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首页> 外文期刊>Journal of Hydrology >Integrating unmanned aerial systems and LSPIV for rapid, cost-effective stream gauging
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Integrating unmanned aerial systems and LSPIV for rapid, cost-effective stream gauging

机译:整合无人机系统和LSPIV的快速,经济高效的流量测量

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Quantifying flow in rivers is fundamental to assessments of water supply, water quality, ecological conditions, hydrological responses to storm events, and geomorphological processes. Image-based surface velocity measurements have shown promise in extending the range of discharge conditions that can be measured in the field. The use of Unmanned Aerial Systems (UAS) in image-based measurements of surface velocities has the potential to expand applications of this method. Thus far, few investigations have assessed this potential by evaluating the accuracy and repeatability of discharge measurements using surface velocities obtained from UAS. This study uses large-scale particle image velocimetry (LSPIV) derived from videos captured by cameras on a UAS and a fixed tripod to obtain discharge measurements at ten different stream locations in Illinois, USA. Discharge values are compared to reference values measured by an acoustic Doppler current profiler, a propeller meter, and established stream gauges. The results demonstrate the effects of UAS flight height, camera steadiness and leveling accuracy, video sampling frequency, and LSPIV interrogation area size on surface velocities, and show that the mean difference between fixed and UAS cameras is less than 10%. Differences between LSPIV-derived and reference discharge values are generally less than 20%, not systematically low or high, and not related to site parameters like channel width or depth, indicating that results are relatively insensitive to camera setup and image processing parameters typically required of LSPIV. The results also show that standard velocity indices (between 0.85 and 0.9) recommended for converting surface velocities to depth-averaged velocities yield reasonable discharge estimates, but are best calibrated at specific sites. The study recommends a basic methodology for LSPIV discharge measurements using UAS that is rapid, cost-efficient, and does not require major preparatory work at a measurem
机译:河流中的量化是对供水,水质,生态条件,对风暴事件的水文反应以及地貌过程进行评估的基础。基于图像的表面速度测量显示在延长可以在现场测量的放电条件范围内的承诺。使用无人机的空中系统(UAS)在表面速度的基于图像的测量中有可能扩大该方法的应用。到目前为止,很少的调查通过评估使用从UA所获得的表面速度评估放电测量的准确性和可重复性来评估这种可能性。本研究使用由UAS和固定三脚架上由摄像机捕获的视频捕获的大规模粒子图像速度(LSPIV),以获得USIN的十个不同流位置的放电测量。将放电值与由声学多普勒电流分析器,螺旋桨计和建立的流仪测量的参考值进行比较。结果展示了UAS飞行高度,相机稳定性和平整精度,视频采样频率和LSPIV询问区域大小对表面速度的影响,并表明固定和UAS相机之间的平均差异小于10%。 LPPIV导出和参考放电值之间的差异通常小于20%,而不是系统地低或高,与信道宽度或深度等站点参数无关,指示结果对相机设置和图像处理参数相对不敏感,通常需要lspiv。结果还表明,标准速度指数(0.85和0.9之间)建议将表面速度转换为深度平均速度,收益率合理的放电估计,但在特定位点最佳校准。该研究建议使用uas的LPPIV放电测量方法,这些方法是快速,成本效益,并且在测量中不需要主要预备工作

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