Landslide susceptibility assessment in Shimian County based on time-series InSAR deformation
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摘要:
滑坡作为一种对自然和社会环境造成极大破坏的地质灾害，其易发性评价对防灾减灾至关重要。现有的滑坡数据库常作为滑坡易发性评价的数据基础，由于更新不及时，存在时效性差和不全面等问题。此外，传统的滑坡易发性评价方法主要依赖于静态数据（如地形、地质、水文），缺乏动态数据（如地表形变），难以全面捕捉正在变形的滑坡特征，导致评价的可靠性较差。结合光学遥感技术和合成孔径雷达干涉测量技术（interferometric synthetic aperture radar，简称InSAR）识别研究区滑坡并获取地表形变作为动态评价因子，结合静态评价因子，采用联合训练和加权叠加两种方法，耦合最大熵模型（maximum entropy，简称MaxEnt），并使用迭代自组织（Iterative Self-Organizing，简称ISO）聚类算法对石棉县进行了滑坡易发性评价及分区。结果表明：（1）综合光学遥感技术和InSAR技术两种方法，在研究区共识别出139处滑坡，石棉县滑坡灾害高易发区主要分布于河流和道路两侧，滑坡灾害点的分布与所划分区域有很好的吻合性。（2）补充InSAR形变因子在一定程度上提高了6.1%的易发性精度（
AUC =0.921），同时显著地降低了评估结果中出现假阳性和假阴性的情况，提高了模型的精确性。该研究突出了将InSAR形变信息融入滑坡易发性模型中的优势，可为石棉县滑坡灾害预防提供重要支撑。Abstract:Objective Landslides are geological disasters that cause significant damage to both natural and social environment. Effective landslide susceptibility assessment is crucial for disaster prevention and mitigation. Existing landslide databases are often used as the primary data source for susceptibility assessments. However, due to delays in updates, these databases suffer from issues such as poor timeliness and incompleteness. Moreover, traditional landslide susceptibility assessment methods primarily rely on static data (e.g., topography, geology, and hydrology) and lack dynamic data (e.g., surface deformation), making it difficult to fully characterize the deforming landslides and reducing assessment reliability.
Methods This study combined optical remote sensing technology and synthetic aperture radar interferometry (Interferometric Synthetic Aperture Radar, InSAR) to identify landslides in the study area and obtain surface deformation as a dynamic evaluation factor. In combination with static evaluation factors, two methods—joint training and weighted superposition—were employed, alongside the Maximum Entropy (MaxEnt) model and the Iterative Self-Organizing (ISO) clustering algorithm to assess and categorize landslide susceptibility in Shimian County.
Results The findings are as follows: (1) By integrating optical remote sensing and InSAR technologies, 139 landslides are identified in the study area. High-risk landslide zones in Shimian County are predominantly located along riverbanks and roadsides. The distribution of landslide disaster points aligns well with the zoned areas. (2) Incorporating the InSAR deformation factor enhances the susceptibility accuracy by 6.1% (AUC=0.921) and substantially reduces the occurrence of false positives and false negatives, thereby improving overall model accuracy.
Conclusion This study demonstrates the advantages of incorporating InSAR deformation data into landslide susceptibility models, offering valuable support for landslide disaster prevention in Shimian County.
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图 1 研究区位置及概况
a. 研究区内Sentinel-1A卫星图像覆盖范围；b. 研究区地形地貌
Figure 1. Location and overview of the study area
图 4 滑坡识别结果
a. 基于光学遥感滑坡识别结果；b. 基于升轨数据滑坡识别结果；c. 基于降轨数据滑坡识别结果；d，e，f. 典型滑坡
Figure 4. Landslide identification results
图 10 易发性分区结果验证
a，e. 分别为HPO1，HPO2滑坡形变速率；b，f. MaxEnt模型结果；c，g. IJ-MaxEnt模型结果；d，h. IW-MaxEnt模型结果
Figure 10. Verification of susceptibility zoning results
表 1 Sentinel-1A数据基本参数
Table 1. Basic parameters of Sentinel-1A data
主要参数 基本数据 波长 5.6 cm 波段 C波段 轨道方向 升/降轨 重访周期 12 d 入射角 39.80/39.42 成像模式 IW 极化方式 VV 
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表 2 影响因子数据源
Table 2. Impact factor data source
数据名称 类型 分辨率/比例尺 来源 DEM 栅格 30 m https://www.gscloud.cn 岩性 矢量 1∶25万 http://www.ngac.org.cn 断层 降雨量 栅格 30 m https://www.resdc.cn 水系 矢量 1∶25万 https://www.webmap.cn 道路 NDVI 栅格 30 m http://www.nesdc.org.cn SAR影像 栅格 5 m×20 m https://search.asf.alaska.edu
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表 3 三种模型滑坡灾害点分布对比表
Table 3. Comparison of landslide hazard point distribution in three models
模型 易发性等级 滑坡数
量/个滑坡比
例/%面积比
例/%单位面积内已有滑坡点
个数(个·10−1 km−2)MaxEnt 极低易发性 8 5.76 49.30 0.1 低易发性 11 7.91 26.05 0.2 中易发性 9 6.47 10.51 0.3 高易发性 26 18.71 6.58 1.5 极高易发性 85 61.15 7.56 4.2 IJMaxEnt 极低易发性 11 7.91 72.62 0.1 低易发性 18 12.95 13.19 0.5 中易发性 15 10.79 5.12 1.1 高易发性 22 15.83 3.59 2.3 极高易发性 73 52.52 5.48 5.0 IWMaxEnt 极低易发性 6 4.32 40.67 0.0 低易发性 8 5.76 30.42 0.1 中易发性 16 11.51 15.99 0.4 高易发性 38 27.33 7.55 1.9 极高易发性 71 51.08 5.37 4.9
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