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CamiloMatusCamiloMatus
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refactor: address review comments on GPW integration and fix pickle bug
1 parent b0ccc3a commit de33b24

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Lines changed: 23 additions & 16 deletions

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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",
@@ -623,12 +624,12 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Vegetation Height data from Global Pasture Watch (using season_year - 1 for pre-fire state)\n",
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"canopy_collection = ee.ImageCollection(\"projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m\")\n",
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"\n",
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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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" canopy_collection.filterDate(f'{season_year-1}-01-01', f'{season_year}-01-01') \\\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",
@@ -1115,8 +1116,8 @@
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"batch_2 = [\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),\n",
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" (veg_height, 'Vegetation Height', 'veg_height', region), # 06 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",
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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",
27+
"- 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",
2728
"- 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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"metadata": {},
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"outputs": [],
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"source": [
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"# Vegetation Height data from Global Pasture Watch (using season_year - 1 for pre-fire state)\n",
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"canopy_collection = ee.ImageCollection(\"projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m\")\n",
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"\n",
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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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" canopy_collection.filterDate(f'{season_year-1}-01-01', f'{season_year}-01-01') \\\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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"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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"batch_2 = [\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),\n",
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" (veg_height, 'Vegetation Height', 'veg_height', region), # 06 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",

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