Integration of Polarimetric PALSAR Attributes and Local Geomorphometric Variables Derived from SRTM for Forest Biomass Modeling in Central Amazonia

The objective of this work is to generate a predictive model for biomass estimation in a forested area of central Amazonia based on the integration of incoherent target scattering decomposition polarimetric attributes extracted from Phased Array type L-band Synthetic Aperture Radar (PALSAR) data and geomorphometric variables derived from Shuttle Radar Topography Mission ( SRTM). In addition to their incorporation as variables of ecophysiological nature in biomass modeling, the geomorphometrics variables were also evaluated with regard to the possibility of minimization of topographic effects that affect the acquisition of PALSAR data. Based on the processed data, three biomass models were generated. The first model involves independent parameters extracted from polarimetric PALSAR data, the second includes the same polarimetric variables, which are additionally adjusted for the cosine factor effect. The third model integrates both the polarimetric parameters extracted from PALSAR and the geomorphometric variables derived from SRTM. The latter model showed the best results of biomass estimation among the models generated. Our results confirm the importance of geomorphometric variables as additional input in prognostic biomass models that use radar remote sensing, in this case L band, because of their causal relationship with ecophysiological aspects that condition the growth of forest communities and because of minimization of the terrain effects on the radar signal.