## 4 Discussion
### ITS Methods with or without Seed Points  
Among all seven ITS methods we explored, the results of PTrees have the lowest accuracy (Table 5), which appears to be due them relying on the kNN algorithm which is sensitive to point cloud density [58]. Due to the unified point cloud density of all sample plots calculated using a thinning algorithm, the parameters of the PTrees method are difficult to set, resulting in poor ITS performance. We found a large difference among the four direct ITS and three seed points based ITS methods in sample plot Y12-2 (Figure 9). We attribute this to the accuracy of direct ITS methods clearly decreasing in dense forests or forest plantations with similar tree heights (the planting spacing of the sample plot Y12-2 is small; Table 1). However, in this situation, the accuracy of the three seed points based ITS methods remains stable (Figure 9). The low accuracy of the four direct ITS methods in the sample plot Y12-2 may also be due to small trees (low poplars with small crowns) beside larger individuals causing omission errors. It is worth noting that although Y12-1 also has the same low planting spacing, its segmentation is relatively high as the number of surviving trees in this plot is small (Table 1). The spatial resolution of CHM and the degree of CHM smoothing affect seed point extraction and ITS results [66]. CHM with higher spatial resolution tends to have better results [66], which is an advantage with UAV-LiDAR data because of its significantly higher point density compared to airborne LiDAR. The re-screening process has little effect on results because the difference in the number of trees between the two seasons is small (Table 1). In addition, re-screening is not needed when applying this ITDS framework on the UAV-LiDAR data from a single time phase or among seasons without new plantations or harvested trees. In addition to error sources from seed point selection point clouds during the leaf-off season with the seed points based ITS methods, the errors in the Seeds + Kmeans method also arose from iterating the central points. The purpose of the iterative process is to better identify cluster centers, but the center points of some small individual trees in the sample plot could move it to the wrong position (Figure 11b). For the Seeds + Dalpnote2016 method, two main error sources exist. First, the treetops of Mean Shift clusters during the leaf-off season are sometimes extracted in the wrong position (i.e., errors from the ITD process under leaf-off position), because of the branches from neighboring trees (Figure 11c). Second, the process of CHM interpolation can also incur errors, and treetop positions can differ between the leaf-on and leaf-off seasons (Figure 11c). The greatest problem for the Seeds + Silva2016 method is segmentation of crown edges (Figure 7a). This is because it is based on the Voronoi polygon that depends solely on Euclidean distance [60]. These errors for the three seed points based ITS methods were highly reduced in the refining process, but could not be totally eliminated for individual trees with high point cloud connectivity [66]. If the crowns of two neighboring trees overlap substantially, the refining process has limited effectiveness. However, we were unable to test the application of our methods in forests with more complex vertical structures in this study, as it may be difficult to accurately segment individual trees that grow under a dense forest canopy. The extraction of these parameters typically requires more advanced techniques [48]. For the evaluation parameters result of the process of refining, it is noticeable that these two parameters tend to have a bigger value in Y11 plot (Figure 10), which may be due to the highest planting spacing in this plantation (Table 1). Because the larger space between trees reduces the overlapping canopy, the extracted horizontal projection area is increased. It also increases the likelihood that the point cloud will detect the tree trunk below the canopy, thus increasing the vertical range.