Regression-Based Integrated Bi-sensor SAR Data Model to Estimate Forest Carbon Stock

Abstract

The objective of this study is to estimate the forest aboveground carbon (AGC) stock using integrated space-borne synthetic aperture radar (SAR) data from COSMO-Skymed (X band) and ALOS PALSAR (L band) with field inventory over a tropical deciduous mixed forest. Carbon acts as a vital constituent in the global decision making policy targeting the impact of reducing emissions from deforestation and forest degradation (REDD) and climate change. The study proposed an approach to develop regression models for assessing the forest AGC with synergistic use of SAR bi-sensor X and L band sigma nought data. The best-fit integrated aboveground biomass (AGB) model was validated with additional sample points that produced a model accuracy of 78.6%, adjusted R2 = 0.88, RMSE = 16.6 Mg/ha, standard error of estimates of 16.03 and Willmott’s index of agreement of 0.93. Resulting modeled AGB was converted to AGC using conversion factors. L band resulted in higher accuracy of estimates when compared to X band, while the estimation accuracy enhanced on integrating X- and L-band information. Hence, the study presents an approach using integrated SAR bi-sensor X and L bands that enhance the AGB and AGC estimation accuracy, which can contribute to the operational forestry and policy making related to forest conservation, REDD/REDD+ climate change, etc.