Estimating and mapping forest canopy fuel parameters from GEDI LiDAR data in Europe

Spatially-explicit information on canopy fuel parameters is key for wildfire propagation modelling, emission estimations and risk assessment. This work aims to develop easily-replicable methods to estimate critical fuel canopy parameters from spaceborne LiDAR observations acquired by the Global Ecosystem Dynamics Investigation (GEDI) sensor onboard the International Space Station. GEDI-like pseudowaveforms were modelled from discrete Airborne Laser Scanning (ALS) data and used to select the best GEDI predictor metrics to derive European wall-to-wall forest height and canopy cover maps. Then, GEDI spaceborne footprints were used to generate continental maps of canopy parameters through a two-steps approach: 1) Spatial interpolation of GEDI footprints inside homogeneous forest fuel type polygons, and 2) Modelling machine learning algorithms for the forest fuel type polygons without GEDI footprints inside, using auxiliary multispectral and RADAR imagery and biophysical variables. Our results show the capabilities of remote sensing and GEDI to estimate and map the spatial patterns of critical forest canopy fuel parameters in fire risk prevention and contribute to generating the necessary tools to develop an integrated risk-wise strategy that reduces fire vulnerability of ecosystems across Europe.