[1]赵淑芬,曾润强*,张宗林,等.黄河上游潜在滑坡早期识别及影响因素[J].山地学报,2022,(2):249-264.[doi:10.16089/j.cnki.1008-2786.000669]
　ZHAO Shufen,ZENG Runqiang*,ZHANG Zonglin,et al.Early Identification and Influencing Factors of Potential Landslides in the Upper Reaches of the Yellow River, China[J].Mountain Research,2022,(2):249-264.[doi:10.16089/j.cnki.1008-2786.000669]
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黄河上游潜在滑坡早期识别及影响因素
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2022年第2期
页码:
249-264
栏目:
山地灾害
出版日期:
2022-03-25

文章信息/Info

Title:
Early Identification and Influencing Factors of Potential Landslides in the Upper Reaches of the Yellow River, China
文章编号:
1008-2786-(2022)2-249-16
作者:
赵淑芬1; 2; 曾润强1; 2*; 张宗林1; 2; 王 鸿1; 2; 孟兴民1; 2
1.兰州大学 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
Author(s):
ZHAO Shufen1; 2;  ZENG Runqiang1; 2*;  ZHANG Zonglin1; 2;  WANG Hong1; 2;  MENG Xingmin1; 2
1. School of Earth Sciences, Lanzhou University, Lanzhou 730000, China; 2. Technology & Innovation Centre for Environment Geology and Geohazards Prevention, Lanzhou 730000, China
关键词:
黄河上游;  SBAS-InSAR技术;  早期识别;  席笈滩滑坡;  锁子滑坡;  变形阶段
Keywords:
upper reach of the Yellow River;  SBAS-InSAR technology;  early-identification;  Xijitan landslide;  Suozi Landslide;  deformation stage
分类号:
P642.22
DOI:
10.16089/j.cnki.1008-2786.000669
文献标志码:
C
摘要:
黄河上游特殊的地质地貌环境与区域气候,孕育了大型、特大型和巨型滑坡群,频发的地质灾害影响着黄河上游的经济发展和生态保护。InSAR技术可用于地质灾害早期识别、预防地质灾害发生和降低灾害损失,然而针对黄河上游区域的潜在滑坡早期识别及影响因素却鲜有开展。本研究基于2017年2月至2020年12月的SAR影像,运用SBAS-InSAR技术获取黄河上游(龙羊峡至刘家峡段)南北20 km范围内的地表形变速率,结合光学遥感影像和野外调查确定潜在滑坡。研究发现:(1)确定出的355处潜在滑坡,在空间分布上具有集群性,其中85处位于老滑坡上。同时,区内潜在滑坡主要集中于海拔1600～2200 m的河谷阶地和低山丘陵,分布在坡度10°～30°,坡向45°～180°,高差50～200 m的较软弱地层上,受NWW-NW向构造影响强烈。(2)对形变速率和面积较大且直接威胁村庄和道路的席笈滩滑坡和锁子滑坡变形特征的野外调查和原因的分析表明,这两个滑坡均为老滑坡局部复活,目前分别处于稳定蠕滑和波动蠕滑阶段。本研究结果具有较高的准确性和可靠性,可为该段潜在滑坡隐患排查和致灾空间分析提供数据参考,具有重要的科学意义与应用价值。
Abstract:
The special geological, geomorphological environment and regional climate, jointly contribute to the formation of large to giant landslide groups in the upper reaches of the Yellow River, which influence the region's economic development and ecological protection. InSAR technology can be used for early identification and prevention of geological hazards, helping to reduce the potential casualties and economic losses,but has not yet been applied in the upper reaches of the Yellow River. This study used SBAS-InSAR technology, optical remote sensing images and field surveys to define potential landslides along a 40 km section of the upper reaches of the Yellow River(Longyangxia to Liujiaxia). Specifically, we used SAR images from February 2017 to December 2020 to obtain surface deformation velocity. Results are as follows:(1)The 355 identified potential landslides were clustered in space, and include 85 sites located on old landslides. The potential landslides were mainly concentrated on river valley terraces and on low mountains and hills at an altitude of 1600 m to 2200 m. They were distributed on relatively weak stratum with slope of 10 ° to 30 °, aspect of 45 ° to 180 °, and height difference of 50 m to 200 m. The landslides were strongly affected by NWW-NW faults.(2)The deformation characteristics of the Xijitan Landslide and Suozi Landslide, which have undergone large deformation across a wide area and directly threaten villages and roads, show that both have developed from the local reactivation of old landslides. They were now in the stable creep and fluctuation creep stages, respectively. Our accurate and precise results, provide key data for potential landslide hazard investigation and assessment in this section, and are additionally of great scientific significance and application value.

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相似文献/References:

[1]曾永年,冯兆东,曹广超.基于GIS的黄河上游龙羊峡库区生态环境遥感监测研究[J].山地学报,2003,(02):140.

备注/Memo

备注/Memo:
收稿日期(Received date):2021-10-13; 改回日期(Accepted date): 2022-03-29
基金项目(Foundation item):国家重点研发计划项目(2018YFC1504704); 甘肃省技术创新中心建设项目(18JR2JA006); 中央高校基本科研业务费专项资金项目(lzujbky-2021-sp01)。[National Key R&D Program of China(2018YFC1504704); The Construction Project of Gansu Technological Innovation Center(18JR2JA006); The Fundamental Research Funds for the Central Universities(lzujbky-2021-sp01)]
作者简介(Biography):赵淑芬(1996-),女,甘肃庆阳人,博士研究生,主要研究方向:环境遥感与地质灾害监测。[ZHAO Shufen(1996-), female, born in Qingyang, Gansu province, Ph.D. candidate, research on environmental remote sensing, early identification of geological disasters] E-mail: zhaoshf21@lzu.edu.cn
*通讯作者(Corresponding author):曾润强(1987-),男,湖南邵阳人,讲师,主要研究方向:环境遥感与地质灾害监测。[ZENG Runqiang(1987-), male, born in Shaoyang, Hunan province, Ph.D., lecturer, research on environmental remote sensing and geological disaster monitoring] E-mail:zengrq@lzu.edu.cn
更新日期/Last Update: 2022-03-30