Abstract: To address the problem of reduced segmentation accuracy in current ground-based tree point cloud segmentation methods caused by insufficient integration of local and global feature information, this paper proposes a tree segmentation method for forest sample plots based on spatial proximity relationship. This method first establishes the spatial relationship and separates the ground points by constructing an octree index. On this basis, a multi-stage random forest model is developed to achieve progressive segmentation of the tree point cloud. Specifically, the two-dimensional morphological features and spatial properties of tree trunk cross-section are used to accurately segment tree trunk point cloud. Subsequently, based on the segmentation results, the optimized ensemble of shape functions (ESF) feature description operator is used to obtain the spatial connectivity features between the tree trunk and the corresponding tree crown point cloud, enabling crown segmentation by incorporating dimensional properties of the tree crown point cloud. Finally, the structural parameters are extracted for single trees and validated against field measurements. Experiments using two sets of mobile laser scanning point cloud data show that the proposed method achieves tree segmentation recall rates of 90.57% and 90.05%, with accuracies of 93.20% and 95.47%, respectively.

Key words: forest, multi-stage random forest, two-dimensional morphological feature, spatial relationship, optimized ensemble of shape functions (ESF) feature