## 3 Experiment and Result
### Location and Segmentation of Trees  
Using template operations, the initial extraction of tree vertices can be achieved. The extraction accuracy is affected by the grid size \(d\) and the elevation threshold \(H_{T}\). To extract all trees in the survey area, both \(d\) and \(H_{T}\) should not be more than 3 m. In the experiment, we increase the value of \(d\) from 1 m to 3 m in steps of 0.5 m and set the value of the height threshold \(H_{T}\) according to the tree species composition in the survey area. To obtain the optimal algorithm parameters for the tree location extraction, ten forest plots are used for the threshold value training and testing. The trees are mainly six species, including Pine, Birch, Cedar, Tsubaki, Shrub and others. The details of the training plots are listed in Table 2.  
Through the grid size \(d\) and the elevation threshold \(H_{T}\), the rough tree position can be obtained. Using the rotated profile with an angle step of \(\theta\), the tree crown segmentation can be generated while the tree position is optimized. The sample plot is 30 m in diameter, there are a total of 781 sample trees in ten plots, and all the details are shown in Table 3. The acquisition of the initial tree locations is the basis for the subsequent tree vertex position acquisition and the final tree segmentation. Therefore, to obtain a more accurate initial tree point position, one first needs to set more appropriate parameters \(d\) and \(H_{T}\) as Figure 16 shows. Different settings of \(d\) and \(H_{T}\) lead to different results of the rough tree locations. If the \(d\) and \(H_{T}\) are small, the detected rough tree number will be large and will require a lot \begin{table}
\begin{tabular}{c c c} \hline
**Tree Species** & **Number** & **Plot ID** \\ \hline Pine & 233 & Plot 1–Plot 10 \\ Birch & 109 & Plot 2, Plot 5 \\ Cedar & 113 & Plot 3, Plot 4, Plot 7 \\ Tsubaki & 67 & Plot 2, Plot 7, Plot 9, Plot 10 \\ Shrub & 148 & Plot 3, Plot 6, Plot 8, Plot 9 \\ Others & 111 & Plot 1–Plot 10 \\ Total & 781 & Plot 1–Plot 10 \\ \hline \end{tabular}
\end{table} 
Table 2: Tree species information for ten plots. of iterations to get the segmented tree crowns and finally the tree locations. Conversely, if the values of \(d\) and \(H_{T}\) are set large, many small trees will be missed. Figure 16: The rough tree number and calculated tree number in each plot with different values of parameters \(d\) and \(H_{T}\). The blue curve represents rough tree numbers of each plot, the green dashed line represents the final calculated tree number obtained from the RPAA and the orange curve represents the true tree numbers measured on the sample plots. (**a**) The segmented tree number when \(d=1\) m and \(H_{T}=1\) m.(**b**) The segmented tree number when \(d=2\) m and \(H_{T}=1\) m. (**c**) The segmented tree number when \(d=2\) m and \(H_{T}=1\) m. (**d**) The segmented tree number when \(d=2.5\) m and \(H_{T}=1\) m. (**e**) The segmented tree number when \(d=1\) m and \(H_{T}=1.5\) m. (**f**) The segmented tree number when \(d=1\) m and \(H_{T}=2.0\) m. (**g**) The segmented tree number when \(d=1\) m and \(H_{T}=2.5\) m.  
It can be seen in Figure 16 that the blue curve represents rough tree numbers for each plot, the green dashed line represents the final calculated tree number obtained from the RPAA, and the orange curve represents the true tree numbers measured in the sample plots. The higher the coincidence degree of the green dashed line and the orange curve, the more reasonable the parameter \(d\) and \(H_{T}\) settings are considered. Figure 16b has ideal results when \(d=1.5\) m and \(H_{T}=1\) m. The result is more sensitive to the parameter \(H_{T}\) and the parameter setting \(d=1.5\) m; \(H_{T}=1\) m is more reasonable. When \(d=1.5\) and \(H_{T}=1\), the green row is the ideal extraction result.