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Dynamic assessment of landslide susceptibility considering the InSAR deformation influenced by Typhoon Megi: A case study of Songyang Country in Zhejiang Province
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摘要:
滑坡易发性评价在预测滑坡发生和潜在风险方面至关重要，但静态易发性制图因忽略了滑坡动态演化特征而导致预测结果的可靠性受限。以2016年台风“鲇鱼”影响下的浙江省松阳县为研究区域，通过引入Sentinel-1A的SAR地表形变数据，开展滑坡动态易发性评价。首先采用D-InSAR技术获取台风前后的地表形变量，以−20 mm/a的形变速率为阈值确定新增滑坡；然后利用SBAS-InSAR技术获得了2015年11月22日−2017年3月4日的研究区地表形变量；最后选取地形、地质、水文和人类工程活动等9个静态评价因子以及垂直向和LOS方向的InSAR地表形变量2个动态评价因子，构建MIV-BP神经网络模型生成滑坡动态易发性图。结果表明：（1）InSAR地表形变动态因子可显著提升滑坡易发性的整体预测精度，当缺失该类因子时，预测精度由0.901下降至0.857；此外，模型对台风“鲇鱼”诱发滑坡的识别具有良好的效果。（2）研究区内滑坡极低和低易发区基本不受台风“鲇鱼”的影响，但地形陡峭或地势较高的区域则由台风前的中高易发区升级为极高易发区，且极高易发区域的变化与InSAR地表形变的发展具有高度的一致性。研究结果可为今后类似极端天气下松阳县的地质灾害防灾减灾提供有价值的参考。
Abstract:ObjectiveLandslide susceptibility assessment (LSA) plays a vital role in predicting landslide occurrence and potential risks. However, static susceptibility maps are limited in reliability their failure to account for dynamic evolution of landslides. This study aims to enhance landslide susceptibility assessment by incorporating dynamic factors through Sentinel-1A SAR data, which is derived from Songyang County, Zhejiang Province: A resion impacted by Typhoon Megi in 2016.
MethodsThe methodology involved three key steps. First, D-InSAR technology was used to measure ground deformation before and after Typhoon Megi. A deformation rate of −20 mm/a was set as the threshold to augment the landslide inventory. Second, SBAS-InSAR technology was applied to obtain ground deformation data from November 22, 2015, to March 4, 2017. Third, nine static factors, including terrain, geology, hydrology, and human activities, were combined with vertical and line-of-sight (LOS) of InSAR ground deformation to produce a dynamic landslide susceptibility map using the MIV-BP neural network framework.
ResultsThe inclusion of dynamic factors, particularly InSAR ground deformation, significantly improved the accuracy of LSA. When these factors were excluded, prediction accuracy decreased from 0.901 to 0.857. Areas with extremely low and low susceptibility were largely unaffected by Typhoon Megi, while regions with steep or elevated terrain experienced an increase in susceptibility, shifting from medium-high to extremely high susceptibility after the typhoon. The changes in the extremely high susceptibility areas closely aligned with the observed ground deformation from InSAR data.
ConclusionThe findings offer valuable insights for geological disaster prevention and mitigation in Songyang County and other regions facing similar extreme weather events in the future.
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图 1 研究区概况及历史滑坡点分布
Figure 1. Overview and distribution of historical landslides in the study area
图 3 松阳县LOS方向形变速率（2016年8月12日−10月11日）
Figure 3. Deformation rate of Songyang Country in LOS direction
图 4 基于InSAR技术增强后的滑坡数据库
Figure 4. Enhanced landslide database based on InSAR technology
图 5 基于D-InSAR技术解译的新增滑坡案例
Figure 5. New landslide cases interpreted based on D-InSAR technology
图 6 基于MIV-BP神经网络模型的松阳县滑坡易发性动态评价流程
Figure 6. Flowchart of dynamic assessment of landslide susceptibility of Songyang Country based on MIV-BP neural network model
图 7 基于SBAS-InSAR技术的松阳县地表累计垂直向形变（2015年12月16日−2017年3月4日）
Figure 7. Accumulated vertical surface deformation of Songyang County based on SBAS-InSAR technology
图 8 滑坡易发性评价静态因子空间分布
Figure 8. Spatial distribution of static factors in landslide susceptibility assessment
图 9 滑坡空间分布与静态评价因子的相关性
CA. 各评价因子不同分级中的面积；LN. 滑坡分级数量；LA. 滑坡分级面积
Figure 9. Relationship between landslide distribution and static factors
图 10 考虑时序InSAR形变的松阳县滑坡易发性制图
Figure 10. Landslide susceptibility mapping of Songyang Country considering time series deformation monitored by InSAR
图 12 考虑InSAR形变因子的滑坡动态易发性评价验证（玉岩镇滑坡案例）
Figure 12. Verification of dynamic assessment of landslide susceptibility considering InSAR deformation factors (case in Yuyan Town)
图 13 考虑InSAR形变因子的滑坡动态易发性评价验证（安民乡滑坡案例）
Figure 13. Verification of dynamic assessment of landslide susceptibility considering InSAR deformation factors (case in Anmin Town)
表 1 MIV-BP神经网络模型中评价因子的重要性排序
Table 1. Importance ranking of assessment factors in MIV-BP neural network model
评价因子 AUC 缺少因子类别 全 0.901 无 缺1个 0.894 垂直向InSAR形变 缺2个 0.857 垂直向及LOS方向InSAR形变 缺3个 0.861 垂直向及LOS方向InSAR形变、植被覆盖率（NDVI） 缺3个 0.816 垂直向及LOS方向InSAR形变、年代地层 缺3个 0.806 垂直向及LOS方向InSAR形变、距河流距离 缺3个 0.798 垂直向及LOS方向InSAR形变、坡度 缺3个 0.787 垂直向及LOS方向InSAR形变、高程 缺3个 0.780 垂直向及LOS方向InSAR形变、距道路距离 注：AUC. 受试者工作特征曲线下的面积，表示模型预测精度 
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