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基于SBAS-InSAR和光学遥感的天津市北部山区潜在滑坡识别研究

王勇 邢振涛 李锁 闫勇 司甜

灾害学2025,Vol.40Issue(1):30-35,6.
灾害学2025,Vol.40Issue(1):30-35,6.DOI:10.3969/j.issn.1000-811X.2025.01.005

基于SBAS-InSAR和光学遥感的天津市北部山区潜在滑坡识别研究

Identification of Potential Landslides in the Northern Mountainous Area of Tianjin Based on SBAS-InSAR and Optical Remote Sensing

王勇 1邢振涛 1李锁 2闫勇 2司甜1

作者信息

  • 1. 天津城建大学 地质与测绘学院,天津,300384
  • 2. 天津市地质工程勘测设计院有限公司,天津,300191
  • 折叠

摘要

Abstract

Taking the mountainous area in the north of Tianjin as the research object,the identification of po-tential landslide is researched,by using 56 Sentinel-1A data from 2018-01 to 2022-08 processed by SBAS-InSAR technology,and combining with the Landsat8 optical remote sensing images from 2018-2022,shape variable and optical remote sensing image features and NDVI are analyzed to screen the potential landslide area.The results show that:There are 20 large deformation areas identified by SBAS-InSAR technology in the northern mountainous area of Tianjin,19 areas identified by optical remote sensing images combined with various development environment ele-ments,and a total of 5 areas identified jointly by the two technologies.These 5 areas are identified as potential land-slide areas,through the identification and analysis of the settlement information of the 5 areas,the maximum annual deformation rate of the five areas can reach-12 mm/a,and it is necessary to conduct key monitoring and early warn-ing.The combination of the two technologies can effectively improve the identification accuracy of potential landslide areas,and provide a new way for disaster monitoring in the northern mountainous area of Tianjin.

关键词

潜在滑坡/SBAS-InSAR/光学遥感影像/天津市北部山区

Key words

potential landslide/SBAS-InSAR/optical remote sensing image/northern mountainous area of Tianjin

分类

环境科学

引用本文复制引用

王勇,邢振涛,李锁,闫勇,司甜..基于SBAS-InSAR和光学遥感的天津市北部山区潜在滑坡识别研究[J].灾害学,2025,40(1):30-35,6.

基金项目

天津市科技计划项目"面向青少年群体的北斗卫星导航系统专题科普宣传(C)"(21KPHDRC00070) (C)

天津市教委科研计划项目"天津北部山区地表形变时空变化与机理分析"(2021ZD001) (2021ZD001)

国家级大学生创新创业训练计划项目"融合GNSS、ERA5、大气污染物的高时空分辨率的PM2.5浓度预测研究"(202310792010) (202310792010)

灾害学

OA北大核心

1000-811X

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