The composition of riverbed materials is an important environmental factor, which affects the water flow, riverbed variation, river habitat, and water quality. This paper presents a safe and highly mobile unmanned aerial
vehicle (UAV)-based image-processing system for determining the grain size in gravel-bed rivers. The image characteristics of a riverbed surface were photographed using a UAV, and automatic image-processing procedures, such as edge detection and watershed segmentation, were used to recognize the gravel’s outlines. Subsequently, the grain-size data were analyzed to obtain the grain-size distribution. This study proposed an improved watershed segmentation procedure to eliminate the problems of over- and under-segmentation in the image-processing procedures. The system was tested using indoor and outdoor experiments. The indoor experiments mainly checked the accuracy of gravel numbers and characteristic lengths identified by the system, whereas the outdoor experiments evaluated the grain-size distribution it produced. The results of the indoor experiments indicated that the improved watershed segmentation procedure correctly recognized the gravel number; furthermore, minimal mean errors in characteristic lengths occurred compared with the other methods used in this study on gravel with separated, closed, and overlapping arrangements.In the outdoor experiments, the surface grain-size distribution of the gravel bed was first analyzed through manual investigation, which included pebble counts, grid counts, and manual extraction of information from images. The results were compared with those obtained through edge detection, watershed segmentation, and improved watershed segmentation. The mean absolute percentage errors of the median grain size (D50) were 2.46–6.73%, 23.60–26.94%,and 0.94–5.28%, respectively. According to the indoor and outdoor experiment results, the improved watershed segmentation procedure could accurately recognize the outlines and correct gravel number and could acquire the grainsize distribution with results similar to manual investigation. The UAV-based image-processing system proposed in this paper could effectively and correctly survey grain-size distributions in gravel-bed rivers.
Keywords: Unmanned Aerial Vehicles, image-processing, grain-size distribution