Accurate mapping of aboveground biomass (AGB) in mountainous forests is essential for carbon stock assessment and ecological management, yet remains challenging due to the difficulty of linking local high-precision observations with regionally continuous coverage. To address this issue, we developed a hierarchical framework integrating local reference construction, UAV–GEDI bridging, footprint-level modeling, and regional continuous mapping, applied to the mountainous forests of Simao District, Pu’er City, Yunnan Province, China. Field plot measurements and UAV-borne LiDAR data were first used to construct a local AGB reference product, which was then transferred to the GEDI footprint scale through geolocation correction and footprint-scale quality control, yielding 252 valid bridging samples across three UAV flight zones, with approximately 65% originating from the TYH zone. Among five candidate models evaluated for GEDI footprint-level AGB estimation, the Stacking ensemble model performed best, with a pooled out-of-fold R2 of 0.736 and RMSE of 24.15 Mg ha−1, and was subsequently applied to 89,579 GEDI footprints across the study area. For regional continuous mapping, the empirical Bayesian kriging regression prediction (EBKRP) scheme combining Landsat TCW, Sentinel-2 IRECI, and the Sentinel-1 polarization ratio achieved the best external validation performance, with R2 of 0.622 and RMSE of 26.05 Mg ha−1 based on 61 independent field plots. These results indicate that the proposed hierarchical framework effectively bridges local high-precision observations and regional continuous AGB mapping in complex mountainous forest environments, offering a systematic methodological reference for GEDI-based forest carbon monitoring.
Yang et al. (Mon,) studied this question.