COMPONENT FOREST ABOVE GROUND BIOMASS ESTIMATION USING LIDAR AND SAR DATA

Forest biomass plays an essential role in forest carbon reservoirs studies, biodiversity protection, forest management, and climate change mitigation actions. Parameters extracted from Light Detection and Ranging (LiDAR) and X-band Synthetic Aperture Radar (SAR) data were used in separately and in combination to estimate total forest aboveground biomass (AGB), but rarely used in components AGB estimation. In this paper, we extracted intensity, density, and height parameters from LiDAR data, coherence coefficients from Interferometric SAR (InSAR) data, backscatter coefficients and polarimetric decomposition parameters from Polarimetric SAR (PolSAR) to estimate forest total and components AGB. The results showed that PolSAR parameters have a unique advantage to estimate leaf biomass, with the highest R-2 of 0.773. And for total, bark and branch AGB, LiDAR, InSAR and PolSAR parameter combination have better accuracy, with R-2 of 0.818, 0.834, and 0.842, respectively. The study revealed that LiDAR and SAR used in combination can effectively estimation the forest total and components AGB.