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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (4) : 174-181     DOI: 10.6046/gtzyyg.2019.04.23
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Landslide activity characteristics and stability analysis based on high-resolution remote sensing image: A case study of Dongmiaojia landslide
Weijie JIA1,2, Zhihua WANG1
1. Institute of Geographic Sciences and Natural Resources Research, China Academy of Sciences, Beijing 100101, China
2. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
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Abstract  

Based on the digital landslide technology, the authors employed revised 10 high-resolution remote sensing images between 2002 and 2016 as remote sensing image information source, densified ASTER DEM data as landslide elevation information source, and 1:200 000 Luoyang City regional geological maps as the geological environment source and, in combination with Dongmiaojia landslide, analyzed the creation conditions and active characteristics of the landslide, and predicted the present stability situation of landslide. The result shows that the active process of Dongmiaojia landslide can be divided into four stages, i.e., the first landslide activity, the subsequent traction activity, the front landslide activity and the follow-up activity, which formed topography of four level terraces with seven platforms and the front steep slope. The estimation of the total size of the Dongmiaojia landslide is 425.4×10 4 m 3. The comparison of ten high-resolution images indicates that there has been no obvious deformation and displacement in the Dongmiaojia landslide in recent years.

Keywords Dongmiaojia landslide      digital landslide technology      interpretation basis      activity characteristics      four level terraces     
:  TP79  
Issue Date: 03 December 2019
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Weijie JIA
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Weijie JIA,Zhihua WANG. Landslide activity characteristics and stability analysis based on high-resolution remote sensing image: A case study of Dongmiaojia landslide[J]. Remote Sensing for Land & Resources, 2019, 31(4): 174-181.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.23     OR     https://www.gtzyyg.com/EN/Y2019/V31/I4/174
Fig.1  Location and regional environment of Dongmiaojia landslide
序号 接收日期 影像特征
1 2002-09-08 初秋,清晰显示滑坡各部分特征
2 2012-06-22 初夏,植被覆盖,部分滑坡细节不清晰
3 2012-12-27 初冬,基本清楚显示滑坡各部分特征
4 2013-03-08 初春,基本清楚显示滑坡各部分特征
5 2014-11-13 秋末,清楚显示滑坡各部分特征
6 2015-03-13 春,雾重,影像不清晰
7 2015-07-01 夏,植被覆盖,部分滑坡细节不清晰
8 2015-12-14 初冬,基本清楚显示滑坡各部分特征
9 2016-09-29 深秋,植被覆盖,部分滑坡细节不清晰
Tab.1  Remote sensing information sources
Fig.2  Image and topography overlay chart of Dongmiaojia landslide
Fig.3  Topographic plane and profile of Dongmiaojia landslide
Fig.4  Slope map of Dongmiaojia landslide
Fig.5  Remote sensing interpretation of dongmiaojia landslide
编号 平均高程/m 面积/ m2
T1 215 1 315
T2 217 7 224
T3 219 2 196
T4 233 2 641
T5 242 1 745
T6 248 3 080
T7 263 3 398
Tab.2  Average elevations and areas of the platforms on the landslide
Fig.6  Main profile of Dongmiaojia landslide
Fig.7  Changes of the nine Dongmiaojia landslide images from 2002 to 2016
[1] 李清波, 徐国刚, 应敬浩 . 黄河小浪底东苗家滑坡稳定性分析及整治措施[ C]//中国水利学会一九九九年优秀论文集.北京: 1999: 367-371.
[1] Li Q B, Xu G G, and Ying J H . Stability analysis and treatment measures of Dongmiaojia landslide in Xiaolangdi Dam of Yellow river[ C].Chinese Water Conservancy Society Excellent Essays in 1999.Beijing: 1999: 367-371.
[2] 王小波, 徐文杰, 张丙印 , 等. DDA强度折减法及其在东苗家滑坡中的应用[J]. 清华大学学报(自然科学版), 2012,52(6):814-820.
[2] Wang X B, Xu W J, and Zhang B Y , et al., Strength reduction of DDA and its application in Dongmiaojia landslide[J]. Journal of Tsinghua University (Science & Technology), 2012,52(6):814-820.
[3] Xu W J, Jie Y X, Li Q B , et al. Genesis,mechanism,and stability of the Dongmiaojia landslide,Yellow River,China[J]. International Journal of Rock Mechanics & Mining Sciences, 2014,67(2):57-68.
[4] 王治华 . 遥感技术在我国水电建设前期工作中的应用[J]. 国土资源遥感, 1995,7(3):1-8.doi: 10.6046/gtzyyg.1995.03.01
doi: 10.6046/gtzyyg.1995.03.01
[4] Wang Z H . The application of remote sensing technique to the hydro-electric construction of earlier stage in China[J]. Remote Sensing for Land and Resource, 1995,7(3):1-8.doi: 10.6046/gtzyyg.1995.03.01
[5] 王治华 . 数字滑坡技术及其应用[J]. 现代地质, 2005,19(2):157-164.
[5] Wang Z H , Progress and applications for digital landslide[J]. Geoscience, 2005,19(2):157-164.
[6] 王治华 . 滑坡遥感[M]. 北京: 科学出版社, 2012.
[6] Wang Z H. Remote Sensing for Landslide[M]. Beijing: Science Press, 2012.
[7] 王治华 . 数字滑坡技术及其典型应用[J]. 中国地质调查, 2016,3(3):47-54.
[7] Wang Z H . Digital landslide technology and its typical application[J]. Geological Survey of China, 2016,3(3):47-54.
[8] 戴其祥 . 黄河小浪底水库工程坝区构造应力场的恢复和分析[J].人民黄河,1991(5):43-48.
[8] Dai Q X . Return and analysis of structural stress field in Xiaolangdi dam site Area[J].Yellow River,1991(5):43-48.
[9] 李志建, 滕伟福, 周爱国 , 等. 黄河小浪底水库诱发地震预测[J]. 地质灾害与环境保护, 2000,11(4):306-309.
[9] Li Z J, Teng W F, and Zhou A G , et al. The prediction of reservoir-induced earthquake in Xiaolangdi area,Yellow River[J]. Journal of Geological Hazards and Environment Preservation, 2000,11(4):306-309.
[10] 童立强, 郭兆成 . 典型滑坡遥感影像特征研究[J]. 国土资源遥感, 2013,25(1):86-92.doi: 10.6046/gtzyyg.2013.01.16.
doi: 10.6046/gtzyyg.2013.01.16
[10] Tong L Q, Guo Z C . A study of remote sensing image features of typical landslides[J]. Remote Sensing for Land and Resource, 2013,25(1):86-92.doi: 10.6046/gtzyyg.2013.01.16.
[11] 胡江春, 邹正盛, 李晨 , 等. 东苗家滑坡稳定性分析及处理方式研究[J]. 中原工学院学报, 2015,26(6):57-60.
[11] Hu J C, Zhou Z S, Li C , et al. Dongmiaojia landslide stability evaluation and treatment method[J]. Journal of Zhongyuan University of Technology. 2015,26(6):57-60.
[12] Jiang X, Wan L, Wang X , et al. Permeability Heterogeneity in a Fractured Sandstone-Mudstone Rock Mass in Xiaolangdi Dam Site,Central China[J]. Acta Geologica Sinica(English,Edition), 2009,83(5):962-970.
[13] 王治华, 贾伟洁 . 基于数字滑坡技术的三峡新滩滑坡研究[J]. 工程地质学报, 2017,25(3):762-771.
[13] Wang Z H, Jia W J . Study on Xintan landslide in Three Gorge based on digital landslide technique[J]. Journal of Engineering Geology, 2017,25(3):762-771.
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