## 4 Discussion  
### The Extraction of Seed Points by the ITS Methods  
It is difficult to select the optimal threshold for ITDS from LiDAR data in a dense forest, especially when applying these methods to 3D point clouds rather than a CHM raster [41]. However, the identification and segmentation of individual trees during the leaf-off season is relatively easier and the accuracy of ITD is easier to judge. In the leaf-off condition, trunk points and thresholds for the ITD methods are relatively simple to identify using our method.  
Although the seed points extracted by Local Minimum have satisfactory accuracy, the Mean Shift method still performs slightly better with the exception of plots Y14-2 and Y20-3. The influence of low trees at Y14-2 and the large branch gap at Y20-3 is responsible for the slight decrease in the performance of the Mean Shift method. We selected the Mean Shift method as the most accurate for seed points selection and ITD because the position of seed points extracted by this algorithm is closer to the tree reference locations (Figure 4). Seed points extracted by the Mean Shift method are more adaptive to the Seeds + Kmeans and Seeds + Silva2016 methods (the two seed points based ITS methods). Although high ITD accuracy is associated with the Local Minimum method (Table 4), the location of its seed points typically deviated from the reference tree locations due to tree tilt.  
By conducting the Mean Shift algorithm on point clouds in the 2D plane instead of 3D space in our study, we reduced computational complexity. This speeds the process of Mean Shift algorithm. The connectivity of some individual tree trunk points was poor even under leaf-off conditions (examples in Figure 6b), which may have greatly affected the accuracy of applying this algorithm on point clouds in 3D space [47]. If the Mean Shift method was directly adopted in the 3D space, a great number of clusters would be generated, with its own challenges and errors for ITD and seed points selection. It is for this reason that we applied the DBSCAN algorithm on the point clouds in the 2D plane during the refining process (Section 2.3.3).  
We used a moving circular window of fixed size to extract tree top and tree bottom points. Other studies have shown that an adaptive size moving window (typically implemented by functions of tree height and window size) would perform better for ITD [62]. Due to the similar tree heights among sample plots in our study area, we did not explore an adaptive size moving window. It is also worth noting that we did not investigate an adaptive bandwidth size for the Mean Shift algorithm, which avoids parameter selection problems due to different crown widths [63]. The similar canopy size of plantation trees  
Figure 10: Boxplot of two indicators (horizontal area (**a**) and vertical range (**b**) of segmented individual trees) of the three methods (manually, segmentation with the refining process, and segmentation without the refining process).  
and clear spacing between crowns in the leaf-off season made it easy to select different bandwidths in each sample plot in our study.  
Although we developed this ITS framework based on point clouds during both leaf-on and leaf-off seasons, it is also promising to apply this framework to LiDAR data in a single time phase with high density and connectivity of tree trunk point clouds. With the development of UAV-LiDAR systems, several high-frequency LiDAR sensors have tremendous capacity to detect tree trunks in deciduous forests during the leaf-on season [44]. There are many algorithms for forest tree trunk extraction [64; 65], which suggest a new direction for trunk-based seed points extraction in our research.