基于背包式激光雷达数据的单木骨架提取OA

Single-tree skeleton extraction is a critical step in 3D tree modeling,holding significant importance for preci-sion forestry and forest resource management.Backpack LiDAR scanning(BLS),as an emerging mobile measurement technology,offers advantages in flexibility and portability.However,its point cloud data suffers from uneven distribu-tion and noise interference,which affect the accuracy of skeleton extraction.To address these issues,this study focuses on Cunninghamia lanceolata in the State-owned Gaofeng Forest Farm of Guangxi Zhuang Autonomous Region and pro-poses a hierarchical progressive skeleton extraction method based on key path detection using BLS data.This approach integrates geometric constraints and hierarchical analysis to achieve precise localization of branch axes,while employing perpendicular bisector intersection calculations to construct a continuous and topologically complete single-tree skeleton.Terrestrial laser scanning(TLS)data was used as validation data,and preprocessing techniques such as voxel filtering and local elevation normalization are applied to enhance BLS data quality.The results indicate that the proposed method exhibited high performance in branch classification.F1-scores ranged from 0.771 to 0.788,with precision ranging from 93.33%to 100%,and recall ranging from 66.67%to 90.63%.Furthermore,the comparative analysis of branch angle estimations based on BLS and TLS data yields a coefficient of determination(R²)of 0.84 and a root mean square error(RMSE)of 7.22°.This study provides a high-precision technical framework for single-tree 3D modeling,laying a data foundation for forest resource management and ecological simulation.