Exploring how to model forest structure and quantify ecosystem services using Python and Earth observation data (GEDI, Sentinel-1, Sentinel-2, SRTM) with data preprocessing, machine learning (Random Forest), and SHAP interpretation to understand variable importance. Estimating canopy height and aboveground biomass and mapping forest ecosystem services for monitoring and climate research. Case study combining GEDI, Sentinel, and SRTM data.
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[Online] Analyzing Geospatial Forest Data with Geopandas and Rasterio
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