Tree structural modeling from leaf-on point clouds for biomass carbon stock estimation

Urban trees represent valuable renewable biomass sources, but traditional allometric equations inadequately capture structural variability in urban environments. Therefore, considering tree structure is crucial for accurate biomass measurement. While LiDAR-based 3D modeling can reflect tree architecture, generating reliable models from dense foliage point clouds has remained difficult. To address this challenge, we developed the Skeleton Generative Method (SkeletonGM), which reconstructs tree trunks and primary branches from mobile laser scanning data even under heavy foliation. SkeletonGM produces a clarified skeletal point cloud that is subsequently converted into 3D tree models using AdTree; aboveground woody biomass is then calculated by combining the estimated volume with species-specific wood density. To validate the proposed method, we applied SkeletonGM to 33 hinoki cypress trees in a forest stand and 25 dawn redwood trees in a park, and compared the resulting biomass estimates with values derived from allometric equations. The results showed strong agreement with the reference equations (R² = 0.95 and 0.94; mean absolute percentage error = 14.1% and 14.6%). These findings indicate that the proposed method has strong potential to improve the accuracy of biomass assessment for urban trees.