, Volume 16, Issue 6, pp 1189–1199 | Cite as

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

  • Keren Dai
  • Qiang XuEmail author
  • Zhenhong Li
  • Roberto Tomás
  • Xuanmei Fan
  • Xiujun Dong
  • Weile Li
  • Zhiwei Zhou
  • Jisong Gou
  • Peilian Ran
Technical Note


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.


Post-disaster assessment Xinmo landslide InSAR TanDEM-X Sentinel-1 


Funding information

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).


  1. 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. CrossRefGoogle Scholar
  2. 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.
  3. 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):29–42CrossRefGoogle Scholar
  4. 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 8:468CrossRefGoogle Scholar
  5. 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):267–283CrossRefGoogle Scholar
  6. Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng Geol 88(3-4):173–199Google Scholar
  7. COMET (2017) Sentinel-1 satellites reveal pre-event movements and source areas of the maoxian landslides, china. Accessed 18 August 2018
  8. Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64:65–87. CrossRefGoogle Scholar
  9. 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. CrossRefGoogle Scholar
  10. 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. CrossRefGoogle Scholar
  11. 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), 2113–2128Google Scholar
  12. DLR (2010) TanDEM-X - A New High Resolution Interferometric SAR Mission. Accessed 18 August 2018
  13. 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. CrossRefGoogle Scholar
  14. 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 9:353CrossRefGoogle Scholar
  15. eoPortal Directory (2014) TDX. Accessed 18 August 2018
  16. ESA (2014) Sentinel-1. Accessed 18 August 2018
  17. 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 14:1677–1688CrossRefGoogle Scholar
  18. 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 171:58–68CrossRefGoogle Scholar
  19. 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 1–18Google Scholar
  20. 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):32CrossRefGoogle Scholar
  21. Ferretti A, Prati C, Rocca F (1999) Multibaseline InSAR DEM reconstruction: the wavelet approach. IEEE Trans Geosci Remote Sens 37(2):705–715CrossRefGoogle Scholar
  22. 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. CrossRefGoogle Scholar
  23. 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 1–18Google Scholar
  24. Gao X, Liu Y, Li T, Wu D (2017) High precision dem generation algorithm based on Insar multi-look iteration. Remote Sens 9:741CrossRefGoogle Scholar
  25. 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. CrossRefGoogle Scholar
  26. 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. CrossRefGoogle Scholar
  27. Hanssen RF (2001) Radar interferometry: data interpretation and error analysis (vol. 2). Springer Science & Business Media, BerlinGoogle Scholar
  28. 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. CrossRefGoogle Scholar
  29. Hungr O, Evans SG (2004) Entrainment of debris in rock avalanches: an analysis of a long run-out mechanism. GSA Bull 116:1240–1252. CrossRefGoogle Scholar
  30. 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. CrossRefGoogle Scholar
  31. 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 204:518–531CrossRefGoogle Scholar
  32. 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):97–116CrossRefGoogle Scholar
  33. 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):39CrossRefGoogle Scholar
  34. 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):5822–5842CrossRefGoogle Scholar
  35. 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 130:108–121CrossRefGoogle Scholar
  36. 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 14:1701–1711CrossRefGoogle Scholar
  37. 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. CrossRefGoogle Scholar
  38. 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. CrossRefGoogle Scholar
  39. 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 8:874CrossRefGoogle Scholar
  40. 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):14576–14596CrossRefGoogle Scholar
  41. 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. CrossRefGoogle Scholar
  42. 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. CrossRefGoogle Scholar
  43. 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. CrossRefGoogle Scholar
  44. 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. CrossRefGoogle Scholar
  45. 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. Accessed 18 August 2018
  46. 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. CrossRefGoogle Scholar
  47. 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 10:2179–2190CrossRefGoogle Scholar
  48. 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 217:101–110CrossRefGoogle Scholar
  49. 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. CrossRefGoogle Scholar
  50. 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. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Geohazard Prevention and Geoenviroment ProtectionChengdu University of TechnologyChengduChina
  2. 2.Chinese Academy of Sciences, State Key Laboratory of Geodesy and Earth’s DynamicsInstitute of Geodesy and GeophysicsWuhanChina
  3. 3.College of Earth SciencesChengdu University of TechnologyChengduChina
  4. 4.COMET, School of EngineeringNewcastle UniversityNewcastle upon TyneUK
  5. 5.Departamento de Ingeniería Civil, Escuela Politécnica SuperiorUniversidad de AlicanteAlicanteSpain

Personalised recommendations