Landslide dynamic hazard assessment based on InSAR and information-logistic regression coupling model

doi:10.13474/j.cnki.11-2246.2025.0608

Abstract

Abstract: In view of the issues of significant discrepancies, poor timeliness, and false negative errors in the results of various landslide hazard assessment models.This paper takes Xiangyun county, Yunnan province as the study area.Firstly, compares the performance of four models:information(I), information-logistic regression(I-LR), long short-term memory(LSTM) and support vector machine(SVM), to determine the most suitable model for landslide hazard assessment. Then, the SBAS-InSAR method is used to process the Sentinel-1 data from both ascending and descending orbits between August 2019 and July 2023, and the surface slope deformation rate is calculated. Finally, the landslide hazard classification and slope deformation rate are combined using a correction matrix to generate the dynamic landslide hazard map.The results show that the I-LR model has better prediction accuracy and stability than the other three models.The landslide dynamic hazard map, generated by combining the I-LR model landslide hazard classification and the InSAR slope deformation rate classification, demonstrates better identification of unstable deformation areas. The proportion of low hazard areas is reduced by 10.35%, while the proportions of lower, medium, and high hazard areas increase by 6.30%, 2.45%, and 1.60%, respectively. This enhances both the timeliness and accuracy of landslide hazard assessment results.

Key words: landslide, hazard mapping, informatics-logistic regression model, SBAS-InSAR

CLC Number:

P237

WANG Chenyu, PENG Junhuan, XUE Yueming, LI Xu, ZHANG Yan. Landslide dynamic hazard assessment based on InSAR and information-logistic regression coupling model[J]. Bulletin of Surveying and Mapping, 2025, 0(6): 43-48.

References

[1] 方然可,刘艳辉,黄志全.基于机器学习的区域滑坡危险性评价方法综述[J].中国地质灾害与防治学报,2021,32(4):1-8.
[2] 谢明娟,蒲瑞,韩信,等.基于多模型的滑坡易发性评价[J].测绘与空间地理信息,2019,42(10):83-85.
[3] 黄龙,孙倩,胡俊.基于InSAR与随机森林的滑坡敏感性评价与误差改正[J].测绘通报,2022(10):13-20.
[4] 朱建军,李志伟,胡俊.InSAR变形监测方法与研究进展[J].测绘学报,2017,46(10):1717-1733.
[5] 曾韬睿,邬礼扬,金必晶,等.基于stacking集成策略和SBAS-InSAR的滑坡动态易发性制图[J].岩石力学与工程学报,2023,42(9):2266-2282.
[6] ZHOU Shu,OUYANG Chaojun,HUANG Yu.An InSAR and depth-integrated coupled model for potential landslide hazard assessment[J].Acta Geotechnica,2022,17(8):3613-3632.
[7] 熊俊楠,朱吉龙,苏鹏程,等.基于GIS与信息量模型的溪洛渡库区滑坡危险性评价[J].长江流域资源与环境,2019,28(3):700-711.
[8] 钱紫玲,王平,李娜,等.基于信息量和逻辑回归耦合模型的黄土地震滑坡危险性分析[J].地震工程学报,2023,45(3):706-715.
[9] 王守华,王睿菘,孙希延,等.基于CF-CNN-LSTM模型的滑坡易发性评价[J].自然灾害学报,2024,33(5):84-95.
[10] 王本栋,李四全,许万忠,等.基于3种不同机器学习算法的滑坡易发性评价对比研究[J].西北地质,2024,57(1):34-43.
[11] 邢明泽,左小清,张荐铭,等.基于SBAS-InSAR技术的西南山区滑坡稳定性监测[J].测绘通报,2024(2):63-68.
[12] 石固林,陈强,刘先文,等.联合升降轨Sentinel-1A数据监测桃坪乡古滑坡沿坡向的形变速度场[J].工程地质学报,2022,30(4):1350-1361.
[13] CIAMPALINI A,RASPINI F,LAGOMARSINO D,et al.Landslide susceptibility map refinement using PSInSAR data[J].Remote Sensing of Environment,2016,184:302-315.
[14] 杨冰颖,缪海波,马闯,等.基于多模型耦合的永嘉县滑坡易发性评价[J].河南城建学院学报,2024,33(4):91-99.

[1]	FANG Lu, XING Yin. Extraction of landslide influence factors based on Relief-F feature preference and modeling analysis of susceptibility to landslides [J]. Bulletin of Surveying and Mapping, 2025, 0(6): 90-96.
[2]	WANG Shenli, LIU Yi, HAN Hao, DU Yong. The deformation monitoring and prediction of ultra-high voltage transmission channels using combined SBAS-InSAR and DS-InSAR [J]. Bulletin of Surveying and Mapping, 2025, 0(6): 130-135,141.
[3]	TU Liping, CHEN Meiqiu, LENG Peng. Landslide hazard identification based on the object detection algorithm YOLOv9:taking Yongxin county as an example [J]. Bulletin of Surveying and Mapping, 2025, 0(6): 37-42,102.
[4]	SHAO Yaxuan, MA Jing, HAN Lili, YAO Guanyu. Time series analysis and prediction of surface subsidence based on SBAS-InSAR technology [J]. Bulletin of Surveying and Mapping, 2025, 0(4): 51-57,62.
[5]	ZHOU Tianxiang, DONG Jinglong, SHEN Qiang. Spatio-temporal analysis of surface subsidence along the Jinan subway line based on SBAS-InSAR technology [J]. Bulletin of Surveying and Mapping, 2025, 0(4): 109-113.
[6]	YANG Yang, JIA Hongguo, BAI Zhenghang, LIU Yuchen. Integrated monitoring of permafrost deformation in the Qinghai-Xizang Plateau using machine learning and SBAS-InSAR [J]. Bulletin of Surveying and Mapping, 2025, 0(4): 45-50.
[7]	SHANG Yiwei, XIONG Junnan, JIA Qian, LUO Siyuan, WANG Qisheng, CAO Yifan. Identification and analysis of landslide hazards in Mianning county using fusion of ascending and descending orbit SBAS-InSAR technology [J]. Bulletin of Surveying and Mapping, 2025, 0(4): 58-62.
[8]	PENG Yibo, YANG Weifang, YAN Xiangrong, GAO Motong, HOU Yuhao, ZHANG Delong. 3D deformation prediction of mine surface based on combined SMA-CNN-GRU-Attention modeling [J]. Bulletin of Surveying and Mapping, 2025, 0(3): 8-14,20.
[9]	ZHANG Yuxin, YUAN Xiping, GAN Shu, PENG Xiang, WANG Song. Application of time series InSAR technology in surface subsidence monitoring and spatio-temporal evolution analysis in mining area [J]. Bulletin of Surveying and Mapping, 2025, 0(3): 15-20.
[10]	XU Qingsong, HU Jun, CUI Wengang, LIU Suihua, HU Dan, NING Fei, LI Man. Identification of landslide hazards of Beipanjiang Guangzhao hydropower station based on Lutan-1 SAR satellite [J]. Bulletin of Surveying and Mapping, 2025, 0(3): 71-75,132.
[11]	SHEN Xianming, LI Sumin, GUO Jun, MAO Jiaqi, YAN Shenghang, DAI Zuomin, PENG Xiang. Slope constraint-based 3D InSAR deformation analysis in alpine mining areas [J]. Bulletin of Surveying and Mapping, 2025, 0(2): 77-82.
[12]	YAN Shenghang, LI Sumin, GUO Jun, SONG Yufei, SHEN Xianming. Interpretation of four-dimensional characteristics of giant landslides based on multi-source remote sensing: a case study of the Nuole landslide [J]. Bulletin of Surveying and Mapping, 2025, 0(2): 48-52,63.
[13]	PENG Xiang, GAN Shu, YUAN Xiping, ZHU Zhifu, LI Xuan, GONG Weizhen. Fusion of InSAR and GWO-LSTM for deformation monitoring and prediction of high and steep mountains: a case study of a slope in the Jinsha River basin [J]. Bulletin of Surveying and Mapping, 2024, 0(9): 44-49.
[14]	LIU Yiming, XU Shenghua, LIU Chunyang, MA Yu. Landslide susceptibility assessment considering multi-method integrated feature selection and negative sample optimization [J]. Bulletin of Surveying and Mapping, 2024, 0(9): 74-79.
[15]	XU Yuxiang, HU Qingwu, DUAN Yansong, LI Jiayuan, AI Mingyao, ZHAO Pengcheng. Landslide dynamic monitoring technology by integrating LiDAR point cloud and UAV image [J]. Bulletin of Surveying and Mapping, 2024, 0(8): 42-47.