## 2 Materials and Methods
### 3D Point Cloud Editing  
The measured 3D point cloud underwent post-processing using the software CloudCompare (2.12 alpha) after alignment. The initial step in post-processing was to clearly crop the specific target section while simultaneously removing outliers that could cause inaccuracies in volume or morphology. For this purpose, the Statistical Outlier Removal  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Coordinates**} & \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Coordinates**} & \multirow{2}{*}{**Z (m)**} \\  & **X (m)** & **Y (m)** & **Z (m)** & & **X (m)** & **Y (m)** \\ \hline GCP1 & 313,137.79 & 401,026.08 & 48.44 & GCP7 & 312,052.35 & 401,082.40 & 49.20 \\ GCP2 & 312,954.72 & 400,941.34 & 48.7 & GCP8 & 312,388.18 & 401,180.32 & 46.36 \\ GCP3 & 312,791.71 & 400,912.27 & 44.94 & GCP9 & 312,642.63 & 401,176.64 & 44.58 \\ GCP4 & 312,378.46 & 400,837.23 & 49.33 & GCP10 & 313,027.94 & 401,321.81 & 44.70 \\ GCP5 & 311,511.35 & 400,776.99 & 49.86 & GCP11 & 313,087.25 & 401,376.73 & 48.47 \\ GCP6 & 311,731.34 & 400,927.81 & 49.43 & & & \\ \hline \end{tabular}
\end{table}
Table 1: Ground control points for 3D point cloud modeling (Korea 2000/Central Belt 2010 (EGM 96 Geoid).  
Figure 3: Point cloud extracted from the drone: (**a**) raw data and the target range with red dash line (**b**) cropped image of the target section.  
(SOR) method was used to filter out outlier data points. The SOR method calculated the average distance between each point in the initial point cloud and its nearest neighbors, then considered points that deviated from a set standard deviation defined through a Gaussian distribution as outliers and removed them [42]. The SOR filter first calculates the average distance of each point to its neighbors through a k-nearest neighbor search function. If this distance is greater than the average distance derived from all points in the data set plus the standard deviation (\(\sigma\)) of the average distance, the point is considered an outlier [43]. However, the raw initial data of the collected point cloud may not be sufficiently cleaned of outliers with a single application of the SOR process; thus, repeated SOR operations may be necessary in some cases [42]. In Boothroyd [42], up to 20% of the points were removed upon the first application of SOR, and up to 15% on the second application. For the data in this study, about 27 million initial data points were reduced to about 9 million after three filtering processes. However, it is known that if applied more than three times, the edges of the target object begin to be removed, meaning that no real benefit is being offered. At the patch size in particular, the stem shape of the vegetation was not properly represented. The point cloud was organized through a single SOR process to remove outliers from the measured raw data used while using as many points as possible. Additionally, to compensate for the limitation of unnecessarily collecting too many points that did not significantly enhance the results but did waste processing time, a process was employed to remove similar data within a certain space. The data processed in this manner were used for the final volume calculation, comprising 97,361 points (Table 2).  
The point cloud measured through the UAV captured the coordinate information of the vegetation patch surface. The vegetation patch would have a hollow shape if it were represented by the directly measured data as the data inside the patch cannot be physically measured from the sky. To overcome this limitation, interpolation was performed from the highest z-coordinate based on each point's x-y coordinates to the lowest point (reference point). Details regarding the data used in the final analysis are shown in Table 2. The point dimensions in Table 2 refer to the edges of each axis. The sampling and editing process from the raw data did not significantly affect the edges in a way that was important for volume estimation.