Automatic Forest Mapping at Individual Tree Levels from Terrestrial Laser Scanning Point Clouds with a Hierarchical Minimum Cut Method

Laser scanning technology plays an important role in forest inventory, as it enables accurate 3D information capturing in a fast and environmentally-friendly manner. The goal of this study is to develop methods for detecting and discriminating individual trees from TLS point clouds of five plots in a boreal coniferous forest. The proposed hierarchical minimum cut method adopts the detected trunk points that are recognized according to pole like shape segmentation as foreground seed points and other points as background seed points, respectively. It constructs the undirected weighted graph of the foreground and background seed points to deduce a cost function for tree crown point segmentation with the decreasing ranking of tree trunk heights. The intermediate results lead to global optimization segmentation of individual trees in a hierarchical order. Finally, the structure metrics of the detected individual trees are calculated and checked with field observations. Plots with different attributes were selected to verify the proposed method, and the experimental studies show that the proposed method is efficient and robust for extracting individual trees from TLS point clouds in terms of the recall of 90.42%.