Low-Cost Automatic Slope Monitoring Using Vector Tracking Analyses on Live-Streamed Time-Lapse Imagery
Low-Cost Automatic Slope Monitoring Using Vector Tracking Analyses on Live-Streamed Time-Lapse Imagery
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DOI：
10.3390/rs13050893
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发表时间：
2021-02
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通讯作者：
Muhammad Waqas Khan;S. Dunning;Rupert Bainbridge;James Martin;A. Diaz-Moreno;H. Torun;Nanlin Jin;J. Woodward;M. Lim
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作者：
Muhammad Waqas Khan;S. Dunning;Rupert Bainbridge;James Martin;A. Diaz-Moreno;H. Torun;Nanlin Jin;J. Woodward;M. Lim
确定能够及时预报滑坡从而减少风险的前兆事件本身就很困难。在这里，我们提出了一种新颖的、低成本的、使用延时成像（TLI）的流动可视化技术，可以对斜坡运动进行真实的时间分析。这种方法适用于苏格兰阿盖尔的Rest and Be Thankful斜坡，过去的泥石流堵塞了A83公路或迫使先发制人地关闭。休息和休息的TLI是从一个固定的站，28毫米透镜，时间间隔相机每15分钟。图像过滤，以对抗风引起的相机振动，不对称的照明和雾的不对准的影响。粒子图像测速（PIV）算法，然后运行产生斜坡运动速度矢量。PIV生成的矢量会自动进行后处理，以将斜坡移动生成的矢量与恶劣环境条件生成的误报分离开来。超过20天的图像的结果表明，前体斜坡运动开始的降雨事件，一段时间的静止10天，随后由一个大的滑坡失败，在进行降雨超过3000吨的沉积物到达道路。结果表明，低成本，实时流TLI和这种新的PIV方法正确检测，重要的是，报告前兆边坡运动，允许早期预警，有效的管理和滑坡影响缓解。这项技术的未来应用将为A83的资产管理开发一个有效的决策工具，降低驾驶者的生命风险。该技术也可应用于其他关键基础设施场地，从而减少灾害风险。
Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. This approach is applied to the Rest and Be Thankful slope, Argyle, Scotland, where past debris flows have blocked the A83 or forced preemptive closure. TLI of the Rest and Be Thankful are taken from a fixed station, 28 mm lens, time lapse camera every 15 min. Imagery is filtered to counter the effects of misalignment from wind induced vibration of the camera, asymmetric lighting, and fog. Particle image velocimetry (PIV) algorithms are then run to produce slope movement velocity vectors. PIV generated vectors are automatically post-processed to separate vectors generated by slope movement from false positives generated by harsh environmental conditions. Results for images over a 20-day period indicated precursor slope movement initiated by a rainfall event, a period of quiescence for 10 days, followed by a large landslide failure during proceeding rainfall where over 3000 tons of sediment reached the road. Results suggest low cost, live streamed TLI and this novel PIV approach correctly detect and, importantly, report precursor slope movement, allowing early warning, effective management and landslide impact mitigation. Future applications of this technique will allow the development of an effective decision-making tool for asset management of the A83, reducing the risk to life of motorists. The technique can also be applied to other critical infrastructure sites, allowing hazard risk reduction.