Segmentation of Individual Trees Based on a Point Cloud Clustering Method Using Airborne Lidar Data

摘要

The objective of this paper was to develop a new algorithm to segment individual trees directly by using the three-dimensional space characteristic of airborne light detection and ranging point cloud data. The local maximum method was used in the initial segmentation and the error identification tree exclusion. On the basis of the point cloud spatial distribution of individual trees and the adjacent relationship with the other trees, a point cloud clustering method was developed to decide the points belonging to the individual trees. This algorithm was tested by 6 forest plots in the Genhe forestry reserve. The results showed that this algorithm could segment individual trees quickly and accurately, and the overall accuracy of this algorithm was 96.3%.