[1]苏晓军a,张 毅b*,贾 俊,等.基于InSAR技术的秦岭南部略阳县潜在滑坡灾害识别研究[J].山地学报,2021,(1):59-70.[doi:10.16089/j.cnki.1008-2786.000576]
　SU Xiaojuna,ZHANG Yib*,JIA Jun,et al.InSAR-Based Monitoring and Identification of Potential Landslides in Lueyang County, the Southern Qinling Mountains, China[J].Mountain Research,2021,(1):59-70.[doi:10.16089/j.cnki.1008-2786.000576]
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基于InSAR技术的秦岭南部略阳县潜在滑坡灾害识别研究()
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2021年第1期
页码:
59-70
栏目:
山地灾害
出版日期:
2021-01-25

文章信息/Info

Title:
InSAR-Based Monitoring and Identification of Potential Landslides in Lueyang County, the Southern Qinling Mountains, China
文章编号:
1008-2786-(2021)1-059-12
作者:
苏晓军1a; 2; 张 毅1b; 2*; 贾 俊3; 梁懿文1b; 2; 李媛茜1b; 2; 孟兴民1b; 2; 4
1.兰州大学 a资源环境学院, b地质科学与矿产资源学院, 兰州 730000; 2.甘肃省环境地质与灾害防治技术创新中心,兰州 730000; 3.中国地质调查局西安地质调查中心,西安 710054; 4.西部环境教育部重点实验室,兰州 730000
Author(s):
SU Xiaojun1a; 2;  ZHANG Yi1b; 2*;  JIA Jun3;  LIANG Yiwen1b; 2;  LI Yuanxi1b; 2;  MENG Xingmin1b; 2; 4
1. a. College of Earth and Environmental Sciences, b. School of Earth Sciences, Lanzhou University, Lanzhou 730000, China; 2. Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China; 3. Xi'an Center of Geological Survey, China Geological Survey, Xi'an 710054, China; 4.Key Laboratory of Western China's Environmental Systems(Ministry of Education), Lanzhou 730000, China
关键词:
秦岭山区;  滑坡;  早期识别;  InSAR;  地表形变
Keywords:
the Qinling mountains;  landslide;  early-identification;  InSAR;  surface deformation
分类号:
K903
DOI:
10.16089/j.cnki.1008-2786.000576
文献标志码:
C
摘要:
秦岭山区因构造活动、人类活动、气候变化等多重因素作用而面临较高的地质灾害风险, 由于地形高差大、坡度陡,植被覆盖率高,滑坡灾害识别调查难度高,急需发展先进的地质灾害监测识别技术体系,提升高植被覆盖山区地质灾害隐患详细调查识别能力。不同的InSAR技术和SAR数据有各自的优势,单一InSAR技术难以在高植被覆盖山区进行区域变形监测和地质灾害隐患识别。本研究结合多种InSAR技术和不同波段SAR数据的探测优势,对秦岭南部山区略阳县城关镇的110 km2范围进行对地观测,综合光学遥感解译和野外调查,识别出滑坡地质灾害隐患点共52处。研究表明:(1)在高植被覆盖山区,D-InSAR技术和L波段SAR数据的应用,能够提升识别高植被覆盖山区潜在滑坡位置和范围的效率。而时序InSAR技术和Sentinel数据可应用于典型灾害点时间变形模式分析。(2)略阳县滑坡主要为黄土滑坡、堆积层滑坡、黄土基岩滑坡三类; 不稳定斜坡主要有陡峭风化基岩不稳定斜坡、不稳定人工边坡,破坏形式主要为基岩崩塌、剥坠落及黄土斜坡崩滑、泥流等; 本研究所识别的潜在地质灾害可为略阳县防灾减灾工作提供数据支持,提出的InSAR潜在地质灾害早期识别方法体系可为秦岭及类似高植被覆盖山区地质灾害监测识别提供方法参考。
Abstract:
The Qinling mountains has been severely haunted by geohazards, such as landslides, debris flows etc. Due to the joint geo-environmental effects of neo-tectonic movement, human activities, and climate change in the Qinling mountains, mechanism interpretation and their earlier identification of landslides had been challenging field investigators, particularly for underlying geohazards, whose occurrence state are characterized by high elevation difference, steep slope, and dense vegetation coverage. There is an urgent need to develop a practical detecting technology for determination of landslide potentials in steep mountain areas covered by dense vegetation. In this study, multiple InSAR technologies(PS-InSAR, SBAS-InSAR, and D-InSAR)and two SAR datasets(Sentinel-1A and Alos-2)were introduced into an improved procedure to solve the technical problem created by a single InSAR approach, which is only applicable to an area with little vegetation cover. The area with an area of 110 km2 in the vicinity of Chengguan district, Lueyang county,, China was targeted as case study. In this procedure, ground deformation measurement, field monitoring and optical image interpretation were calibrated and analyzed, and 52 underlying landslides were delineated in the study area. The achievements are concluded:(1)The combined use of D-InSAR and L band SAR data facilitated the recognition of underlying landslides at a regional scale in mountainous areas with dense vegetation coverage. The time-series InSAR technique and Sentinel-1A were great success in tracing the temporal deformation patterns of underlying landslides;(2)The active landslides in Lueyang county were classified into three types: loess landslides, colluvial landslides, and loess-rock landslides. Unstable slopes mainly consisted of steep weathered bedrock and artificial slopes with potential for rockfall, collapse, loess-slide and flowslide. The dataset of underlying landslides collected by present study can provide references for local authorities for disaster prevention and mitigation purpose. Our proposed procedure to underlying landslide early detection based on multiple InSAR technologies can provide a feasible technical guidance for geohazard control not only at some places of the Qinling Mountains but similar mountainous areas with dense vegetation cover.

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备注/Memo

备注/Memo:
收稿日期(Received date):2020-05-4; 改回日期(Accepted date): 2020-12-29
基金项目(Foundation item):甘肃省科技重大专项(19ZD2FA002); 国家重点研发计划项目(2017YFC1501005); 兰州大学中央高校基本科研业务费专项资金(lzujbky-2019-28); 自然资源部中国地质调查局地质调查项目(DD20190714)。[Science and Technology Major Project of Gansu Province(19ZD2FA002); National Key Research and Development Program of China(2017YFC1501005); Fundamental Research Funds for the Central University(lzujbky-2019-28); Project of China Geological Survey(DD20190714)]
作者简介(Biography):苏晓军( 1995-),男,青海互助县人,博士研究生,主要研究方向:环境遥感、地质灾害监测与评价。[SU Xiaojun(1995-), male, born in Huzhu County of Qinghai Province, Ph.D. candidate, research on environmental remote sensing, geological disaster monitoring and assessment]E-mail: suxj19@lzu.edu.cn
*通讯作者(Corresponding author):张毅(1991-),男,甘肃兰州人,博士,讲师,研究方向:遥感与地质灾害监测预警。[ZHANG Yi(1991-), male, born in Lanzhou, Gansu province, Ph.D., Assistant professor, specialized in remote sensing, geohazard monitoring and early warning research] E-mail: zhangyigeo@lzu.edu.cn
更新日期/Last Update: 2021-01-30