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Post-disaster assessment of 2017 catastrophic Xinmo landslide (China) by spaceborne SAR interferometry

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Abstract

Timely and effective post-disaster assessment is of significance for the design of rescue plan, taking disaster mitigation measures and disaster analysis. Field investigation and remote sensing methods are the common ways to perform post-disaster assessment, which are usually limited by dense cloud coverage, potential risk, and tough transportation etc. in the mountainous area. In this paper, we employ the 2017 catastrophic Xinmo landslide (Sichuan, China) to demonstrate the feasibility of using spaceborne synthetic aperture radar (SAR) data to perform timely and effective post-disaster assessment. With C-band Sentinel-1 data, we propose to combine interferometric coherence to recognize the stable area, which helps us successfully identify landslide source area and boundaries in a space-based remote sensing way. Complementarily, X-band TanDEM-X SAR data allow us to generate a precise pre-failure high-resolution digital elevation model (DEM), which provides us the ability to accurately estimate the depletion volume and accumulation volume of Xinmo landslide. The results prove that spaceborne SAR can provide a quick, valuable, and unique assistance for post-disaster assessment of landslides from a space remote sensing way. At some conditions (bad weather, clouds, etc.), it can provide reliable alternative.

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References

  • Andersson-Sköld Y, Bergman R, Johansson M, Persson E, Nyberg L (2013) Landslide risk management—a brief overview and example from Sweden of current situation and climate change. Int J Dis Risk Reduct 3:44–61.https://doi.org/10.1016/j.ijdrr.2012.11.002

    Article  Google Scholar 

  • Bai X, Jian J, He S, Liu W (2018) Dynamic process of the massive Xinmo landslide, Sichuan (China), from joint seismic signal and morphodynamic analysis. Bull Eng Geol Environ: 1–11.https://doi.org/10.1007/s10064-018-1360-0

  • Cascini L, Fornaro G, Peduto D (2010) Advanced low-and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales. Eng Geol 112(1–4):29–42

    Article  Google Scholar 

  • Chen M, Tomás R, Li Z, Motagh M, Li T, Hu L, Gong H, Li X, Yu J, Gong X (2016) Imaging land subsidence induced by groundwater extraction in Beijing (China) using satellite radar interferometry. Remote Sens 8:468

    Article  Google Scholar 

  • Cigna F, Bianchini S, Casagli N (2013) How to assess landslide activity and intensity with persistent Scatterer interferometry (PSI): the PSI-based matrix approach. Landslides 10(3):267–283

    Article  Google Scholar 

  • Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng Geol 88(3-4):173–199

  • COMET (2017) Sentinel-1 satellites reveal pre-event movements and source areas of the maoxian landslides, china.http://comet.nerc.ac.uk/sentinel-1-satellites-reveal-pre-event-movements-source-areas-maoxian-landslides-china/. Accessed 18 August 2018

  • Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64:65–87.https://doi.org/10.1016/S0013-7952(01)00093-X

    Article  Google Scholar 

  • Dai K, Li Z, Tomás R, Liu G, Yu B, Wang X, Cheng H, Chen J, Stockamp J (2016) Monitoring activity at the daguangbao mega-landslide (China) using sentinel-1 tops time series interferometry. Remote Sens Environ 186:501–513.https://doi.org/10.1016/j.rse.2016.09.009

    Article  Google Scholar 

  • Dai K, Liu G, Li Z, Li T, Yu B, Wang X, Singleton A (2015) Extracting vertical displacement rates in Shanghai (China) with multi-platform Sar images. Remote Sens 7:9542–9562.https://doi.org/10.3390/rs70809542

    Article  Google Scholar 

  • Del Soldato, M., Riquelme, A., Bianchini, S., Tomàs, R., Di Martire, D., De Vita, P., ... & Calcaterra, D. (2018). Multisource data integration to investigate one century of evolution for the Agnone landslide (Molise, southern Italy). Landslides, 15(11), 2113–2128

  • DLR (2010) TanDEM-X - A New High Resolution Interferometric SAR Mission.http://www.dlr.de/hr/en/desktopdefault.aspx/tabid-2317/3669_read-5488/. Accessed 18 August 2018

  • Dong J, Zhang L, Li M, Yu Y, Liao M, Gong J, Luo H (2018) Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets. Landslides 15(1):135–144.https://doi.org/10.1007/s10346-017-0914-8

    Article  Google Scholar 

  • Du Y, Xu Q, Zhang L, Feng G, Li Z, Chen R-F, Lin C-W (2017) Recent landslide movement in tsaoling, Taiwan tracked by terrasar-x/tandem-x dem time series. Remote Sens 9:353

    Article  Google Scholar 

  • eoPortal Directory (2014) TDX.https://directory.eoportal.org/web/eoportal/satellite-missions/t/tandem-x. Accessed 18 August 2018

  • ESA (2014) Sentinel-1.https://sentinel.esa.int/web/sentinel/missions/sentinel-1. Accessed 18 August 2018

  • Fan J, Zhang X, Su F, Ge Y, Tarolli P, Yang Z, Zeng C, Zeng Z (2017a) Geometrical feature analysis and disaster assessment of the xinmo landslide based on remote sensing data. J Mt Sci 14:1677–1688

    Article  Google Scholar 

  • Fan X, van Westen CJ, Korup O, Gorum T, Xu Q, Dai F, Huang R, Wang G (2012) Transient water and sediment storage of the decaying landslide dams induced by the 2008 Wenchuan earthquake china. Geomorphology 171:58–68

    Article  Google Scholar 

  • Fan X, Xu Q, Scaringi G, Dai L, Li W, Dong X, Zhu X, Pei X, Dai K and Havenith H-B (2017b) Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan china. Landslides 1–18

  • Fernández T, Pérez JL, Colomo C, Cardenal J, Delgado J, Palenzuela JA et al (2017) Assessment of the evolution of a landslide using digital photogrammetry and LiDAR techniques in the Alpujarras region (Granada, southeastern Spain). Geosciences 7(2):32

    Article  Google Scholar 

  • Ferretti A, Prati C, Rocca F (1999) Multibaseline InSAR DEM reconstruction: the wavelet approach. IEEE Trans Geosci Remote Sens 37(2):705–715

    Article  Google Scholar 

  • Fiorucci F, Cardinali M, Carlà R, Rossi M, Mondini AC, Santurri L, Ardizzone F, Guzzetti F (2011) Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology 129:59–70.https://doi.org/10.1016/j.geomorph.2011.01.013

    Article  Google Scholar 

  • Frattini P, Crosta GB, Rossini M and Allievi J (2018) Activity and kinematic behaviour of deep-seated landslides from ps-Insar displacement rate measurements. Landslides 1–18

  • Gao X, Liu Y, Li T, Wu D (2017) High precision dem generation algorithm based on Insar multi-look iteration. Remote Sens 9:741

    Article  Google Scholar 

  • Ge L, Ng AH-M, Li X, Abidin HZ, Gumilar I (2014) Land subsidence characteristics of Bandung basin as revealed by envisat asar and alos palsar interferometry. Remote Sens Environ 154:46–60.https://doi.org/10.1016/j.rse.2014.08.004

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216.https://doi.org/10.1016/S0169-555X(99)00078-1

    Article  Google Scholar 

  • Hanssen RF (2001) Radar interferometry: data interpretation and error analysis (vol. 2). Springer Science & Business Media, Berlin

  • Hu K, Wu C, Tang J, Pasuto A, Li Y, Yan S (2018) New understandings of the June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China. Landslides 15:2465–2474.https://doi.org/10.1007/s10346-018-1073-2

    Article  Google Scholar 

  • Hungr O, Evans SG (2004) Entrainment of debris in rock avalanches: an analysis of a long run-out mechanism. GSA Bull 116:1240–1252.https://doi.org/10.1130/B25362.1

    Article  Google Scholar 

  • Intrieri E, Raspini F, Fumagalli A, Lu P, Del Conte S, Farina P, Allievi J, Ferretti A, Casagli N (2017) The Maoxian landslide as seen from space: detecting precursors of failure with sentinel-1 data. Landslides 15:123–133.https://doi.org/10.1007/s10346-017-0915-7

    Article  Google Scholar 

  • Jiang H, Mao X, Xu H, Yang H, Ma X, Zhong N, Li Y (2014) Provenance and earthquake signature of the last deglacial xinmocun lacustrine sediments at diexi, East Tibet. Geomorphology 204:518–531

    Article  Google Scholar 

  • Lucieer A, Jong SMD, Turner D (2014) Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Prog Phys Geogr 38(1):97–116

    Article  Google Scholar 

  • Meng W, Xu Y, Cheng WC, Arulrajah A (2018) Landslide event on 24 June in Sichuan Province, China: preliminary investigation and analysis. Geosciences 8(2):39

    Article  Google Scholar 

  • Miller M, Shirzaei M (2015) Spatiotemporal characterization of land subsidence and uplift in Phoenix using InSAR time series and wavelet transforms. J Geophys Res Solid Earth 120(8):5822–5842

    Article  Google Scholar 

  • Neelmeijer J, Motagh M, Bookhagen B (2017) High-resolution digital elevation models from single-pass TanDEM-X interferometry over mountainous regions: a case study of Inylchek glacier, Central Asia. ISPRS J Photogramm Remote Sens 130:108–121

    Article  Google Scholar 

  • Ouyang C, Zhao W, He S, Wang D, Zhou S, An H, Wang Z, Cheng D (2017) Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian county, China. J Mt Sci 14:1701–1711

    Article  Google Scholar 

  • Pei XJ, Guo B, Cui SH, Wang DP, Xu Q, Li TT (2018) On the initiation, movement and deposition of a large landslide in Maoxian County, China. J Mt Sci 15(6):1319–1330.https://doi.org/10.1007/s11629-017-4627-1

    Article  Google Scholar 

  • Qiu J, Wang X, He S, Liu H, Lai J, Wang L (2017) The catastrophic landside in Maoxian county, Sichuan, sw China, on june 24, 2017. Nat Hazards 89:1485–1493.https://doi.org/10.1007/s11069-017-3026-9

    Article  Google Scholar 

  • Qu T, Lu P, Liu C, Wu H, Shao X, Wan H, Li N, Li R (2016) Hybrid-Sar technique: joint analysis using phase-based and amplitude-based methods for the xishancun giant landslide monitoring. Remote Sens 8:874

    Article  Google Scholar 

  • Raspini F, Ciampalini A, Del Conte S, Lombardi L, Nocentini M, Gigli G et al (2015) Exploitation of amplitude and phase of satellite SAR images for landslide mapping: the case of Montescaglioso (South Italy). Remote Sens 7(11):14576–14596

    Article  Google Scholar 

  • Rossi G, Tanteri L, Tofani V, Vannocci P, Moretti S, Casagli N (2018) Multitemporal UAV surveys for landslide mapping and characterization. Landslides 15:1045–1052.https://doi.org/10.1007/s10346-018-0978-0

    Article  Google Scholar 

  • Scaringi G, Fan X, Xu Q, Liu C, Ouyang C, Domènech G, Yang F, Dai L (2018) Some considerations on the use of numerical methods to simulate past landslides and possible new failures: the case of the recent Xinmo landslide (Sichuan, China). Landslides 15(7):1359–1375.https://doi.org/10.1007/s10346-018-0953-9

    Article  Google Scholar 

  • Shi X, Zhang L, Zhou C, Li M, Liao M (2018) Retrieval of time series three-dimensional landslide surface displacements from multi-angular Sar observations. Landslides 15(5):1015–1027.https://doi.org/10.1007/s10346-018-0975-3

    Article  Google Scholar 

  • Su L, Hu K, Zhang W, Wang J, Lei Y, Zhang C, Cui P, Pasuto A, Zheng Q (2017) Characteristics and triggering mechanism of Xinmo landslide on 24 June 2017 in Sichuan, China. J Mt Sci 14:1689–1700.https://doi.org/10.1007/s11629-017-4609-3

    Article  Google Scholar 

  • The central people's government of China (2017) The volume of landslides in Mao County,Sichuan Province reached 18 million cubic meters with a maximum drop of 1600 meters.http://www.gov.cn/xinwen/2017-06/24/content_5205161.htm. Accessed 18 August 2018

  • Tomás R, Li Z, Lopez-Sanchez JM, Liu P, Singleton A (2015) Using wavelet tools to analyse seasonal variations from Insar time-series data: a case study of the huangtupo landslide. Landslides 13:437–450.https://doi.org/10.1007/s10346-015-0589-y

    Article  Google Scholar 

  • Tsai F, Hwang J, Chen L, Lin T (2010) Post-disaster assessment of landslides in southern Taiwan after 2009 typhoon morakot using remote sensing and spatial analysis. Nat Hazards Earth Syst Sci 10:2179–2190

    Article  Google Scholar 

  • Van der Horst T, Rutten MM, van de Giesen NC, Hanssen RF (2018) Monitoring land subsidence in Yangon, Myanmar using Sentinel-1 persistent scatterer interferometry and assessment of driving mechanisms. Remote Sens Environ 217:101–110

    Article  Google Scholar 

  • Wang Y, Zhao B and Li J (2018) Mechanism of the catastrophic June 2017 landslide at Xinmo Village, Songping River, Sichuan Province, China. Landslides 15(2):333–345.https://doi.org/10.1007/s10346-017-0927-3

    Article  Google Scholar 

  • Zhao S, Chigira M, Wu X (2018) Buckling deformations at the 2017 xinmo landslide site and nearby slopes, Maoxian, Sichuan, China. Eng Geol 246:187–197.https://doi.org/10.1016/j.enggeo.2018.09.033

    Article  Google Scholar 

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Funding

This work was funded by Sichuan Science and Technology Plan Key Research and Development Program (Grant No. 2018SZ0339), National Natural Science Foundation of China (Grant No. 41801391), State Key Laboratory of Geodesy and Earth’s Dynamics Open fund (Grant No. SKLGED2018-5-3-E), The Funds for Creative Research Groups of China (Grant No. 41521002) and partially supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI), and European Funds for Regional Development (FEDER), under project TIN2014-55413-C2-2-P and by the Spanish Ministry of Education, Culture and Sport, under project PRX17/00439. This work was also supported by the National Environment Research Council (NERC) through the Centre for the Observation and Modeling of Earthquakes, Volcanoes and Tectonics (COMET, ref.: come30001), the LiCS project (ref. NE/K010794/1), the ESA-MOST DRAGON-4 project (ref. 32244), and the Hunan Province Key Laboratory of Coal Resources Clean-Utilization and Mine Environment Protection, Hunan University of Science and Technology (Ref. E21608).

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Authors and Affiliations

  1. State Key Laboratory of Geohazard Prevention and Geoenviroment Protection, Chengdu University of Technology, Chengdu, 610059, China

    Keren Dai, Qiang Xu, Xuanmei Fan, Xiujun Dong & Weile Li

  2. Chinese Academy of Sciences, State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Wuhan, 430077, China

    Keren Dai & Zhiwei Zhou

  3. College of Earth Sciences, Chengdu University of Technology, Chengdu, 610059, China

    Keren Dai, Jisong Gou & Peilian Ran

  4. COMET, School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

    Zhenhong Li

  5. Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Alicante, P.O. Box 99, 03080, Alicante, Spain

    Roberto Tomás

Authors
  1. Keren Dai

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  2. Qiang Xu

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  3. Zhenhong Li

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  4. Roberto Tomás

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  5. Xuanmei Fan

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  6. Xiujun Dong

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  7. Weile Li

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  8. Zhiwei Zhou

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  9. Jisong Gou

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  10. Peilian Ran

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Corresponding author

Correspondence toQiang Xu.

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Dai, K., Xu, Q., Li, Z.et al. Post-disaster assessment of 2017 catastrophic Xinmo landslide (China) by spaceborne SAR interferometry.Landslides16, 1189–1199 (2019). https://doi.org/10.1007/s10346-019-01152-4

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