加权信息量支持下融合InSAR形变特征的滑坡易发性评价  

Landslide Susceptibility Assessment by Fusing InSAR Deformation Features under the Support of Weighted Information Volume

作　　者：肖海平[1] 万俊辉 陈兰兰 范永超 陈磊[1] XIAO Haiping;WAN Junhui;CHEN Lanlan;FAN Yongchao;CHEN Lei(School of Civil and Surveying and Mapping Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China;School of Resources and Civil Engineering,Gannan University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学土木与测绘工程学院,江西省赣州市341000 [2]赣南科技学院资源与土木工程学院,江西省赣州市341000

出　　处：《大地测量与地球动力学》2024年第7期718-724,共7页Journal of Geodesy and Geodynamics

基　　金：国家自然科学基金(42361012);江西省自然科学基金(20212BAB204030);江西省教育厅科学技术研究项目(GJJ2203602)。

摘　　要：使用SBAS-InSAR处理六盘水市水城区2018-07~2019-08共69景Sentinel-1A卫星影像,获取地表形变作为动态评价因子,用于完善传统滑坡易发性评价研究缺乏动态特征数据应用的问题。结果表明,使用10种静态评价因子融合InSAR形变特征数据作为动态评价因子,在耦合层次分析法与信息量法的加权信息量模型下对比仅使用静态特征数据,ROC曲线下面积分别为0.756 02和0.888 68,模型性能提升约13.3%;再将历史灾害点叠加于2种分区图下检验分区精度,相比于未融入形变特征,融入形变特征可纠正约12.44%的误分类区域,能较好地提升分区的可靠性。We process 69 Sentinel-1A satellite images using SBAS-InSAR from July 2018 to August 2019,and obtain surface deformation in Shuicheng district,Liupanshui city as dynamic evaluation factors to address the lack of dynamic feature data in traditional landslide susceptibility studies.The results show that,by fusing ten static evaluation factors and InSAR deformation feature data as dynamic evaluation factors,in a weighted information model coupled with analytic hierarchy process(AHP)and information volume method,the model performance improves approximately 13.3%compared to using only static feature data.The area under the ROC curves is 0.75602 and 0.88868,respectively.To assess zoning accuracy,we overlay historical disaster sites on two types of zoning maps.Compared to scenarios without the inclusion of deformation features,the introduction of deformation features corrected approximately 12.44%of misclassified areas,significantly enhancing zoning reliability.

关 键 词：相关性矩阵 静态特征数据 InSAR形变特征数据 加权信息量 滑坡易发性评价 

分 类 号：P237[天文地球—摄影测量与遥感] P258[天文地球—测绘科学与技术]