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Early identification and characteristics of potential landslides in the Bailong River Basin using InSAR technique

  • role： First author第一作者
  • Affiliation：

    School of Earth Sciences, Lanzhou University, Lanzhou 730000, China

    Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China

  • Email：liyq19@lzu.edu.cn
  • Introduction：李媛茜，1996年生，女，硕士研究生，研究方向为环境遥感与地质灾害。E-mail: liyq19@lzu.edu.cn
LI Yuanxi13,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Sciences, Lanzhou University, Lanzhou 730000, China

    Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China

  • Email：zhangyigeo@lzu.edu.cn
  • Introduction：张毅，1991年生，男，讲师，研究方向为环境遥感与地质灾害。E-mail: zhangyigeo@lzu.edu.cn
ZHANG Yi13*,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

    Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China

SU Xiaojun23,  
  • Affiliation：

    College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

    Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China

ZHAO Fumeng23,  
  • Affiliation：

    School of Earth Sciences, Lanzhou University, Lanzhou 730000, China

    Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China

LIANG Yiwen13,  
  • Affiliation：

    School of Earth Sciences, Lanzhou University, Lanzhou 730000, China

    Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China

    Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou 730000, China

MENG Xingmin134,  
  • Affiliation：

    Xi’an Center of Geological Survey, China Geological Survey, Xi’an, 710054, China

JIA Jun5

resumen

The Bailong River Basin is located in the southeast of Gansu Province and situated at the intersection of the Qinghai–Tibet Plateau, the Loess Plateau, and the Sichuan Basin. Geohazards, such as landslides and debris flows, have high frequency and wide distribution due to the impact of rainfall, tectonic activity, and earthquakes. These phenomena pose a serious threat to the safety of life and property of the local people. Investigating a new method to detect potential landslide and study its characteristics is important to provide key supports for local disaster prevention and mitigation.In this study, an InSAR technique called Small Baseline Subset was selected to process 60 Sentinel-1A SAR images acquired from March 2018 to March 2019. Moreover, the study area was clipped into 8 blocks to improve the efficiency of data processing and minimize the errors caused by the complex terrain of the region.On the basis of the abovementioned method, the mean surface displacement rates ranging from -158 and 100 mm/year along the line-of-sight direction were detected during March 2018 and March 2019. A total of 114 potential landslides were investigated and identified in the Bailong River Basin based on optical image interpretation and field survey. Statistical analysis of their basic information shows that most of the potential landslides tend to develop in the S, SSW, and SSE-faced slope with a gradient of 20°—40°. The elevation difference of potential landslides is less than 150 m. The slope material is mostly composed of slope deposits and heavy weathered rocks, such as phyllite. The majority of potential landslides have an area less than 5×104 m2.Yahuokou landslide, which was investigated as a potential landslide with displacement rates > 38 mm/year, broke and ran into Min River from 19 July 2019. On the basis of the analysis of landslide pre-cursory deformation and geomorphology, the landslide was divided into three sections: source, propagation, and accumulation areas. The successful identification of potential landslide demonstrates the applicability and efficiency of InSAR technique in landslide investigation and identification. This research provides foundation and scientific support for landslide mapping and disaster prevention in Bailong River Basin.

palabra clave

Bailong River Basin;potential landslide;InSAR;ground deformation;early identification;characteristics;Yahuokou landslide

References

  1. 1.
    Berardino P, Fornaro G, Lanari R and Sansosti E. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2375-2383
  2. 2.
    Cerdà A. 1996. Seasonal variability of infiltration rates under contrasting slope conditions in southeast Spain. Geoderma, 69(3/4): 217-232
  3. 3.
    Chen M. 2017. Research on development characteristics and formation mechanism of large-scale landslides in Bailongjiang River Basin. Chengdu: Chengdu University of Technology
  4. 4.
    Du G L, Zhang Y S, Gao J C, Sun Y C and Guo C B. 2016. Landslide susceptibility assessment based on GIS in Bailongjiang watershed, Gansu Province. Journal of Geomechanics, 22(1): 1-11
  5. 5.
    Fan X Y and Qiao J P. 2004. Topography discriminance of the assessment of Landslide Hazard Degree. Journal of Mountain Science, 22(6): 730-734
  6. 6.
    Guo C B, Ren S S, Li X, Zhang Y S, Yang Z H, Wu R A and Jin J J. 2019. Development characteristics and reactivation mechanism of the Jiangdingya ancient landslide in the Nanyu Town, Zhouqu County, Gansu Province. Geoscience, 33(1): 206-217
  7. 7.
    Guo H D and Zhang L. 2019. 60 years of radar remote sensing: Four-stage development. Journal of Remote Sensing, 23(6): 1023-1035
  8. 8.
    Guzzetti F, Manunta M, Ardizzone F, Pepe A, Cardinali M, Zeni G, Reichenbach P and Lanari R. 2009. Analysis of ground deformation detected using the SBAS-DInSAR technique in Umbria, Central Italy. Pure and Applied Geophysics, 166(8/9): 1425-1459
  9. 9.
    Haque U, Da Silva P F, Devoli G, Pilz J, Zhao B X, Khaloua A, Wilopo W, Andersen P, Lu P, Lee J, Yamamoto T, Keeling D, Wu J H and Glass G E. 2019. The human cost of global warming: deadly landslides and their triggers (1995-2014). Science of the Total Environment, 682: 673-684
  10. 10.
    Jiang S, Wen B P, Li Z H and Zhao C. 2016. An analysis of the activity features of the Suoertou landslide in Zhouqu county of Gansu. Hydrogeology and Engineering Geology, 43(2): 69-74, 92
  11. 11.
    Jiang X Z, Wen B P, Jiang S, Feng C H, Zhao C and Li R D. 2015. Main factors analysis for controlling kinematic behavior of Suoertou landslide. Journal of Jilin University (Earth Science Edition), 45(6): 1798-1807
  12. 12.
    Lee C H, Lin S H, Kao C L, Hong M Y, Huang P C, Shih C L and Chuang C C. 2020. Impact of climate change on disaster events in metropolitan cities-trend of disasters reported by Taiwan national medical response and preparedness system. Environmental Research, 183: 109186
  13. 13.
    Li S Z, Dai S, Wang H W and Zhang X. 2015. Fault features and their implications on distribution and formation of landslides in Bailongjiang Region. Journal of Lanzhou University (Natural Sciences), 51(2): 145-152
  14. 14.
    Meng X M, Chen G, Guo P, Xiong M Q and Wasowski J. 2013. Research of landslides and debris flows in Bailong River Basin: progress and prospect. Marine Geology and Quaternary Geology, 33(4): 1-15
  15. 15.
    Pan G T, Xiao Q H, Lu S N, Deng J F, Feng Y M, Zhang K X, Zhang Z Y, Wang F G, Xing G F, Hao G J and Feng Y F. 2009. Subdivision of tectonic units in China. Geology in China, 36(1): 1-28
  16. 16.
    Pang M K. 2011. Study on the Developing Characteristics and Genesis Geological Environmental Conditions of Landslides in the Bailongjiang River Basin. Chengdu: Chengdu University of Technology
  17. 17.
    Schmidt D A, Bürgmann R. 2003. Time-Dependent land uplift and subsidence in the Santa Clara Valley, California, from a large interferometric synthetic aperture radar data set. Journal of Geophysical Research: Solid Earth, 108(B9): 2416
  18. 18.
    Tang X K. 2014. Fractal Features of Landslides in the Area Southern Gansu Province in the Bailongjiang River Basin. Lanzhou: Lanzhou University
  19. 19.
    Wang C Y and Chen J P. 2007. Study on the relationship between aspect and slope stability. Technological Development of Enterprise, 26(12): 12-14, 20
  20. 20.
    Wang T and Liao M S. 2018. Coseismic displacement derived from Sentinel-1 data: latest techniques and case studies. Journal of Remote Sensing, 22(S1): 120-127
  21. 21.
    Wasowski J and Bovenga F. 2014. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: current issues and future perspectives. Engineering Geology, 174: 103-138
  22. 22.
    Xiao L, He Y G, Xing X M, Wen D B, Tong C G, Chen L F and Yu X Y. 2019. Time series subsidence analysis of drilling solution miningrock salt mines based on Sentinel-1 data and SBAS-InSAR technique. Journal of Remote Sensing, 23(3): 501-513
  23. 23.
    Yang W M, Huang X, Zhang C S and Si H B. 2014. Deformation behavior of landslides and their formation mechanism along Pingding Huama active fault in Bailongjiang River region. Journal of Jilin University (Earth Science Edition), 44(2): 574-583
  24. 24.
    Yu Z S. 2007. Study on prediction of disaster loss and causes of Xieliupo landslides. Gansu Science and Technology, 23(12): 72-76
  25. 25.
    Zhang J X, Gong J, Ma X C and Liu D Q. 2018. GeoDA-based spatial correlation analysis of landscape fragmentation in Bailongjiang Watershed of Gansu. Chinese Journal of Ecology, 37(5): 1476-1483
  26. 26.
    Zhang Y. 2018. Detecting Ground Deformation and Investigating Landslides Using InSAR Technique—Taking Middle Reach of Bailong River Basin as an Example. Lanzhou: Lanzhou University
  27. 27.
    Zhang Y D, Li Y D, Dong J, Fan Q, Che B, Zhang L, Zhou Y and Liao M S. 2019. Landslide hazard detection in Markam with time-series InSAR analyses. Journal of Remote Sensing, 23(5): 987-996
  28. 28.
    Zhang Y J, Wan Z, Xie C, Shao Y, Yuan M H, Chen W and Wang X. 2015. Deformation analysis of the seawall in Qiantang estuary with multi-temporal InSAR. Journal of Remote Sensing, 19(2): 339-354
  29. 29.
    Zhao F M, Zhang Y, Meng X M, Su X J and Shi W. 2019. Early identification of geological hazards in the Gaizi valley near the Karakoran Highway based on SBAS-InSAR technology. Hydrogeology and Engineering Geology, 47(1): 142-152

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