] # Get the associated date - except TypeError: - try: - date = item.properties["start_datetime"] - except KeyError: - date = item.properties["datetime"] + date = item["properties"].get("start_datetime", item["properties"].get("datetime")) # Get the associated date + except (TypeError, AttributeError): + date = item.properties.get("start_datetime", item.properties.get("datetime")) year_month = date[:10].replace('-', '') # Convert datetime to year-month stats[year_month] = raster_stats(item, geojson,**kwargs) df = clean_stats(stats)