Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

Abstract

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R 2 = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R 2 = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R 2 = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.