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Merge pull request #2 from CamiloMatus/main
feat: integrate GPW 30m Canopy Height into Batch 2 exports
2 parents 482d411 + de33b24 commit aac4f8c

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‎.vscode/settings.json‎

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{
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"githubPullRequests.ignoredPullRequestBranches": [
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"main"
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]
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}

‎CPU_GEE-Data_Cube_Generator-F2A.ipynb‎

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"\n",
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"## Introduction\n",
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"\n",
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"This Colab notebook is designed to process and analyze geospatial data using Google Earth Engine (GEE). It focuses on deriving topographic, climatic, and spectral information for a specified region of interest (ROI). By leveraging GEE's powerful cloud computing capabilities, this workflow enables efficient data handling and export of key environmental variables for further analysis.\n",
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"This Colab notebook is designed to process and analyze geospatial data using Google Earth Engine (GEE). It focuses on deriving topographic, climatic, vegetation structure and spectral information for a specified region of interest (ROI). By leveraging GEE's powerful cloud computing capabilities, this workflow enables efficient data handling and export of key environmental variables for further analysis.\n",
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"\n",
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"\n",
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"### What Does This Notebook Do?:\n",
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"\n",
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"- Topographic Variables: Elevation, slope, aspect, landforms, and topographic position index (TPI).\n",
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"- Climate Variables: Total precipitation and average temperature over a defined period.\n",
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"- Structural Variables: Vegetation Height derived from the GPW dataset (30 m resolution). This dataset provides wall-to-wall coverage of all terrestrial ecosystems, used here to represent pre-fire standing fuel from the prior calendar year.\n",
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"- Spectral Indices: NDVI, NDBI, NDWI, and EVI derived from Sentinel-2 imagery.\n",
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"- Sentinel-2 Variables: Red, Green, Blue, R1, R2, R3, NIR, R4, SWIR1, and SWIR2 bands with cloud correction included.\n",
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"- Radar Data: VV and VH polarizations from Sentinel-1 with slope correction applied.\n",
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"climate_variables = apply_region_mask(ee.Image([precipitation, temperature]), region)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Vegetation Height"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# GPW provides median vegetation height for all terrestrial ecosystems (not limited to short veg)\n",
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"veg_height_collection = ee.ImageCollection(\"projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m\")\n",
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"veg_height = apply_region_mask(\n",
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" veg_height_collection.filterDate(f'{season_year-1}-01-01', f'{season_year}-01-01') \\\n",
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" .median() \\\n",
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" # The dataset stores values as scaled integers; multiply by 0.1 to convert to meters\n",
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" .multiply(0.1) \\\n",
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" .rename('veg_height') \\\n",
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" .toFloat(),\n",
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" region\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EcDMs62JWgIb"
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},
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"source": [
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"### Sentinel-2 Processing\n",
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"\n"
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"### Sentinel-2 Processing"
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]
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},
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{
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" (tpi, 'TPI', 'tpi', region), # 05 minutes aprox\n",
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" (landform, 'Landform', 'landform', region), # 03 minutes aprox\n",
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" (climate_variables, 'Climate Variables', 'climate_variables', region), # 06 minutes aprox\n",
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" (veg_height, 'Vegetation Height', 'veg_height', region), # 06 minutes aprox\n",
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"]\n",
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"\n",
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"batch_3 = [\n",

‎GPU_and_CPU_GEE-Data_Cube_Generator-F2A.ipynb‎

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"\n",
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"## Introduction\n",
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"\n",
13-
"This Colab notebook is designed to process and analyze geospatial data using Google Earth Engine (GEE). It focuses on deriving topographic, climatic, and spectral information for a specified region of interest (ROI). By leveraging GEE's powerful cloud computing capabilities, this workflow enables efficient data handling and export of key environmental variables for further analysis.\n",
13+
"This Colab notebook is designed to process and analyze geospatial data using Google Earth Engine (GEE). It focuses on deriving topographic, climatic, vegetation structure and spectral information for a specified region of interest (ROI). By leveraging GEE's powerful cloud computing capabilities, this workflow enables efficient data handling and export of key environmental variables for further analysis.\n",
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"\n",
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"\n",
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"### What Does This Notebook Do?:\n",
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"\n",
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"- Topographic Variables: Elevation, slope, aspect, landforms, and topographic position index (TPI).\n",
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"- Climate Variables: Total precipitation and average temperature over a defined period.\n",
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"- Structural Variables: Vegetation Height derived from the GPW dataset (30 m resolution). This dataset provides wall-to-wall coverage of all terrestrial ecosystems, used here to represent pre-fire standing fuel from the prior calendar year.\n",
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"- Spectral Indices: NDVI, NDBI, NDWI, and EVI derived from Sentinel-2 imagery.\n",
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"- Sentinel-2 Variables: Red, Green, Blue, R1, R2, R3, NIR, R4, SWIR1, and SWIR2 bands with cloud correction included.\n",
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"- Radar Data: VV and VH polarizations from Sentinel-1 with slope correction applied.\n",
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"climate_variables = apply_region_mask(ee.Image([precipitation, temperature]), region)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Vegetation Height\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# GPW provides median vegetation height for all terrestrial ecosystems (not limited to short veg)\n",
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"veg_height_collection = ee.ImageCollection(\"projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m\")\n",
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"veg_height = apply_region_mask(\n",
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" veg_height_collection.filterDate(f'{season_year-1}-01-01', f'{season_year}-01-01') \\\n",
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" .median() \\\n",
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" # The dataset stores values as scaled integers; multiply by 0.1 to convert to meters\n",
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" .multiply(0.1) \\\n",
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" .rename('veg_height') \\\n",
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" .toFloat(),\n",
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" region\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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" 'tpi': tpi,\n",
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" 'nasadem_variables': nasadem_variables,\n",
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" 'climate_variables': climate_variables,\n",
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" 'veg_height': veg_height,\n",
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" 'sentinel2_collections': sentinel2_collections,\n",
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" 'sentinel2_index_variables_collections': sentinel2_index_variables_collections,\n",
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" 'sentinel1_collections': sentinel1_collections,\n",
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"with open('/content/processed_data.pkl', 'wb') as f:\n",
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" pickle.dump(data, f)\n",
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"\n",
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"print(\"Data Serilized and saved as processed_data.pkl\")"
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"print(\"Data Serialized and saved as processed_data.pkl\")"
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]
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},
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{
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"# Access the variables\n",
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"landform = data['landform']\n",
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"tpi = data['tpi']\n",
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"nasadem_variables = data['elevation_variables']\n",
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"nasadem_variables = data['nasadem_variables']\n",
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"climate_variables = data['climate_variables']\n",
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"veg_height = data['veg_height'] \n",
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"sentinel2_collections = data['sentinel2_collections']\n",
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"sentinel2_index_variables_collections = data['sentinel2_index_variables_collections']\n",
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"sentinel1_collections = data['sentinel1_collections']\n",
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" (tpi, 'TPI', 'tpi', region), # 05 minutes aprox\n",
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" (landform, 'Landform', 'landform', region), # 03 minutes aprox\n",
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" (climate_variables, 'Climate Variables', 'climate_variables', region), # 06 minutes aprox\n",
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" (veg_height, 'Vegetation Height', 'veg_height', region), # 06 minutes aprox\n",
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"]\n",
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"\n",
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"batch_3 = [\n",

‎README.md‎

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- **Topographic Variables**: Elevation, slope, aspect, landforms, and TP.
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- **Climatic Variables**: Total precipitation and average temperature over defined periods.
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- **Structural Variables**: Vegetation Height (Canopy).
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- **Spectral Indices**: NDVI, NDBI, NDWI, EVI from Sentinel-2 imagery.
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- **Sentinel-2 Variables**: Includes Red, Green, Blue, and additional spectral bands.
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- **Radar Data**: Sentinel-1 VV and VH polarizations with slope correction.

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