Landslide susceptibility assessment in the alpine and canyon areas on the basis of ascending and descending InSAR data
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摘要:
近年来，反映地表变形因子的合成孔径雷达干涉测量InSAR（interferometric synthetic aperture radar）数据被逐渐引入到滑坡易发性评价中。然而这些研究未考虑SAR影像差异，特别是在高山峡谷区InSAR升、降轨成像效果差别大，对地表变形的反映存在较大误差。为了在滑坡易发性评价中更加准确地使用InSAR数据，选择象鼻岭水电站库区作为研究区，经过指标因子相关性分析后，选择了和高山峡谷区滑坡发生相关的11个孕灾因子与升、降轨InSAR变形数据组合进行滑坡易发性评价。比较是否使用变形数据和使用不同变形数据之间的结果发现，在易发性评价中补充采样点较稀疏的升轨数据反而会降低易发性评价精度，补充采样点较多的降轨数据能一定程度上提高2.7%的易发性精度（
AUC $= $0.9248 ）。研究表明，InSAR变形数据作为因子引入滑坡易发性评价中会影响评价结果，在高山峡谷区选用合适的InSAR变形数据可提高易发性评价精度。Abstract:Objective In recent years, Interferometric Synthetic Aperture Radar (InSAR) data, which reflect surface deformation, have increasingly been integrated into landslide susceptibility assessments. However, previous studies have not adequately addressed the variability in SAR images, particularly in alpine and canyon regions where the imaging characteristics of InSAR ascending and descending passes differ significantly, leading to substantial errors in surface deformation measurements.
Methods This study selected the reservoir area of Xiangbiling Hydropower Station as the research site. After conducting a correlation analysis of influencing factors, 11 influencing factors and InSAR deformation data pertinent to landslides in alpine and canyon areas were chosen for landslide susceptibility evaluation.
Results Comparisons between using different deformation datasets revealed that incorporating sparse ascending-pass data from sampling points decreases the accuracy of landslide susceptibility assessment. Conversely, utilizing descending-pass SAR data, which includes a higher density of sampling points, improved the accuracy by 2.7% (
AUC =0.9248 ).Conclusion The inclusion of InSAR deformation data as a influencing factor in landslide susceptibility assessment significantly influences the evaluation outcomes. Therefore, it is crucial to select appropriate InSAR deformation data to enhance the accuracy of susceptibility assessments.
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图 1 牛栏江流域位置示意图(a)和研究区遥感影像图(b)
Figure 1. Niulan watershed (a) and remote sensing image of the study area (b)
图 3 结合不同孕灾因子的滑坡易发性评价方法
Figure 3. Landslide susceptibility assessment methods of various factors
图 4 研究区地表沉降速率图
a. 升轨InSAR采样点数据；b. 升轨InSAR插值；c. 降轨InSAR采样点数据；d. 降轨InSAR插值
Figure 4. Ground subsidence rate map of the study area
图 5 不同成像模式下高山峡谷区滑坡易发性评价图
a. 孕灾因子；b. 孕灾因子+升轨InSAR；c. 孕灾因子+降轨InSAR；d. 孕灾因子+升轨+降轨InSAR
Figure 5. Landslide susceptibility assessment maps
图 6 滑坡易发性信息量差异
a. 易发性Ⅱ−Ⅰ；b. 易发性Ⅲ−Ⅰ；c. 易发性Ⅳ−Ⅰ
Figure 6. Differences of landslide susceptibility in the information value
表 1 Sentinel-1A卫星基本参数
Table 1. Sentinel-1A data basic parameters
主要参数 基本数据 波长/cm 5.6 波段 C波段 轨道方向 升/降轨 重访周期/d 12 入射角/(°) 39.31，39.24 成像方式 宽幅成像（IW） 极化方式 垂直单极化（VV） 
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表 2 滑坡信息量差异值占比
Table 2. Proportion of landslide information difference values
编号 信息量变化 滑坡面所占栅格数 栅格总数 有效增加指数/% 易发性Ⅱ−Ⅰ 增加 2251 59371 3.79 不变 5162 145843 减少 353 9985 易发性Ⅲ−Ⅰ 增加 475 7044 6.74 不变 5740 162564 减少 1551 45591 易发性Ⅳ−Ⅰ 增加 1766 28994 6.09 不变 5718 170218 减少 282 15987
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表 3 InSAR变形数据因子信息量值
Table 3. InSAR deformation data factor information values
数据类型 变形速率/(mm·a−1)分段 栅格数 滑坡栅格数 信息量 升轨 （−87.06，−26.47] 20702 473 −0.66 （−26.47，−9.37] 52930 1798 −0.09 （−9.37，4.88] 75328 2618 −0.05 （4.88，21.98] 52714 2132 0.16 （21.98，94.68] 13525 741 0.60 降轨 （−124.35，−22.91] 14895 1366 1.35 （−22.91，−4.83] 55591 2945 0.55 （−4.83，9.22] 76992 1896 −0.55 （9.22，27.30] 52884 1225 −0.63 （27.30，131.76] 14837 330 −0.69
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