###### Abstract  
This study employs technology that has many different applications, including flood management, flood level control, and identification of vegetation type by patch size. Recent climate change, characterized by severe droughs and floods, intensifies riparian vegetation growth, demanding accurate environmental data. Traditional methods for analyzing vegetation in rivers involve on-site measurements or estimating the growth phase of the vegetation; however, these methods have limitations. Unmanned aerial vehicles (UAVs) and ground laser scanning, meanwhile, offer cost-effective, versatile solutions. This study uses UAVs to generate 3D riparian vegetation point clouds, employing the alpha shape technique. Performance was evaluated by analyzing the estimated volume results, considering the influence of the alpha radius. Results are most significant with an alpha radius of 0.75. This technology benefits river management by addressing vegetation volume, scale, flood control, and identification of vegetation type. U  
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willow mattresses. In their natural state, floodplains are hotspots of biodiversity; they are claimed to be home to more plant and animal species than most other landscapes [14; 15; 16; 17]. It is therefore necessary to analyze the trade-off between the hydraulic issues and ecological benefits of riparian vegetation and apply it to river design and management techniques. According to [PERSON] and [PERSON] [18], it is essential to understand both the landscapes where land conversion is occurring and the adjacent landscapes so that managers can investigate the causes of change and determine whether planning and intervention are needed or the changes are part of the overall functioning of the ecosystem. Consequently, there is a demand for a reliable estimation technique regarding the space occupied by vegetation from the flow area. Previous approaches for analyzing the space occupied by vegetation in rivers that were based on the flow involve measuring vegetation distribution on-site, selecting distributed vegetation types, and inferring the growth phase to determine typical morphologies. However, these estimation methods' ability to provide highly reliable estimates are inherently limited and are not free from temporal and spatial constraints [15; 16; 17; 19; 20; 21]. Moreover, vegetation in rivers typically do not exist as isolated species; in most cases, various types of vegetation join in complex patches. Therefore, to accurately estimate the spatial distribution of vegetation, it is essential to obtain 3D spatial information of the vegetation in patch forms [22; 23; 24]. As technology advances, unmanned aerial vehicles (UAVs) are being introduced that can make extensive observations of the river environment using precise 3D representation technologies, such as Terrestrial Laser Scanning (TLS) and Light Detection And Ranging (LiDAR), to overcome the limitations of on-site measurements. Consequently, there is a growing trend in domains such as rivers, environment, forestry, and agriculture to use LiDAR and UAVs for research that aims to precisely quantify the physical characteristics of vegetation [25; 26; 27; 28; 29; 30; 31; 32; 33]. However, TLS and LiDAR still have limitations, including high equipment cost, relatively short detection ranges, and sensitivity to weather conditions [34; 35]. UAVs are much more cost-effective than LiDAR. Using UAVs to estimate vegetation size allows analysis over a relatively large area and offers the advantage of remote access to hard-to-reach areas. For these reasons, many studies are attempting to use UAVs to obtain images and apply the structure from motion (SFM) algorithm for monitoring, quantifying, and optimizing vegetation height, canopy growth, forest changes, and harvest volume [36]. [PERSON] et al.