Identification of Potential Landslide in Jianzha Counctry Based on InSAR and Deep Learning

Author:

Yang Xianwu1,Chen Dannuo1,Dong Yihang1,Xue Yamei1,Qin Kexin1

Affiliation:

1. Xinyang Normal University

Abstract

Abstract

Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR、SBAS-InSAR and optical remote sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. The results indicate that: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, it was found that 10 out of 13 previously occurred landslide disaster points were located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10–40°, elevations between 15–30 m, and slope orientations predominantly towards the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

Publisher

Springer Science and Business Media LLC

Reference41 articles.

1. Flentje P, Chowdhury R. Resilience and sustainability in the management of landslides[C]//Proceedings of the institution of civil engineers-engineering sustainability. Thomas Telford Ltd, 171(1): 3–14(2016).

2. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania);Constantin M;Environ. Earth Sci,2011

3. Identification of potential landslides in the loess hilly area (Xiji County) of Ningxia with InSAR technology;Chen Siming;Science Technology and Engineering,2022

4. Accurate landslide identification by multisource data fusion analysis with improved feature extraction backbone network;Jin Y;Geomatics, Natural Hazards and Risk,2022

5. Automatic Remote Sensing Identification of Co-Seismic Landslides Using Deep Learning Methods;Pang D;Forests,2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3