# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 1 Introduction  
With the development and popularization of light detection and ranging (LiDAR) technology in forestry, LiDAR point clouds have been widely used in the extraction of individual tree parameters such as tree height [1], diameter at breast height (DBH) [2], crown diameter [3], volume [4], biomass [5], and forest pest control [6]. As the key process of extracting tree parameters and monitoring the changes of significance indicators of forest ecosystem [7], the accuracy of individual tree segmentation is closely related to subsequent work. However, the top-down data acquisition mode is difficult to obtain detailed understory information of trunks and branches [8], which limits its further fine research and application on the individual tree scale [9]. Terrestrial laser scanning (TLS) has comparable advantages in airborne LiDAR and unmanned aerial vehicle (UAV) LiDAR, such as simple operation, efficient, convenient, and no need to carry other equipment [10; 11; 12]. However, it is characterized by high density of TLS data and incomplete detection of upper canopy. In addition, facing the complex forest with dense environment and different sizesof individual tree structures, there are still challenges in individual tree segmentation for TLS point clouds [13; 14]. At present, some studies directly apply the individual tree segmentation method for airborne LiDAR to TLS point clouds. These methods can be divided into two categories: based on the canopy height model (CHM) [15] and based on point clouds [16]. CHM is a raster image obtained by interpolation of point clouds describing the top of the canopy [17]. The segmentation method based on point clouds needs to find the highest point of trees based on K-means clustering [18] and watershed algorithm [19] to carry out the next clustering. However, due to the shielding effect and low density of TLS point clouds at the top of canopy, it is difficult to identify treetops and other useful canopy features. Therefore, although there are some mature methods for individual tree segmentation of airborne LiDAR [20], these methods for segmentation by identifying canopy height cannot be directly translated and applied to TLS point clouds [14], due to different observation geometry and characteristics of point clouds. There are also some studies on segmenting urban street trees of TLS point clouds [21], but these trees are regularly distributed. This method has low applicability to the segmentation of TLS point clouds in forest plots. [PERSON] et al. [22] developed an adaptive clustering method to segment individual trees in a pine forest for the original LiDAR point clouds. This method is similar to the concept of watershed segmentation. However, it requires sufficient training data for supervised learning, and its performance in complex forests has not been tested. [PERSON] et al. [11] proposed a top-down hierarchical segmentation method of TLS point clouds. The method divides a large area of point clouds into local point sets through spatial clustering, and then further divides the core area and overlapping area of the tree. [PERSON] et al. [23] proposed a method to segment TLS individual tree point clouds based on voxel layer by layer clustering. The method obtained the position of individual trees by analyzing the vertical z-value sequence and clustering layer by layer based on fuzzy C-means algorithm. However, this method was only applied to separate individual trees of a mongolian oak (_Quercus mongolica [PERSON]_) plantation in deciduous stage. [PERSON] et al. [14] proposed a tree crown segmentation method based on ecological theory. The method first identifies the trunk through density-based spatial clustering of applications with noise (DBSCAN) algorithm or circular detection method, and then divides the tree crown based on the shortest path algorithm, which achieved high segmentation accuracy in different test data. [PERSON] et al. [24] proposed an individual tree segmentation method based on iterative DBSCAN cylinder volume clustering analysis.