利用GACOS辅助下InSAR Stacking对金沙江流域进行滑坡监测

Landslide Detection of the Jinsha River Region Using GACOS Assisted InSAR Stacking

    摘要
  • 摘要: 西藏自治区贡觉县雄松乡至沙东乡金沙江流域作为川藏铁路的必经河流，地形崎岖、地质灾害隐患点多，亟需对该地区隐患点进行全方位的识别。首先，选取61景哨兵一号（Sentinel-1）升轨影像、53景Sentinel-1降轨影像和7景陆地观测技术卫星2号（advanced land observing satellite 2，ALOS-2）升轨影像对研究区域进行滑坡探测与监测。然后，利用合成孔径雷达（interferometric synthetic aperture radar，InSAR）通用型大气改正在线服务（generic atmospheric correction online service for InSAR，GACOS）辅助干涉影像堆叠技术（InSAR Stacking）的方法，获取研究区域雷达视线（line of sight, LOS）方向的InSAR年形变平均速率图，并结合3个轨道的结果提取出沿坡向和垂直滑坡向的平均速率图。最后，与LiCSBAS时间序列分析包的结果进行比较，发现两者具有高度一致性，LOS向年形变速率图像的相关系数在0.92以上，沿坡向和沿垂直滑坡向年形变速率的相关系数在0.85以上，证明了GACOS辅助下InSAR Stacking结果的可靠性。此外，还发现研究区域内沿坡向最大年形变速率为-163 mm/a；结合InSAR形变结果与光学遥感影像解译，可将该滑坡群分为A~G 7个区域进行实时监测。

     

    Abstract:
      Objectives  The Sichuan-Tibet Railway crosses the Jinsha River specifically the section from Xiongsong Town to Shadong Town, Gongjue County, Tibet Autonomous Region, in which exist strong topography variations and many potential geohazards, and hence it urgent to detect the potential geohazards in this region to ensure the railway safety.
      Methods  In this paper, 61 Sentinel-1 ascending, 53 Sentinel-1 descending and 7 advanced land observing satellite 2 (ALOS-2) ascending images are used to derive the annual mean deformation rates in the satellite radar line of sight (LOS) with two advanced InSAR(interferometric synthetic aperture radar) approaches, namely generic atmospheric correction online service for InSAR(GACOS)assisted InSAR Stacking and LiCSBAS. The three LOS annual mean deformation rate maps are then used to determine 2D surface movements (one along the slope and the other perpendicular to the slope) in the study region.
      Results  The comparison between GACOS assisted InSAR Stacking and LiCSBAS results show that they agree with each other with correlation coefficients over 0.92 for the LOS deformation rates and over 0.85 for the 2D deformation rates, suggesting the reliability of GACOS assisted InSAR Stacking. The maximum annual deformation rate of 163 mm/a can be observed in the slope direction and seven landslides (A, B, C, D, E, F, G) can be clearly identified, which in turn lays the foundation for future real-time monitoring. Based on the detailed analysis of the seven regions by using InSAR and optical interpretation results, it is found that the seven landslides had relatively obvious deformation. Landslides A, B, C and D are active landslides, which might cause the result of river blocking due to overall instability. At present, landslides E, F and G are still in the stage of slow deformation.
      Conclusions  This study also find that GACOS assisted InSAR Stacking can effectively remove long-band and topographic related atmospheric delay error with the help of GACOS, which has the advantages of being simple, effective, fast, and easy to popularize and apply. GACOS assisted InSAR Stacking technology can be used to quickly identify potential landslide hazards.

     

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