Abstract
Unmanned Aerial Vehicles (UAVs) are now filling in the gaps between spaceborne and ground-based observations and enhancing the spatial resolution and temporal coverage of data acquisition. In the realm of hydrological observations, UAVs play a key role in quantitatively characterizing the surface flow, allowing for remotely accessing the water body of interest. In this paper, we propose a technology that uses a sensing platform encompassing a drone and a camera to determine the water level. The images acquired by means of the sensing platform are then analyzed using the Canny method to detect the edges of water level and of Ground Control Points (GCPs) used as reference points. The water level is then retrieved from images and compared to a benchmark value obtained by a traditional device. The method is tested at four locations in an artificial lake in central Italy. Results are encouraging, as the overall mean error between estimated and true water level values is around 0.05 m. This technology is well suited to improve hydraulic modeling and thus provides reliable support to flood mitigation strategies.
Highlights
In recent years, the awareness of the importance of spatial and temporal variation of open water level has increased [1]
The measurement procedure leads to determining the distance between each ground control points (GCPs) and the edge
The measurement procedure leads to determining the distance between each GCP and the edge of the water level
Summary
The awareness of the importance of spatial and temporal variation of open water level has increased [1]. Water level sensors are characterized by some drawbacks, such as, for instance, their pointwise measurements. Their accuracy can be affected by several issues, such as severe storm events and systematic errors associated with the sensors themselves [7]. Remote sensor-based methods such as the ENVISAT and ERS-2 satellite missions allowed the estimation of the water level of inland lakes with high precision (e.g., [9,10]). Spaceborne sensors are affected by limitations that restrict their ability to measure the temporal and spatial variation of the water level, such as fixed orbit configurations and coarse temporal resolution. There has been increasing interest in the development of image-based technologies for determining water level values (e.g., [12]). Griesbaum et al [13] developed a low-cost method for estimating flood elevation and inundation depth based on user-generated flood images
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