## 5 Discussion  
### Comparison with Existing Methods  
Multiple ITS methods are used in the four plots to test separately, and the test results are shown in Table 7. The average \(p\), \(r\), \(F\), and \(time(min)\) of the SHDR method [12] in the four plots reached 0.84, 0.85, 0.84, and 23.3, respectively. The average \(p\), \(r\), \(F\), and \(time(min)\) of the DK method [10] in the four plots reached 0.66, 0.59, 0.62, and 12.5, respectively. The average \(p\), \(r\), \(F\), and \(time(min)\) of the Improved DK method in the four plots reached 0.83, 0.86, 0.85, and 14.9, respectively. The average \(p\), \(r\), \(F\), and \(time(min)\) of the HAIS method [25] in the four plots reached 0.79, 0.72, 0.75, and 13.7, respectively. The average \(p\), \(r\), \(F\), and \(time(min)\) of the proposed method in the four plots reached 0.91, 0.88, 0.90, and 13.5, respectively. The spatial horizontal distance rule (SHDR) is a top-down method. This method has high segmentation accuracy, but it has high time complexity and is sensitive to the selection of the horizontal distance threshold parameter, which leads to poor universality. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Plots** & **RCD** & **TN1** & **MT2** & **OE3** & **CE4** & \(p\) & \(r\) & \(F\) & _Time(Min)5_ \\ \hline \multirow{8}{*}{SHDR method} & U1 & 61.58\% & 132 & 91 & 25 & 12 & 0.88350 & 0.78448 & 0.83105 & 4.5 \\  & U2 & 69.09\% & 186 & 141 & 22 & 26 & 0.84431 & 0.86503 & 0.85455 & 4.7 \\  & G1 & 93.25\% & 108 & 94 & 13 & 18 & 0.83929 & 0.87850 & 0.85845 & 6.6 \\  & G2 & 96.40\% & 261 & 194 & 27 & 42 & 0.82203 & 0.87783 & 0.84902 & 7.5 \\  & Overall & 687 & 520 & 87 & 98 & 0.84728 & 0.85146 & 0.84826 & 23.3 \\ \hline \multirow{8}{*}{DK method} & U1 & 61.58\% & 132 & 56 & 40 & 38 & 0.59574 & 0.58333 & 0.58947 & 1.9 \\  & U2 & 69.09\% & 186 & 74 & 48 & 82 & 0.47436 & 0.60656 & 0.53237 & 2.2 \\  & G1 & 93.25\% & 108 & 61 & 35 & 18 & 0.77215 & 0.63542 & 0.69714 & 3.9 \\  & G2 & 96.40\% & 261 & 67 & 52 & 13 & 0.83750 & 0.56303 & 0.67337 & 4.5 \\  & Overall & 687 & 258 & 175 & 151 & 0.66993 & 0.59708 & 0.62308 & 12.5 \\ \hline \multirow{8}{*}{Improved DK method} & U1 & 61.58\% & 132 & 102 & 7 & 16 & 0.86441 & 0.93578 & 0.89868 & 2.7 \\  & U2 & 69.09\% & 186 & 140 & 19 & 22 & 0.86420 & 0.88050 & 0.87227 & 2.6 \\ \cline{1-1}  & G1 & 93.25\% & 108 & 76 & 14 & 23 & 0.76768 & 0.84444 & 0.80423 & 4.1 \\ \cline{1-1}  & G2 & 96.40\% & 261 & 184 & 42 & 34 & 0.84404 & 0.81416 & 0.82883 & 5.5 \\ \cline{1-1}  & Overall & 687 & 502 & 82 & 95 & 0.83508 & 0.86872 & 0.85084 & 14.9 \\ \hline \multirow{8}{*}{HAIS method} & U1 & 61.58\% & 132 & 86 & 19 & 27 & 0.76106 & 0.81905 & 0.78899 & 2.2 \\  & U2 & 69.09\% & 186 & 112 & 58 & 16 & 0.85700 & 0.65882 & 0.75168 & 2.6 \\ \cline{1-1}  & G1 & 93.25\% & 108 & 63 & 18 & 17 & 0.78750 & 0.77778 & 0.78261 & 4.1 \\ \cline{1-1}  & G2 & 96.40\% & 261 & 142 & 74 & 44 & 0.76344 & 0.65741 & 0.70647 & 4.8 \\ \cline{1-1}  & Overall & 687 & 403 & 169 & 104 & 0.79675 & 0.72826 & 0.75743 & 13.7 \\ \hline \multirow{8}{*}{The proposed method} & U1 & 61.58\% & 132 & 110 & 9 & 15 & 0.88000 & 0.92437 & 0.90164 & 2.1 \\ \cline{1-1}  & U2 & 69.09\% & 186 & 158 & 13 & 15 & 0.91329 & 0.92398 & 0.91860 & 2.5 \\ \cline{1-1}  & G1 & 93.25\% & 108 & 87 & 16 & 5 & 0.94565 & 0.87931 & 0.90667 & 4.0 \\ \cline{1-1}  & G2 & 96.40\% & 261 & 203 & 46 & 13 & 0.93981 & 0.81526 & 0.87312 & 4.9 \\ \cline{1-1}  & Overall & 687 & 558 & 84 & 48 & 0.91968 & 0.88573 & 0.90000 & 13.5 \\ \hline \hline \end{tabular}  
* The actual number of individual trees in the plot. 2 The number of correctly segmented individual trees. 3 The number of under-segmented individual trees. 4 The number of over-segmented individual trees. 5 The time consumption of the method.  
\end{table} 
Table 7: Comparison of individual tree segmentation accuracy of SHDR method, DK method, HAIS method, Improved DK method, and the proposed method in four plots. Figure 11: HAIS set cluster algorithm vs. ITS set cluster algorithm. DBSCAN with K-means (DK) is a bottom-up method.