Tree Skeletonization with DBSCAN Clustering Using Terrestrial Laser Scanning Data

Abstract

A tree skeleton reflects the geometric and structural characteristics of a tree. Terrestrial laser scanning (TLS) provides an effective means to construct tree skeletons that can capture the surface and topological features of trees. However, it is difficult to construct a tree skeleton located at the geometric centre of the tree with a detailed hierarchy of branches because of the natural intricate topology of the tree and the defects of the tree point cloud scanned by TLS. In this study, we presented a tree-skeletonization method based on density-based spatial clustering of applications with noise (DBSCAN) using TLS data. First, outliers were removed using DBSCAN, and the point-traversal order of each point was recorded. Next, a tree point cloud was divided into several tree slices using contour planes, and several tree segments were obtained by applying DBSCAN to each tree slice. Tree skeleton points were retrieved from each tree segment after the point-inversion transformation. Then, the adjacent relationship between skeleton points and the flow weight of each skeleton point was calculated based on the point-traversal order. After that, the skeleton points were classified into stems and different levels of branch points using the flow weights of the skeleton points, and the branch hierarchies were identified. Finally, the tree skeleton was optimized using the angle consistency. The positional deviation dp and directivity deviation dd were presented in this study to verify the validity of the constructed tree skeleton. From the visualization results, the constructed tree skeleton effectively reflected the geometrical structure and branch hierarchies of the tree. The averages of dp and dd were 0.418 cm and 8.474 degrees, respectively. The results demonstrated the validity of the presented method. It will help improve the visualization and accurate measurement of trees.