# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 4 Discussion  
### Optimization Verification of Parameters Automatically Obtained  
[PERSON] et al. [24] proposed an individual tree segmentation method based on iterative DBSCAN cylinder volume clustering analysis, requiring manual input of two parameters \(\varepsilon\) and _MinPts_. If the value of \(\varepsilon\) is set very small, a large portion of the data set will not be clustered into meaningful clusters but will be treated as outliers because it does not satisfy the number of points within a particular region to define a dense region. If the \(\varepsilon\) value is set too high, most of the valuable and meaningless data will be merged into the same cluster. If the number of points in the cluster is greater than or equal to _MinPts_, then these points are considered part of the cluster. If the number of points in the cluster is less than _MinPts_, then these points are considered noise points or outliers. Our improved DBSCAN method can automatically obtain the optimal parameters \(\varepsilon\) and _MinPts_. The choice of these two parameters will affect the result of the trunk detection. The result of trunk detection is related to the number of trees. During the experiment, \(N\) groups of parameters \(\varepsilon\) and _MinPts_ are successively taken as the input parameters of the DBSCAN algorithm to obtain the cluster number corresponding to each group of parameters. If there is no change in the number of clusters obtained by continuous multiple groups of parameters, the group of parameters is considered to have the best adaptability. The automatically obtained \(\varepsilon\) is 0.72 and _MinPts_ is 9 in Plot 1. When \(\varepsilon\) is in the range of 0.2 to 0.75, the cluster number does not change (Figure 10a). Therefore, the obtained parameters are the optimal values in Plot 1. The automatically obtained \(\varepsilon\) is 0.76 and _MinPts_ is 15 in Plot KA09. When \(\varepsilon\) is in the range of 0.25 to 0.76, the cluster number does not change (Figure 10b). Therefore, the obtained parameters are the optimal values in Plot KA09. When \(\varepsilon\) is small, the cluster number is large, and there will be more over-segmentation errors in the segmentation results (Figure 11). In the layer-by-layer clustering process of regional growth, the wrong trunks are assigned the remaining point clouds, which is meaningless to the wrong trees. When \(\varepsilon\) is large, the cluster number is small, and there will be more under-segmentation errors in the segmentation results (Figure 12). Because the value of \(\varepsilon\) is set too large, the trunk detection method considers that these point clouds belong to the same trunk and fails to segment them correctly. Finally, the result of individual tree segmentation is to allocate the point clouds of multiple trees to one tree. So when the cluster number does not change, the over-segmentation and under-segmentation errors in the segmentation result are the least. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Plot** & **Actual Tree Number** & **TP** & **FP** & **FN** & **Recall (\%)** & **Precision (\%)** & **F1-Score** \\ \hline Plot 1 & 47 & 39 & 5 & 8 & 82.98 & 88.64 & 0.86 \\ Plot 2 & 52 & 42 & 4 & 10 & 80.77 & 91.30 & 0.86 \\ Plot 3 & 46 & 41 & 2 & 5 & 89.13 & 95.35 & 0.92 \\ Plot 4 & 33 & 28 & 2 & 5 & 84.85 & 93.33 & 0.89 \\ Total artificial forest & 178 & 150 & 13 & 28 & 84.27 & 92.02 & 0.88 \\ BR04 & 56 & 45 & 3 & 11 & 80.36 & 93.75 & 0.87 \\ KA09 & 39 & 33 & 3 & 6 & 84.62 & 91.67 & 0.88 \\ Total mixed forest & 95 & 78 & 6 & 17 & 82.11 & 92.86 & 0.87 \\ Total & 273 & 228 & 19 & 45 & 83.52 & 92.31 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Segmentation results based on [PERSON]’s segmentation method. The results show that the parameters obtained automatically by the proposed method are optimal in artificial forest and mixed forest.