Artificial neural networks on integrated multispectral and SAR data for high-performance prediction of eucalyptus biomass

Abstract Biomass estimation plays an important role in forest management being applied in most carbon sequestration studies, assessment of forest succession, conservation of natural resources, quantification of nutrient cycling, energy planning where forest biomass is used as primary fuel for power generation and harvest planning and stock management in pulp industry. Using data from Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) and Phased Array type L-band Synthetic Aperture Radar (PALSAR) sensors onboard Advanced Land Observing Satellite (ALOS), above-ground biomass (AGB) estimates were generated via artificial neural networks for a eucalyptus planting area in Minas Gerais State, Brazil. With 206 inventory plots, computed coefficient of determination between AGB estimates and observed values within validation sample was 0.95. Relative root mean square error was 2.87% with errors ranging from −8% to 4%. These results demonstrated artificial neural networks higher performance in modeling eucalyptus biomass based on Multispectral and SAR data over previous study, in which multiple linear regression method was applied in the same dataset, achieving R2 equal to 0.71.