Supporting fire behavior modelling with canopy base height and canopy bulk density estimates using airborne and spaceborne lidar

The next-generation of fire behavior models must integrate 3D forest structural metrics to better explain fire spread, risk and severity. Canopy base height (CBH) and canopy bulk density (CBD) can be calibrated using lidar data collocated over field plots. Where no airborne lidar scanning data (ALS) exist, GEDI spaceborne lidar can provide 25-m predictions of CBH and CBD contingent to ALS-calibrated workflows. Our research presents GEDI footprint-level estimates of CBH and CBD for Mediterranean forests and builds upon collocated ALS-GEDI crossovers. Our accuracies are based on Random Forests classification: the R2 values for CBH and CBD were < 0.4 for all plant functional types evaluated. The classification of vertical continuity was satisfactory to inform on fire-prone conditions at the GEDI footprint level. Predictions for CBD were aggregated to produce a regional baseline maps, one at 1-km resolution. The usability of these coarse-scale aggregations of fuel estimates is limited because of resolution, presence of gaps and high heterogeneity of forest fuels within small steps. To inform about this heterogeneity and change estimates over time, we predict CBH and CBD over adjacent GEDI tracks collected 5-years apart (2019/24). These change estimates are relevant to show the high variability of the forest fuels that compromises the ability to depict change adding the issue of exact collocation between GEDI measurements that are adjacent, not collocated and therefore not repeated. We discuss methodological differences between our approach and recent studies on mapping fuel baselines and their approach to inform on dynamics. • Fuel mapping using GEDI and airborne lidar well-geolocated crossovers. • GEDI-predictions of canopy base height and canopy bulk density. • Close-range pairs of GEDI measurements over time to inform on change. • Good classification accuracy to estimate vertical continuity. • Scalable approach to add estimates of forest fuels into GEDI metrics.