Remote Sensing of River Velocity Using Drone Video and Optical Flow Algorithm

摘要

Watershed models of extreme flooding events require accurate and reliable measurements of streamflow for calibration and validation. However, flow rate measurements during floods are inherently uncertain, and physical measurements of velocity during flood conditions are prohibitive in many cases. Therefore, novel methods to measure stream velocity during extreme floods must be considered. This project addresses this challenge through the development of a novel system that utilizes drones, video imaging, and optical flow algorithms to measure velocity in rivers and streams. This system was applied at a case study location on the Menomonee River in Wauwatosa, WI. To remotely sense stream velocity, a DJI Matrice 210 RTK drone equipped with a Zenmuse X5S camera was used to capture video. The video data from the drone was analyzed using an optical flow algorithm, which generates a high-resolution velocity field by computing the optical displacement of pixels between each video frame. This data was compared against in situ measurements of velocity captured with a hand-held velocimeter. Results indicate that the optical flow algorithms estimate the magnitude of surface velocity to within 12% of hand-held measurements. If fully developed, this system could transform the way that streamflow is measured and modeled.