Measuring River Surface Velocity Using UAS-Borne Doppler Radar

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Zhen Zhou, Laura Riis-Klinkvort, Emilie Ahrnkiel Jørgensen, Christine Lindenhoff, Monica Coppo Frías, Alexander Rietz Vesterhauge, Daniel Haugård Olesen, Makar Lavish, Alexey Dobrovolskiy, Alexey Kadek, Niksa Orlic, Tomislav Grubesa, Luka Drmić, Henrik Grosen, Sune Nielsen, Daniel Wennerberg, Viktor Fagerström, Jenny Axén, David Gustafsson, Peter Bauer-Gottwein
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Abstract

Using Unoccupied Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS-borne Doppler radar is particularly attractive because it is suitable for real-time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross-sections (XSs) were surveyed within a 10 km stretch of Rönne River in Sweden. Ground-truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at one m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity based on Gaussian models with: (a) one peak, and (b) two peaks. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone-induced propwash velocity can be neglected in fast and highly turbulent flows. To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river surface velocity.
利用无人机载多普勒雷达测量河面流速
利用配备光学 RGB 摄像机和多普勒雷达的无人机系统(UAS),可以高效地测量高空间分辨率的表面速度。无人机载多普勒雷达尤其具有吸引力,因为它适用于实时速度测定,测量是非接触式的,而且与图像测速技术相比限制较少。本文对瑞典 Rönne 河 10 公里河段内的五个断面 (XS) 进行了勘测。使用电磁速度传感器(OTT MF Pro)沿 XS 以一米的间距进行地面实况表面速度观测。使用粒子图像测速仪(PIV)和时空图像测速仪(STIV)技术分析了无人机 RGB 摄像机拍摄的视频。此外,我们还使用多普勒雷达记录了河上多个航点的全波形信号数据。一种算法对平均振幅曲线拟合出两种可选模型,从而根据具有以下特征的高斯模型得出正确的河面流速:(a) 一个峰值;(b) 一个峰值:(a) 一个峰值和 (b) 两个峰值。结果表明,在流速较低的 XS,可以发现无人机引起的河面流速和螺旋桨冲刷速度,而在流速快、湍流大的情况下,无人机引起的螺旋桨冲刷速度可以忽略不计。为了验证多普勒雷达得出的河流流速,计算了每个航点多普勒雷达覆盖范围内的平均 PIV 值。最后,将 OTT MF Pro 数据与 STIV、平均 PIV 和多普勒雷达进行定量比较后发现,无人机载多普勒雷达可以可靠地测量河面流速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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