| 基于SBAS-InSAR技术的深切割高山峡谷区滑坡灾害早期识别 |
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| 引用本文： | 周定义,左小清,喜文飞,肖波,毕瑞,范馨.基于SBAS-InSAR技术的深切割高山峡谷区滑坡灾害早期识别[J].中国地质灾害与防治学报,2022,33(2):16-24. |
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| 作者姓名： | 周定义 左小清 喜文飞 肖波 毕瑞 范馨 |
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| 作者单位： | 1.昆明理工大学国土资源工程学院，云南 昆明 650093 |
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| 基金项目： | 国家自然科学基金项目（41761081）；云南省应用基础研究计划面上项目（2018FB078） |
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| 摘 要： | 近年来，高山峡谷区滑坡灾害频频发生，给人民生命和财产安全带来严重威胁。针对多数学者利用SAR单轨道数据对高山峡谷区滑坡进行早期识别，存在SAR成像几何畸变造成部分滑坡不能识别、识别结果不全面等问题。为全面准确的对高山峡谷区滑坡隐患进行早期识别，文章采用SBAS-InSAR技术，以东川小江沿线两侧深切割高山峡谷区为研究区，通过升降轨SAR数据结合互补的方式进行滑坡灾害隐患识别，引入高分辨率光学影像等作为辅助识别，最终共识别出18处滑坡灾害体，其中5处为高风险潜在滑坡，并对三类典型潜在滑坡进行分析。分析结果表明:利用升降轨SAR数据结合互补的方式，能有效避免SAR单轨道数据在高山峡谷地区产生的几何畸变问题，同时，该方法能更为准确全面地对高山峡谷区滑坡隐患进行早期识别，为防灾减灾事业及政府部门决策提供一种有效的手段。
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| 关 键 词： | 高山峡谷区 SBAS-InSAR 滑坡灾害 早期识别 东川小江 |
| 收稿时间： | 2021-06-17 |
Early identification of landslide hazards in deep cut alpine canyon using SBAS-InSAR technology |
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| Institution: | 1.Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650093, China2.Department of Geography, Yunnan Normal University, Kunming, Yunnan 650500, China3.Institute of Highway, Yunnan Communications Vocational and Technical College, Kunming, Yunnan 650500, China4.College of Earth Sciences, Chengdu University of Technology, Chengdu, Sichuan 610059, China |
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| Abstract: | In recent years, landslides occurred frequently in mountain and gorge areas, which brought serious threats to people's life and property safety. Most scholars use SAR single-track data for early identification of landslides in alpine and canyon areas, but some landslides cannot be identified due to geometric distortion of SAR imaging, and the identification results are not comprehensive. In order to carry out comprehensive and accurate early identification of landslide hazards in alpine valley area, this paper adopts bas-INSAR technology, takes the deep cut alpine valley area along the Xiaojiang River in Dongchuan as the research area, and adopts the combination of SAR data of lifting and lowering orbit to identify landslide hazards, and introduces high-resolution optical images as auxiliary identification. Finally, 18 landslide disaster bodies were identified, among which 5 were high-risk potential landslides, and three types of typical potential landslides were analyzed. The analysis results show that the use of elevator rail SAR data combined with complementary way, can effectively avoid the SAR single orbital data geometric distortion problem in mountain valley area, at the same time, this method can more accurately comprehensively to early identification of alpine valley area of landslide hazard, the cause of disaster prevention and mitigation and government decision-making provides a effective means. |
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