ALE-CS-NGMS: An advanced approach for individual tree stem- and branch- scale structural parameters extraction using ULS and BLS point clouds

• An advanced approach was proposed for tree structural parameter estimation. • An adaptive ellipse fitting algorithm was developed to extract stem diameter. • A vertical profile-based separation model was built to identify tree canopy and stem. • Neighborhood graphs and minimum spanning were integrated to extract stem path. • ALE-CS-NGMS performed well in fine-scale tree structural parameter extraction. Individual tree-level fine-scale (especially stem- and branch- scales) structural parameters constitute a critical foundation for tree structural trait assessments, biomass component estimations and tree physiological property evaluations. However, the Unmanned Aerial Vehicle (UAV) Laser Scanning (ULS) has limitations in sampling distance and penetrating capability in dense tree canopies, thus restricting its ability to extract detailed stem- and branch-scale structural parameters. The emergence of both advanced ULS and Backpack Laser Scanning (BLS) technologies have potential to precisely extract fine-scale structural parameters of individual trees. In this study, we proposed an advanced ALE-CS-NGMS approach for individual tree ( Poplar ( Populus spp.)) stem- and branch- scale structural parameters extraction by ULS and BLS point clouds. First, an Adaptive Least-squares Ellipse (ALE) fitting algorithm was developed to accurately derive the stem diameter of individual trees. Second, a Canopy-stem Separation (CS) model was built by identifying canopy point cloud through derivatives based on the vertical distribution profile of individual trees, while canopy volume was delineated by the AlphaShape as well as a voxel-based algorithm. Finally, a method integrating Neighborhood Graphs and Minimum Spanning (NGMS) was developed to extract individual tree stem, and stem taper curves were fitted to estimate individual-tree stem volume. The results demonstrated that the developed ALE approach yielded a root mean square error (RMSE) of 2.87 cm, representing an accuracy enhancement approximately 0.47 cm for DBH estimation. The NGMS approach produced RMSEs of 0.33 m 3 and 0.40 m 3 for stem volume estimation by using BLS and BLS + ULS data. The CS model achieved RMSEs of 6.48 m 3 and 3.48 m 3 for canopy volume estimation with the BLS and BLS + ULS data, respectively. Branch inclination angles exhibited an increase with stand age, generally ranging between 60° and 100°. The distribution of branch inclination across stands of varying ages revealed that in the 8-year-old and 12-year-old plots, branch angles fell within the 60°-90° interval.