Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data

Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports light detection ranging (LiDAR), optical airborne synthetic aperture radar (SAR) data for fuel estimation, but SAR are generally individually explored. Optical expected to be sensitive different types of loads because their imaging mechanisms. mainly captures characteristics leaf forest canopy, while latter more vertical structures due its strong penetrability. This study aims explore performance Landsat Enhanced Thematic Mapper Plus (ETM+) Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) as well combination on estimating three load—stem (SFL), branch (BFL) foliage (FFL). We first analyzed correlation between data. Then, partial least squares regression (PLSR) was used build estimation models based measurements from Vindeln, Sweden, variables derived Based leave-one-out cross-validation (LOOCV) method, results show that performed all (R2 = 0.72, 0.70, 0.72). best FFL 0.66), followed by BFL 0.56) SFL 0.37). Further improvements were found SFL, when integrating 0.76, 0.81, 0.82), highlighting importance selection estimation.