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 (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
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 is necessary to extrapolate biomass from sample plots using appropriate models and remote sensing data. This study utilized ground survey data, unmanned aerial vehicle (UAV) -Light detection and ranging (LiDAR) data, Global Ecosystem Dynamics Investigation (GEDI) data, Sentinel-1, and Landsat 8 Operational Land Imager (OLI) imagery to estimate forest biomass in Qilian Mountain National Park. Key findings include the following: First, UAV-LiDAR-derived parameters, when integrated with a random forest model, effectively predict forest biomass at the sample plot level. Second, combining GEDI data with UAV-LiDAR estimates provides the accurate biomass predictions at footprint points. Third, 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, root-mean-square error = 19.08 Mg/ha, and relative root-mean-square error = 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.