# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
## 1 Introduction
A forest point cloud can also be segmented by employing characteristic analysis based on differential quantity, including normal vector and principal curvature. For example, given that the directions of the normal vector of the trunk points are in general consistent, trunks in a terrestrial scanning forest point cloud can be detected and then the individual trees can be isolated [12]. The second category of forest point cloud segmentation mainly emphasizes tree crown segmentation or tree extraction through tree crown segmentation. Some methods emphasize using a priori knowledge, density analysis, clustering, and region growing. To obtain the accurate tree crown model with a complex structure from airborne LiDAR data for latter feature extraction, the method of [PERSON] et al. [13] consists of several phases: filtering, transformation to a grayscale image, contrast enhancement, the opening and closing operation, and the watershed segmentation. [PERSON] et al. [14] completed the initial canopy segmentation using a normalized cut segmentation with a priori knowledge about the position of each tree. Given that closely located and intersected trees are often clustered together as multi-tree components, [PERSON] et al. [15] suggested a tree-shaped model-based continuously adaptive mean shift algorithm. [PERSON] et al. [16] presented an algorithm for individual tree crown delineation that uses the excess green index, the marker-controlled watershed segmentation, the region growing algorithms, and a buffer around a treetop. [PERSON] et al. [17] adopted a point cloud density model, a local maximum algorithm with optimal window size, to improve the watershed algorithm for extracting the tree crowns. [PERSON] et al. [18] adopted the unsupervised mean shift clustering to segment a forest point cloud and then used the 3D ellipsoid model to fit the points of each cluster. By this way, the geometrical tree parameter's location, height, and crown radius are extracted. [PERSON] [19] built a multi-layered tree extraction method using a graph-based segmentation algorithm for segmenting the canopy and the sliding window detection method for other parts of understory trees. Other literature put more attention to the geometry shape or topology information of the tree crown. [PERSON] [20] proposed an approach to the tree crown segmentation process that combines the seeded region growing with an active contour to approximate a crown boundary. For correcting deviations caused by topography based on individual tree crown segmentation, [PERSON] et al. [21] took into account the weight with normalized canopy height and the precise Digital Elevation Model (DEM) derived from the point cloud that is classified by a multi-scale curvature classification algorithm. For airborne LiDAR data, [PERSON] et al. [22] proposed a segmentation method that captures the forest's topological structure in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This bottom-up segmentation strategy is based on several quantifiable cohesion criteria that measure belief on two crown components. In addition, a study found that combining airborne laser scanning with multiple and hyperspectral data could improve the accuracy of tree crown segmentation [23]. These representative methods are very diverse in tree crown reconstruction, a critical step of tree reconstruction [24]. [PERSON] [25] used the obtained vector model to extract features and reconstruct single tree crowns from laser scanner data. [PERSON] et al. [26] proposed an approach for tree crown reconstruction based on improving alpha-shape modeling, where the data are points unevenly distributed in a volume rather than on a surface only.