Mapping Subalpine Forest Aboveground Biomass in Qilian Mountain National Park Using UAV-LiDAR, GEDI, and Multisource Satellite Data
Yanyun Nian, Siwen Chen, Jie Chen, Minglu Che, Wenhui Zhang, Junejo Sikandar Ali, Hao Zhang, Xingbang Wang, Bingzhi Liao, Xufeng Wang
Abstract
Accurately estimating aboveground forest biomass is essential for understanding regional and global carbon cycles. In subalpine forest regions, complex topography and challenging forest inventories introduce significant uncertainties in estimating structural parameters and aboveground biomass. To generate continuous biomass maps over large-scale mountainous forests, it's necessary to extrapolate biomass from sample plots using appropriate models and remote sensing data. This study utilized ground survey data, UAV-LiDAR data, GEDI data, Sentinel-1, and Landsat 8 OLI imagery to estimate forest biomass in Qilian Mountain National Park. Key findings include: (1) UAV-LiDAR-derived parameters, when integrated with a random forest model, effectively predict forest biomass at the sample plot level; (2) combining GEDI data with UAV-LiDAR estimates provides accurate biomass predictions at footprint points; (3) by extrapolating biomass from discrete GEDI footprints and incorporating variables from Sentinel-1 and Landsat 8 OLI, a continuous, high-accuracy forest biomass map for the entire Qilian Mountain National Park was generated (R² = 0.66, RMSE = 19.08 Mg/ha, rRMSE = 11.04%). This study successfully scaled biomass estimates from plots to a regional level, enhancing biomass estimation accuracy in mountainous regions by leveraging complementary data sources.