Ye Kai, Wang Zhe, Wang Ting, Luo Ying, Chen Yiming, Zhang Jiaqian, Cai Jialun
College of Environment and Resources, Southwest University of Science & Technology, Mianyang 621010, China.
School of Life Science and Engineering, Southwest University of Science & Technology, Mianyang 621010, China.
Sensors (Basel). 2024 Oct 21;24(20):6760. doi: 10.3390/s24206760.
Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide's downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide's trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 78 cm, 57 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide's downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides.
由于合成孔径雷达（SAR）卫星成像模式固有的局限性，利用时间序列干涉合成孔径雷达（InSAR）技术处理单轨道卫星图像数据通常仅能得到沿视线（LOS）方向的一维变形信息。这种限制阻碍了对滑坡真实地表变形的全面表征。因此，在本文中，对哨兵 - 1A卫星的30景升轨和30景降轨图像进行小基线集InSAR（SBAS - InSAR）和永久散射体InSAR（PS - InSAR）处理后，基于SAR卫星的成像几何关系，我们提出了一种融合升轨和降轨LOS方向时间序列变形的新计算方法，以提取滑坡的下坡方向变形。将该方法应用于白格滑坡监测，并与改进的切向角预警准则相结合，我们将滑坡后缘划分为高速、匀速和低速变形区域，变形量分别为7 - 8厘米、5 - 7厘米和3 - 4厘米。与滑坡变形监测实测数据的对比分析表明，融合后的滑坡下坡方向变形与实测数据之间的平均均方根误差仅为3.62毫米。与单升轨和单降轨LOS方向时间序列变形相比，平均均方根误差分别降低了56.9%和57.5%，表明监测精度更高。最后，基于对计算得到的时间序列变形结果所推导的滑坡变形及其诱发因素的分析，确定降水、地层岩性和正在进行的地质活动是白格滑坡滑动的重要因素。该方法为动态滑坡监测提供了更全面、准确的地表变形信息，有助于相关部门进行滑坡监测与管理，并为多轨道卫星LOS方向变形融合以准确重建滑坡真实地表变形提供技术建议。
