戴可人, 卓冠晨, 许强, 李振洪, 李为乐, 管威. 雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯[J]. 武汉大学学报 ( 信息科学版), 2019, 44(12): 1778-1786, 1796. DOI: 10.13203/j.whugis20190092
引用本文: 戴可人, 卓冠晨, 许强, 李振洪, 李为乐, 管威. 雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯[J]. 武汉大学学报 ( 信息科学版), 2019, 44(12): 1778-1786, 1796. DOI: 10.13203/j.whugis20190092
DAI Keren, ZHUO Guanchen, XU Qiang, LI Zhenhong, LI Weile, GUAN Wei. Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2019, 44(12): 1778-1786, 1796. DOI: 10.13203/j.whugis20190092
Citation: DAI Keren, ZHUO Guanchen, XU Qiang, LI Zhenhong, LI Weile, GUAN Wei. Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2019, 44(12): 1778-1786, 1796. DOI: 10.13203/j.whugis20190092

雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯

Tracing the Pre-failure Two-dimensional Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry

  • 摘要: 利用存档光学遥感影像对灾前演变情况进行分析是目前常用的方法,但往往受限于获取时间密度、云量等因素。随着雷达遥感卫星数据质量的不断提升,合成孔径雷达干涉测量(interferometric syntheticaperture radar,InSAR)技术可以为滑坡灾前形变探测提供新的技术途径。基于欧洲空间局哨兵一号(Sentinel-1)雷达卫星数据,同时结合升轨与降轨视线向形变结果提取沿坡向与垂直向二维形变,对2018年7月12日甘肃南峪乡滑坡灾前二维形变进行追溯分析。时序结果显示,该滑坡自2017年6月起便已经开始缓慢的变形,至滑坡发生前13个月时最大累积形变量达77 mm。结合降雨量数据对比分析,发现该滑坡灾前变形与降雨量变化高度吻合,说明降雨是该滑坡发生的主要诱因之一。该InSAR追溯结果展示了星载雷达干涉测量技术在滑坡探测方面的应用潜力,为滑坡诱因分析、防灾减灾乃至滑坡监测预警工作提供了新的思路与参考。

     

    Abstract: The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to analyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency's Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future.

     

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