## 2 Materials and Methods
### Methodology
#### 2.3.3 ITS in Leaf-On Conditions  
To explore the advantage of seed point detection in ITS in UAV-LiDAR point clouds of broadleaved plantations in the leaf-on season, four commonly used ITS methods were compared using the _lidR_ and _lidRplugins_ packages in R software [54]. The first ITS method is an up-bottom segmentation method used by applying a regional growth algorithm on the CHM raster [34]. This first ITS method requires the treetops first be identified from the CHM, which is the same as the local maximum ITD method based on the CHM from the leaf-off season. The diameter of the moving circle window was also determined in the trials using tree spacing in each plot (Table 3). The two important parameters for judging growth for this first ITS method (_th_seed_ and _th_cr_) were kept as algorithm defaults (0.45 and 0.55) in this study. The second ITS approach is a regional growth method (an up-bottom), combined with the threshold judgment, based on point cloud data rather than CHM raster [42]. For this second ITS method, the important index that decides which point belongs to which tree is the horizontal Euclidean distance between this point and treetops. Therefore, treetops need to be first selected by a local maximum algorithm based on the \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Plot**} & **Dlaponte2016–** & **Li2012–** & **PTrees—** & **Layer Stacking—** \\  & **Diameter (m) * & **Diameter (m) * & \(k1\)*, \(k2\)* & \(w1\)*, \(w2\)* \\ \hline Y8-1 & 5 & 5 & 500, 250 & 3, 1.5 \\ Y11-1 & 5 & 5 & 600, 300 & 3, 1.5 \\ Y12-1,2 & 4.5 & 4.5 & 600, 300 & 3, 1.5 \\ Y14-2 & 4 & 4 & 400, 200 & 4, 2 \\ Y14-4 & 4.5 & 4.5 & 600, 300 & 3, 1.5 \\ Y16-1 & 5.5 & 5.5 & 700, 350 & 7, 3.5 \\ Y16-2,3 & 4.5 & 4.5 & 600, 300 & 4, 2 \\ Y17-1,2 & 5.5 & 5.5 & 700, 350 & 7, 3.5 \\ Y20-3 & 6 & 6 & 1500, 750 & 7, 3.5 \\ \hline \end{tabular}  
* Diameter is the size of search windows in the Dalponte2016 and Li2012 algorithms; \(k1\), \(k2\) are scale parameters in PTrees algorithm; \(w1\) and \(w2\) are two search window sizes in the LayerStacking algorithm.  
\end{table} 
Table 3: Parameters of four segmentation methods among plots in the leaf-on season. data for this ITS method as well (Table 3). The threshold parameters of \(dt1\) and \(dt2\) were set as algorithm defaults (1.5 and 2). The third ITS method is PTrees, which is a multi-scale point cloud segmentation algorithm [58]. This method requires the absolute maxima be determined rather than the local maxima. The fundamental principle is to apply a k-nearest neighbor algorithm on the normalized point cloud and calculate the area of a convex hull from these k-nearest points in the 3D space. The primary parameter for this third method is the \(k\); two types of k values (\(k1\), \(k2\)) were set to meet multi-scale needs. The k values were selected based on the trials and the point cloud densities for different sample plots (Table 3). The fourth ITS method, layer stacking, is a segmentation algorithm based on the normalized point cloud data [59]. The principle of this method is to first slice the point clouds according to their heights, obtain the clusters in each layer, and finally locate the position of each tree using the high-density area by overlapping all layers' results. For this fourth method, the layer thickness was set as default (0.5) and the sizes of two search windows (\(w1\) and \(w2\)) were determined by trial for each sample plot (Table 3). We set \(w1\) as \(2\times w2\) and tested the performance of the algorithm on a scale of \(w2\) (from 1 to 5) and selected the best parameters. We also designed a segmentation framework based on seed points extracted from the leaf-off poplar plantation (see Section 2.3.2). The specific steps were as follows: (1) re-screening seed points, (2) implementing the segmentation method, and (3) refining segmentation results. The initial seed points were determined with the method with highest accuracy among all three ITD methods based on the LiDAR data from the leaf-off season.