A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization

• Developing a terrain-adaptive point cloud fusion strategy to enhance data accuracy. • Building a framework for AGB estimation using fused point clouds in forest research. • Establishing an individual tree ABG model for Pinus kesiya var . langbianensis . Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis , exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. To enhance AGB estimation accuracy, we integrated unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) point clouds using the Iterative Closest Point (ICP) algorithm for precise registration and fusion under varying slope conditions, enabling 3D tree reconstruction. Furthermore, a height-filtered segmentation strategy was introduced to further enhance registration accuracy by aligning ULS points above 2 m with BLS data. The results showed that: 1) Unnormalized point clouds exhibited better registration performance than normalized ones, indicating that slope has a significant impact on registration accuracy. 2) The segmentation-driven registration method performed best in integrating tree trunks and crowns, with the lowest registration error (RMSE = 0.2581 m) observed in the mid-slope areas (15-25°). In contrast, higher registration errors were found in steep (>25°) and gentle (0-15°) slopes, with RMSE of 0.2976 m and 0.2814 m, respectively. 3) The AGB allometric equation derived from the felled trees demonstrated high accuracy (R 2 = 0.9879, RMSE = 22.9243 kg). 4) The individual tree AGB estimation based on breast height (DBH) and height (H) extracted from the fused point clouds had R 2 values ranging from 0.8721 to 0.9730, with RMSE between 20.0242 kg to 48.6254 kg. This framework provides valuable insights for accurate forest resource surveys and management in complex terrain and highly heterogeneous regions.