## 5 Conclusions  
Based on established data, UAV photogrammetry was used to develop and align 3D point clouds and estimate the amount of riparian vegetation at the patch scale. Initially, a UAV was used to capture 37,042 images of the target cross-section, which were then used to generate a point cloud with precise location information. To ensure more precise location information of the 3D point cloud, 11 GCPs were operated. The point cloud was generated and aligned using Pix4D Mapper, confirming that there were no issues with image quality, data, camera optimization, matching quality, or location accuracy. Among the results generated through the post-processing process, an area that accurately represents the vegetation patch of the analysis target area was selected, and the amount of riparian vegetation was finally estimated using 97,361 points. The first method to estimate the volume of riparian vegetation used the DSM and DTM developed from the 3D point cloud. Specifically, the DSM was directly developed from the point cloud, and subsequently, the point cloud was classified into soil, road, vegetation, building, and artificially created objects to develop the DTM. To compare with the volume estimation results by alpha shape, the difference between DSM and DTM was assumed to be vegetation, and the validation of height of vegetation (DSM-DTM) with the reference and field survey was reasonable. Using this result, the vegetation volume (approximately 242,000 m\({}^{3}\)) was calculated by multiplying the average difference value by the area of the target section. The volume estimation results by alpha radius were compared with methods using volume estimation by alpha shape. The analysis results showed that a small radius cannot properly represent the intended object or produces unrealistically low volumes (about 7610 m\({}^{3}\) at radius = 0.6, error 97%). In contrast, a large radius overestimates the  
Figure 12: Volume implementation result images when the alpha radius is 0.75: (a) 1 st SOR, (b) 2 nd SOR, and (c) 3 rd SOR. object's shape (about 377,582 m\({}^{3}\) at radius = 10, error 56%), but it has been observed that the estimated volume does not increase infinitely as the radius increases. When compared to the orthoimages generated from actual drone images, the most meaningful radius was found to be 0.75. It was confirmed that a thorough review of the radius value is necessary when estimating the amount of vegetation using the alpha form. Additionally, while outlier removal and handling of large amounts of meaningless data were essential, changes in volume owing to the actual alpha radius values had less of an impact when using point clouds to estimate the volume of an object. However, even as the radius value increases and the volume does not continuously increase, it was observed that the volume decreases as noise processing increases. This result indicates the importance of ensuring that essential points are not removed during data processing. The research results of this study are expected to serve as basic data for managing riparian vegetation that excessively adheres to rivers. If the exact value of the target vegetation patch is known, deriving an accurate value for the radius will be essential. However, we recognized the limitations of not knowing the actual value of natural vegetation patches and began research to find realistic alternatives. In reality, calculating the amount of vegetation planted in a space of approximately 70,000 m\({}^{2}\) is only possible by cutting down all the vegetation and measuring it on a laboratory scale. We recognized and accepted these limitations in the field and tried to present approximate values that could be realistically used. Therefore, if someone needs to estimate the approximate volume of large vegetation patches like this (for dense and lush vegetation patches, like Figure 6c-e), it is recommended to use 0.75.