## 2 Materials and Methods
### Methodology
#### 2.3.3 ITS in Leaf-On Conditions
To eliminate the error caused by deforestation, a k-nearest neighbor algorithm was applied to evaluate whether each initial seed point is qualified. This is a re-screening of the seed points to match those points calculated from LiDAR data in leaf-off season with that in leaf-on season. The horizontal Euclidean distance between each seed point and its five neighbors in the point cloud from the leaf-on season was calculated first, and then all the distances from the target seed and its neighbors were summed. The sum of the distances was compared with a threshold (1.5 m in this study) to judge whether the specific seed point is valid (Figure 5). During the second step, three segmentation methods (seed points could be applied to these methods) including Kmeans, region growth, and Voronoi polygon were compared. Kmeans, a common clustering method, determines optimum clusters by continuously iterating center points (iteration times were set to 10 by default). A good starting center point has a strong capacity to achieve good segmentation results from point clouds based on the Kmeans method [46]. We used the center point obtained after clustering with the Mean Shift algorithm (Figure 6a) as the seed point for the Kmeans algorithm. The region growth [34] is an ITS method based on the regional growth of the seed points. Figure 5: Example of the initial seed point in the data view from leaf-off (**a**) and leaf-on (**b**) seasons (the seed point in the red circle was removed by the re-screening method). Since the initial growth point for this method is the highest location in a specific region, we clustered around the seed points and took those with the highest z-value (where the z-value represents the height of normalized point clouds) in each cluster as the new growth point (Figure 6b). The diameters of the circle moving window in this regional growth method are the same as Dalponte2016 (Table 3). Thus, the segmentation of the Dalpnote2016 algorithm was conducted using accurate seed points which were treetop points extracted by ITD in the leaf-off season. The third method is to construct a Voronoi polygon directly based on the seed points (the center points) in each sample plot and split the point clouds in a 2D plane with this polygon as reference [60]. We set the max value parameter of a crown diameter (_max_cr_factor_) at 0.6 in the segmentation using Voronoi polygon, so that the diameter will not exceed 60% of the tree height, which was in line with the characteristics of the forest and could reduce a certain degree of under-segmentation [60]. We located the position difference between the two seed points (center and top) and the reference points by the field RTK (Figure 6c). After applying these three seed points based ITS methods, individual trees were extracted. However, there were still some errors in the results, especially around tree edges (Figure 7a). To improve accuracy for ITS, the DBSCAN algorithm was applied on the initial individual tree points in the horizontal plane. According to the concept of the DBSCAN algorithm regarding sample point tightness [61], the initial individual tree points were divided into a variety of new point clusters (Figure 7b). Then, three tree-crown features in a horizontal plane--the number of points of the cluster (N), the crown area of the cluster (A), and the crown shape of the cluster (S)--were calculated to evaluate the right cluster for the individual tree. Feature A was measured by the area of the convex hull by applying a convex hull algorithm on each cluster from the initial individual tree points. Feature S was measured by the proportion of the first eigenvalue in all eigenvalues by applying the principal components analysis (PCA) on each cluster of the initial individual tree points (i.e., if the S of one cluster is high, its shape is more likely to be elongated which does not match the actual shape of a tree).