## 5 Conclusions  
In conclusion, in this study we developed a new ITDS strategy to effectively use UAV-LiDAR point clouds in dense deciduous broadleaved forests. Seed points were extracted by the Mean Shift method based on the leaf-off season point clouds, obtaining an initial ITS result based on those points and the Voronoi polygon (Silva2016) from the leaf-on point clouds. The initial ITS results were then re-segmented to improve accuracy by a DBSCAN and kNN classification refining process. We find it promising that this method can be applied to single-phase point clouds (i.e., if the data capture enough trunk points) with improvements in UAV-LiDAR surveys--the higher density and higher echo point clouds may better describe forest trunk points. Our detailed conclusions are as follows: (1) the two point-cloud-based ITD methods (the Local Minimum (\(r\) = 0.99, \(p\) = 0.99, \(F\) = 0.99) and Mean Shift methods (\(r\) = 0.99, \(p\) = 0.99, \(F\) = 0.99)) perform better for seed selection and ITD from the LiDAR data during the leaf-off season than the CHM-based ITD method (Local Maximum (\(r\) = 0.92, \(p\) = 0.94, \(F\) = 0.93). (2) The Mean Shift method performs slightly better in different sample plots, because the position of seed points is closer to the reference location of trees. (3) Compared to the four direct ITS methods (Dalponte2016: \(F\) = 0.91, Li2012: \(F\) = 0.91, PTees: \(F\) = 0.88, LayerStacking: \(F\) = 0.90), the three seed points (obtained from ITD results using the Mean Shift method on the UAV-LiDAR data of the leaf-off season) based ITS methods (Seeds + Kmeans: \(F\) = 0.98, Seeds + Dalponte2016: \(F\) = 0.98, Seeds + Silva2016: \(F\) = 0.99) have significantly greater capacity for ITS from UAV-LiDAR data in leaf-on seasons. (4) The Seeds + Silva2016 method achieves the highest overall accuracy because its segmentation results are more stable based on seed points and better in plantations with uniform crown sizes. (5) The refining process of DBSCAN and kNN classification algorithms significantly mitigates edge errors and improves the ITS accuracy. (6) The ITDS framework proposed in this study has an operation time advantage (median processing \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Median Time (s)** & **Maximum Time (s)** & **Minimum Time (s)** & **Mean Time(s)** \\ \hline Dalponte2016 & 0.47 & 5.66 & 0.23 & 0.88 \\ Li2012 & 28.17 & 1070.41 & 5.59 & 196.88 \\ PTees & 17.00 & 354.90 & 12.18 & 56.15 \\ LayerStacking & 38.38 & 1008.69 & 20.26 & 143.37 \\ Seeds + Kmeans & 3.88 & 10.81 & 3.28 & 3.97 \\ Seeds + Dalponte2016 & 4.37 & 11.17 & 2.04 & 4.41 \\ Seeds + Silva2016 & 4.00 & 10.87 & 1.98 & 4.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The descriptive statistics of the processing time of all the sample plots for the four direct ITS methods and the three seed points based ITS methods. Figure 12: The refining process (iterative classification). The horizontal projection (**a**) and the vertical profile (**b**) of the core trees (purple and yellow) and the neighbor points (black) with the eps setting of 0.4. (**c**) The reclassification results using the iterative classification process. (**d**) A new iteration with the eps set to 0.55.  
time of the three seed points based ITS methods are all less than 5 s), which enhances its capacity to be applied on larger regions or more complex forests using high-density UAV-LiDAR point clouds. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15061619/s1](https://www.mdpi.com/article/10.3390/rs15061619/s1)]([https://www.mdpi.com/article/10.3390/rs15061619/s1](https://www.mdpi.com/article/10.3390/rs15061619/s1)), Supplementary Materials A: The explanation of k value selection for KNN; python script; R script. Conceptualization, D.X. ; methodology, Y.P. ; validation, Y.P. and H.W. ; formal analysis, Y.P. ; investigation, Y.P., H.W., and X.L. ; resources, D.X. ; data curation, X.X. ; writing--original draft preparation, Y.P. ; writing--review and editing, D.X.