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工程科学与技术:2020,52(6):61-74
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冰川密林陡谷区活动性滑坡InSAR精细识别-以澜沧江梅里雪山段为例
(1.中国地质科学院 地质力学研究所 新构造运动与地质灾害重点实验室,北京 100081;2.中国地质大学(北京) 工程技术学院,北京 100083)
Accurate Identification of Active Landslides in Region Composed with Glacier, Forest, Steep Valley: A Case Study in the Lantsang Meili Snow Mountain Section
(1.Key Lab. of Neotectonic Movement and Geological Hazards, Inst. of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China;2.School of Eng. and Technol., China Univ. of Geosciences(Beijing), Beijing 100083, China)
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投稿时间:2020-04-01    修订日期:2020-10-12
中文摘要: 西南山区地质灾害频发,近年来,茂县滑坡、白格滑坡、水城滑坡等多起大型滑坡灾害表明,灾难型滑坡事件的发生不仅与该地区易孕育地质灾害的条件有关,无法准确识别与监控滑坡灾害的发育发展情况也是主要因素之一。本文利用基于主动式微波遥感成像的InSAR技术,针对传统的地面式地质灾害调查手段与光学遥感技术受限的高山地区,以海拔高、地形陡,夏秋季山高林密、多降雨,春冬季天气寒冷、多降雪的澜沧江梅里雪山段为例,开展活动性地质灾害识别的研究。使用多源遥感数据,包括SAR与多时相传统光学遥感影像,以及PALSAR–1/2和升、降轨的Sentinel–1数据,展开多时段、多角度、多波长、多空间分辨率的组合对地观测;结合D-InSAR、IPTA-InSAR和Stacking计算结果,人机交互及目视解译,并利用Google Earth平台多时相光学遥感数据及3维地貌进行核验。针对典型滑坡灾害进行地面核验,结合现场调查结果与InSAR计算结果分析其稳定性,得到如下成果:1)共识别滑坡地质灾害92处,其中:位于澜沧江河谷段的滑坡地质灾害共76处(占总解译数的82.6%),澜沧江支流解译5处,德钦县城周边地区解译滑坡11处;滑坡平面面积大于20×104 m2的有26处,占解译总数的28.3%。澜沧江河谷区二叠系、三叠系软弱地层沿河谷分布,加上地形、降雨、水库蓄水作用等多重因素影响,沿河谷多发育活动性大型、快速活动滑坡灾害。2)梅里水滑坡是河谷中典型的高位变形滑坡,快速变形区位置集中且海拔较高,有较大势能,河谷内发现多处类似的高位变形活动现象。3)亚贡滑坡在数据观测时段(2007-2019年)一直存在变形情况,且规模巨大,发育的次级滑体有更快的变形速率,滑坡体前缘变形破坏严重,变形度率达7~11 cm/a,威胁滑坡中部平台上居民及耕地安全。4)德钦县城观测范围内共识别11处滑坡变形体,包括丁羊丁滑坡、归巴顶滑坡两处蠕变型滑坡,局部变形现象明显,仍处于发展阶段。5)InSAR技术可同时监测多种灾害类型,县城周边发育4条大型泥石流沟,物源区和流通区都有不同程度的变形现象;高海拔区冰碛物活动频繁、变形量大,除冰碛物与冰雪相互作用形成的冰碛物滑坡外,多处高山斜坡风化破碎严重,也存在较强的变形现象。高山地区多轨道、多类型SAR数据组合的方式可有效弥补SAR卫星自身成像缺陷,提高可观测面积。与多种InSAR算法的结合,可以发挥不同SAR数据的长处,提高活动性滑坡的识别精度;在澜沧江高寒、陡峭、多植被的山区,短波长的Sentinel–1数据可以通过大数据量的Stacking计算显著改善计算效果,长波段的PALSAR1/2数据在恶劣条件下有更好的适应性,在IPTA-InSAR的结果中有更好的表现。
中文关键词: 澜沧江  地质灾害  滑坡  滑坡识别  InSAR
Abstract:Geological hazards occur frequently in the southwest mountainous area. In recent years, successive large-scale landslide hazards such as Maoxian landslide, Baige landslide, and Shuicheng landslide have shown that the occurrence of catastrophic landslide events is not only related to the conditions that are prone to breeding geological hazards in the area, but also connecting with the failure in accurately identifying and monitoring the development of landslide disasters. Based on active microwave remote sensing imaging, the InSAR technology was utilized to identify of active geological disasters in Meili Snow Mountain sections of the Lantsang River, where traditional ground-based geological disaster survey methods and optical remote sensing technology were limited. Multi-source remote sensing data was used, including SAR images and multi-temporal traditional optical remote sensing images. By using PALSAR–1/2 and Sentinel–1 data of ascending and descending orbits, the earth observations of multiple periods, multiple angles, multiple wavelengths and spatial resolutions were carried out. Combined with the calculation results of D-InSAR, IPTA-InSAR and Stacking, Human-computer interaction, visual interpretation, the testing data was verified comparing with the multi-temporal optical remote sensing data and three-dimensional topography collected from Google Earth. Ground verification for typical landslide disasters was conducted and its stability was analyzed by using InSAR calculation results. The results of this work are as follows: 1) A total of 92 landslide geological hazards were identified, of which 76 landslide geological hazards in the Lantsang valley section accounted for 82.6% of the total interpretation; 5 Lantsang tributaries were interpreted, and Deqin County surrounding areas interpreted 11 landslides. There were 26 landslides with a floor area greater than 200000 square meters, accounting for 28.3% of the total interpretated ones. The Permian and Triassic weak strata in the Lantsang valley area are distributed along the valley. Coupled with the influence of multiple factors such as topography, rainfall, and reservoir storage, there were many large and fast-moving landslide disasters along the valley. 2) The Meilishui landslide was a typical high-level deformation landslide in the river valley. The rapid deformation area was located at a high altitude and had a large potential energy. Many similar high-level deformation activities have been found in the river valley. 3) The Yagong landslide has always been deformed during the data observation period (2007-2019), and the scale was huge. The developed secondary landslide had a faster deformation rate, and the front edge of the landslide had serious deformation and damage, with a deformation rate of 7~11 cm/a, threatening the safety of residents and cultivated land on the platform in the middle of the landslide. 4) A total of 11 landslide deformation bodies were identified in the observation range of Deqin County, including two creep-type landslides, i.e., Dingyangding landslide and Guibading landslide, with obvious local deformation phenomena and still in the development stage. 5) Multiple disaster types can be monitored by InSAR technology. Four large debris flow ditches were developed around the county, and the source area and circulation area had different degrees of deformation; the high-altitude area had frequent activities and large deformations of moraines. In addition to the moraine landslide formed by the interaction of ice and snow, many high mountain slopes were severely weathered and broken, and there were also strong deformations. The combination of multi-orbit and multi-type SAR data in high mountain areas can effectively compensate for SAR satellite imaging defects and increase the observable area. Combining with multiple InSAR algorithms can take advantages of different SAR data and improve the identification accuracy of active landslides; in the cold, steep, and vegetation-rich mountainous area of the Lantsang, short-wavelength Sentinel–1 data can be used for stacking calculations, which significantly improved the calculation effect. The long-band PALSAR–1/2 data had better adaptability under harsh conditions, and had better performance in the IPTA-InSAR results.
文章编号:202000243     中图分类号:P694    文献标志码:
基金项目:国家重点研发计划项目(2018YFC1505002);基本科研业务费专项(JYYWF20181501);国家自然科学基金项目(41672359;41807299)
作者简介:周振凯(1993-),男,博士生.研究方向:地质灾害InSAR监测.E-mail:zzk114043@163.com
引用文本:
周振凯,姚鑫,刘红岩,任开瑀.冰川密林陡谷区活动性滑坡InSAR精细识别-以澜沧江梅里雪山段为例[J].工程科学与技术,2020,52(6):61-74.
ZHOU Zhenkai,YAO Xin,LIU Hongyan,REN Kaiyu.Accurate Identification of Active Landslides in Region Composed with Glacier, Forest, Steep Valley: A Case Study in the Lantsang Meili Snow Mountain Section[J].Advanced Engineering Sciences,2020,52(6):61-74.