A Triangulation-Based Technique for Tree-Top Detection in Heterogeneous Forest Structures Using High Density LiDAR Data

This letter presents a novel approach to tree-top detection in heterogeneous forest structures characterized by mixed species using high-density light detection and ranging (LiDAR) data. Although literature techniques can achieve accurate results in even-size and even-age homogeneous forests, they detect several false tree tops in forests characterized by variable crown dimensions. To solve this problem, the proposed method 1) identifies a preliminary set of candidate tree tops (CTPs) used to build a triangulated network; 2) performs an edge-based local forest analysis to identify groups of CTPs having the highest probability of belonging to the same crown; and 3) removes false tree tops according to a local directed graph analysis. To address large-scale forest analysis, the method exploits the Delaunay triangulation that efficiently defines a network topology made up only by relevant edges, thus sharply reducing the edge-based analyses to be performed. Given the triangulated network properties, the computational effort of the local analysis is not affected by the network size. The method has been tested in a mixed multi-layer multi-age forest located in the southern Italian Alps. The results obtained demonstrate that this computationally scalable algorithm outperforms standard tree-top detection methods increasing the overall detection accuracy up to 15.3%.