Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests

One of the most determining factors in forest fire behaviour is to characterize fuel attributes. We investigated a complex Mediterranean type—mountainous Abies pinsapo–Pinus–Quercus–Juniperus with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), multispectral satellite data for estimating The three-step procedure consisted of: (i) three key attributes (biomass, structural complexity hygroscopicity), (ii) proposing synthetic index that encompasses quantify potential capacity propagation, (iii) generating cartograph propagation capacity. Our main findings showed Biomass–ALS calibration models performed well pinsapo (R2 = 0.69), Juniperus spp. 0.70), Pinus halepensis 0.59), mixed 0.80), spp.–Juniperus 0.59) forests. highest values biomass were obtained forests (190.43 Mg ha−1). fuels was assessed by calculating LiDAR Height Diversity Index (LHDI) regard distribution vertical diversity vegetation LHDI, which corresponded spp.–evergreen (2.56), Quercus suber (2.54), (2.49) forests, minimum being (1.37) shrubs (1.11). High Fuel Desiccation (IDM) those areas dominated (−396.71). Potential Behaviour Biomass (ICB) high or very 11.86% area 77.07%. Structural Complexity (ICE) 37.23% area, 46.35%, (ICD) opposite ICB ICE, complexity. Fire (ICP) 38.25% 45.96%. ICP related pinaster Remote sensing has been applied improve attribute characterisation cartography, highlighting utility integrating ALS estimate are more closely spatial organisation vegetation.