Multitemporal Sentinel and GEDI data integration for overstory and understory fuel type classification

A three-step approach for large-scale overstory and understory fuel type mapping using multispectral, SAR, and LiDAR data. • Mapping the first level of the FirEUrisk fuel classification (overstory) using multispectral, SAR data, and Random Forest . • Estimating shrubland and grassland height using biophysical models based on precipitation and NDVI. • Mapping the second level of the FirEUrisk fuel classification (understory) using Interpolated GEDI data and decision rules. Wildfires significantly reshape the landscape of the Mediterranean basin, altering forest composition, structure, and diversity. Consequently, detailed fuel mapping is crucial for improving fire risk assessment and enhancing fire behavior modeling, as wildfires typically ignite from surface fuels and may spread vertically to canopy fuels due to canopy fuel continuity. This study generates a fuel type map of the overstory and understory based on the FirEUrisk hierarchical fuel classification system (FHFCS) in three steps, including overstory mapping using multispectral and radar data (Sentinel-1 and Sentinel-2), and topographic variables; shrubland and grassland height estimation using biophysical models based on precipitation and Normalized Difference Vegetation Index (NDVI); and understory mapping using spaceborne LiDAR data from the Global Ecosystem Dynamic Investigation (GEDI) and decision rules. An Overall Accuracy (OA) of 84.53% was achieved for overstory mapping for the composition of Vegetation Indices (VIs), Gray-Level Co-Occurrence Matrix (GLCM) textures, vertical transmit–horizontal receive (VH) of Sentinel-1, and elevation, integrated with biophysical models. Cropland, urban, non-fuel, and various forest classes, particularly evergreen needle-leaved forests, demonstrated outstanding performance, achieving F1 scores ranging from 83% to 100%. Finally, a rule-based model was established using Relative Height (RH) metrics from GEDI L2A data to estimate the height and type of understory defined in FHFCS with an OA of 69.2%. The RH metrics and decision rules proved a significantly effective and easy-to-interpret approach for estimating understory type and height in the absence of airborne LiDAR data. This three-step methodology provides a simple and efficient approach to large-scale overstory and understory mapping using multispectral, radar, and LiDAR data, which may facilitate both surface and crown fire simulation.