2026-10-06T06:07:08Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34047778 2026-10-01T17:27:26Z category_272 category_734 category_931 category_64 portal_87 item_type_6 month_year_10_2026

Hierarchical upscaling forest biomass mapping by fusing UAV-LiDAR, calibrated GEDI, and ALOS-2 PALSAR-2 data Jiaqi Hu (1425412) Wangfei Zhang (17286535) Huaiqing Zhang (14569721) Yongjie Ji (2301016) Mengjin Wang (16913676) Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Aboveground biomass (AGB) hierarchical upscaling data fusion unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) Global Ecosystem Dynamics Investigation (GEDI) Advanced Land Observing Satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding the global carbon cycle and supporting carbon neutrality. However, conventional methods relying on single remote sensing sources are often constrained by signal saturation resulting from limited vertical detection capability and by their limited ability to characterize changes in AGB across large-scale subtropical regions characterized by complex topography and high spatial heterogeneity. To address this, we proposed a hierarchical multi-source inversion framework integrating unmanned aerial vehicle (UAV) light detection and ranging (LiDAR), Global Ecosystem Dynamics Investigation (GEDI), Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2), and Sentinel-2. The framework employs a three-step upscaling strategy to progressively transfer inversion accuracy. First, a high-precision local reference layer was generated using UAV LiDAR and field plots to serve as reliable ground truth. Next, this layer was used to correct GEDI L4A products using machine learning models, and then the approach with best performance was selected for footprint-level estimation. Finally, corrected GEDI footprints were combined with PALSAR-2 structural metrics and Sentinel-2 spectral features to produce a wall-to-wall AGB map with resolution of 30 m. Results showed that UAV-based correction significantly improved GEDI accuracy, yielding an R2 of 0.78 and a root mean square error (RMSE) of 19.97 Mg/ha, the total uncertainty with pixel-level was controlled within 36.26 Mg/ha. In conclusion, the proposed framework, leveraging physical mechanism feature guidance, offers a novel and robust strategy for the high-precision inversion of forest AGB across large-scale with complex topography and high spatial heterogeneity. 2026-10-01T17:27:26Z Text Journal contribution 10.6084/m9.figshare.34047778.v1 https://figshare.com/articles/journal_contribution/Hierarchical_upscaling_forest_biomass_mapping_by_fusing_UAV-LiDAR_calibrated_GEDI_and_ALOS-2_PALSAR-2_data/34047778 CC BY 4.0