## 3 Results
### Results of Volume Estimation by the Alpha Shape Method  
The volume estimation by the alpha shape method for different radii (\(\alpha\)) is shown in Figures 8 and 9. Figure 8 represents the top view and cross-sectional results of volume estimation by radius 0.7 and 10. The volume was estimated considering that the target object was unstructured. It was determined whether the appropriate radius was 1 or 10, regardless of the object's size. Objects with more complex edges require smaller radii, while less complex edges require larger radii. If the vegetation patch has been a large cuboid, it is not necessary to be overly concerned with the alpha radius. Nevertheless, as we know, finding the appropriate alpha radius when edges are jumbled makes sense. This study finds appropriate values for an unlimited alpha radius range and estimates the most optimized volume. Due to the range covered, which changed significantly depending on the radius \(\alpha\) used in the alpha shape method (especially for larger subjects like the one in this study),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cell Size** & **Mean Elevation of DSM-DTM (m)** & **Standard Deviation of DSM-DTM (m)** & **Area of Study Site** & **Volume (m\({}^{2}\))** & **(m\({}^{3}\))** \\ \hline
0.56 & 3.567 & 1.78 & 66,380.4 & 236.779 \\
1.2 & 3.545 & 1.77 & 68,545.9 & 242,995 \\
2.4 & 3.542 & 1.75 & 70,011.8 & 247,981 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Area of the research site and the volume of vegetation calculated in the section, extracted by resolution.  
Figure 7: Boundaries of the research area extracted by resolution (a) 2.4 m, (b) 1.2 m, (c) 0.56 m, and (d) areas excluded from the research area due to a lack of point cloud.  
the volume at \(\alpha=0.7\) and \(\alpha=10\) was 113,035 and 377,582 m\({}^{3}\), respectively, a difference of approximately 260,000 m\({}^{3}\).  
Figure 8: Volume estimation images by alpha radius (**a**) top view with radius 0.7, (**b**) top view with radius 10, (**c**) cross-sectional image with radius 0.7 at the red dashed line in (**a**), and (**d**) cross-sectional image with radius 10 at the red dashed line in (**b**).  
Figure 9: Results of volume estimation by alpha radius (\(\alpha\)).  
will not encompass the entire target object, preventing an accurate representation of the actual volume. Conversely, as shown in Figure 8d, a too-large radius results in volume estimation even for areas that are actually empty. Using an \(\alpha\) value that is too large would overestimate and inaccurately represent the object's shape. These results highlight the importance of setting the alpha radius. Comparing the results of the volume estimation by alpha radius (Figure 9), when the radius was set to 0.8, the volume of the vegetation patch was 246,413 m3, which is the most similar value to the patch volume based on DSM and DTM.  
The vegetation patch volume estimation based on DSM and DTM was a result that did not consider the voids inside the vegetation. Therefore, it can be presumed to be overestimated compared to the actual volume. Hence, the volume estimation result using Radius 0.8 in the alpha shape method was also expected to be overestimated. Considering this estimation and the radius range that was too small to be properly implemented, the optimal alpha radius for this research subject could be recommended as 0.75, and the 3D image of the implemented vegetation patch using this is as shown in Figure 10. By comparing the implemented vegetation patch and the orthoinage generated from the drone capture results of the area, it was confirmed that areas without actual vegetation were reproduced, and the amount of vegetation excluding these areas was estimated.  
Figure 10: (**a**) 3D vegetation patch implementation result using alpha radius (\(\alpha\) = 0.75) and (**b**) orthoinage of the respective section.