Component Forest Above Ground Biomass Estimation Using Lidar and Sardata

Forest biomass plays an essential role in forest carbon reservoirs studies, biodiversity protection, forest manage-ment, 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 above-ground 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 coeffi-cients and polarimetric decomposition parameters from Po-larimetric SAR (PoISAR) to estimate forest total and components AGB. The results showed that PolSAR parameters have a unique advantage to estimate leaf biomass, with the highest R2 of 0.773. And for total, bark and branch AGB, LiDAR, InSAR and PolSAR parameter combination have better accuracy, with R2 of 0.818, 0.834, and 0.842, respec-tively. The study revealed that LiDAR and SAR used in combination can effectively estimation the forest total and components AGB.