Journal of Mountain Science

, Volume 14, Issue 9, pp 1677–1688 | Cite as

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

  • Jian-rong Fan
  • Xi-yu Zhang
  • Feng-huan SuEmail author
  • Yong-gang Ge
  • Paolo Tarolli
  • Zheng-yin Yang
  • Chao Zeng
  • Zhen Zeng


At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture (Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle (UAV), and a digital elevation model (DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include QuickBird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events. Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.


Xinmo Landslide Geological disaster Remote Sensing Unmanned aerial vehicle (UAV) Digital elevation model (DEM) Satellite data 



This research is funded by the National Key Technologies R&D Program of China (Grants No. 2017YFC0505104) and the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation of China (Grants No. DM2016SC09). The Sichuan Bureau of Surveying, Mapping and Geo-information (SBSMG) conducted the UAV survey and provided the terrain data and remote sensing images used in this study.


  1. Calò F, Ardizzone F, Castaldo R, et al. (2014) Enhanced landslide investigations through advanced DInSAR techniques: The Ivancich case study, Assisi, Italy. Remote Sensing of Environment 142: 69–82. Scholar
  2. Cui P, Chen XQ, Zhu YY, et al. (2011) The Wenchuan earthquake (May 12, 2008), Sichuan province, China, and resulting geohazards. Natural Hazards 56(1): 19–36. Scholar
  3. Cui P, Zhu YY, Han YS, et al. (2009) The 12 May Wenchuan earthquake-induced landslide lakes: distribution and preliminary risk evaluation. Landslides 6(3): 209–223. Scholar
  4. DeLong SB, Prentice CS, Hilley GE, et al. (2012) Multitemporal ALSM change detection, sediment delivery, and process mapping at an active earthflow. Earth Surface Processes and Landforms 37(3): 262–272. Scholar
  5. Fan JR, Chen JX, Tian BW, et al. (2010) Rapid assessment of secondary disasters induced by the Wenchuan Earthquake. Computing in science & engineering 12(1): 10–19. Scholar
  6. Fan JR, Li XZ, Guo FF, et al. (2011) Empirical-statistical models based on remote sensing for estimating the volume of landslides induced by the Wenchuan earthquake. Journal of Mountain Science 8(5): 711–717. Scholar
  7. Fan X, van Westen CJ, Korup O, et al. (2012) Transient water and sediment storage of the decaying landslide dams induced by the 2008 Wenchuan earthquake, China. Geomorphology 171: 58–68. Scholar
  8. Fan YD, Wu W, Wang W, et al. (2016) Research progress of disaster remote sensing in China. Journal of Remote Sensing 20(05): 1170–1184. (In Chinese)Google Scholar
  9. Fang X, Pomeroy JW, Westbrook CJ, et al. (2010) Prediction of snowmelt derived streamflow in a wetland dominated prairie basin. Hydrology and Earth System Sciences 14(6): 991–1006. Scholar
  10. Fang YP, Fan J, Shen MY, et al. (2014) Sensitivity of livelihood strategy to livelihood capital in mountain areas: Empirical analysis based on different settlements in the upper reaches of the Minjiang River, China. Ecological Indicators 38: 225–235. Scholar
  11. Fiorucci F, Cardinali M, Carlà R, et al. (2011) Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology 129(1): 59–70. Scholar
  12. Galli M, Ardizzone F, Cardinali M, et al. (2008) Comparing landslide inventory maps. Geomorphology 94(3): 268–289. Scholar
  13. Guthrie RH, Evans SG. (2004) Analysis of landslide frequencies and characteristics in a natural system, coastal British Columbia. Earth Surface Processes and Landforms 29(11): 1321–1339. Scholar
  14. Guzzetti F, Ardizzone F, Cardinali M, et al. (2009) Landslide volumes and landslide mobilization rates in Umbria, central Italy. Earth and Planetary Science Letters 279(3): 222–229. Scholar
  15. Guzzetti F, Carrara A, Cardinali M, et al. (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1): 181–216. Scholar
  16. Imaizumi F, Sidle RC, Kamei R. (2008) Effects of forest harvesting on the occurrence of landslides and debris flows in steep terrain of central Japan. Earth Surface Processes and Landforms 33(6): 827–840. Scholar
  17. Innes JL (1983) Lichenometric dating of debris-flow deposits in the Scottish Highlands. Earth Surface Processes and Landforms 8(6): 579–588. Scholar
  18. Jaboyedoff M, Oppikofer T, Abellán A, et al. (2012) Use of LIDAR in landslide investigations: a review. Natural Hazards 61(1): 5–28. Scholar
  19. Koi T, Hotta N, Ishigaki I, et al. (2008) Prolonged impact of earthquake-induced landslides on sediment yield in a mountain watershed: the Tanzawa region, Japan. Geomorphology 101(4): 692–702. Scholar
  20. Korup O (2005) Distribution of landslides in southwest New Zealand. Landslides 2(1): 43–51. Scholar
  21. Lin CW, Tseng CM, Tseng YH, et al. (2013) Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography. Journal of Asian Earth Sciences 62: 389–400. Scholar
  22. Lucieer A, Jong SM, Turner D. (2014) Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in Physical Geography 38(1): 97–116. Scholar
  23. Nex F, Remondino F (2014) UAV for 3D mapping applications: a review. Applied Geomatics 6(1): 1–15. Scholar
  24. Ouyang CJ, Zhao W, He SM, et al. (2017) Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian County, China. Journal of Mountain Science 14(9). Scholar
  25. Passalacqua P, Hillier JH, Tarolli P. (2014) Innovative analysis and use of high resolution DTMs for understanding Earthsurface processes. Earth Surface Processes and Landforms 39(10): 1400–1403. Scholar
  26. Prosdocimi M, Calligaro S, Sofia G, et al. (2015) Bank erosion in agricultural drainage networks: new challenges from structurefrom-motion photogrammetry for post-event analysis. Earth Surface Processes and Landforms 40(14): 1891–1906. Scholar
  27. Rice RM, Crobett ES, Bailey RG (1969) Soil slips related to vegetation, topography, and soil in southern California. Water Resources Research 5(3): 647–659. Scholar
  28. Santangelo M, Marchesini I, Cardinali M, et al. (2015) A method for the assessment of the influence of bedding on landslide abundance and types. Landslides 12(2): 295–309. Scholar
  29. Schlögel R, Doubre C, Malet JP, et al. (2015) Landslide deformation monitoring with ALOS/PALSAR imagery: a DInSAR geomorphological interpretation method. Geomorphology 231: 314–330. Scholar
  30. Siebert S, Teizer J (2014) Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Automation in Construction 41: 1–14. Scholar
  31. Smith MW, Vericat D (2015) From experimental plots to experimental landscapes: topography, erosion and deposition in sub-humid badlands from structure-from-motion photogrammetry. Earth Surface Processes and Landforms 40(12): 1656–1671. Scholar
  32. Stumpf A, Malet JP, Allemand P, et al. (2015) Ground-based multi-view photogrammetry for the monitoring of landslide deformation and erosion. Geomorphology 231: 130–145. Scholar
  33. Su LJ, Hu KH, Zhang WF, et al. (2017) Characteristics and triggering mechanism of Xinmo landslide on 24 June 2017 in Sichuan, China. Journal of Mountain Science 14(9). Scholar
  34. Tachikawa T, Hato M, Kaku M, et al. (2011) Characteristics of ASTER GDEM version 2. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), ancouver, BC, 2011, pp. 3657–3660. Scholar
  35. Tang C, Rengers N, Van Asch TWJ, et al. (2011) Triggering conditions and depositional characteristics of a disastrous debris flow event in Zhouqu city, Gansu Province, northwestern China. Natural Hazards and Earth System Sciences 11(11): 2903–2912. Scholar
  36. Tarolli P. (2014) High-resolution topography for understanding Earth surface processes: Opportunities and challenges. Geomorphology 216: 295–312. Scholar
  37. Tarolli P, Sofia G, Dalla Fontana G (2012) Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion. Natural Hazards 61(1): 65–83. Scholar
  38. Tseng CM, Chang KJ, Tarolli P. (2017) The Sediment Production and Transportation in a Mountainous Reservoir Watershed, Southern Taiwan. In: Mikoš M, Vilímek V, Yin Y, Sassa K (eds) Advancing Culture of Living with Landslides. WLF 2017. 291-299. aSpringer, Cham. Scholar
  39. Tseng CM, Lin CW, Dalla Fontana G, Tarolli P. (2015) The topographic signature of a major typhoon. Earth Surface Processes and Landforms 40: 1129–1136. Scholar
  40. Tseng CM, Lin CW, Stark CP, et al. (2013) Application of a multitemporal, LiDAR-derived, digital terrain model in a landslidevolume estimation. Earth Surface Processes and Landforms 38(13): 1587–1601. Scholar
  41. Turner D, Lucieer A, De Jong SM (2015) Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sensing 7(2): 1736–1757. Scholar
  42. Wang F, Cheng Q, Highland L, et al. (2009) Preliminary investigation of some large landslides triggered by the 2008 Wenchuan earthquake, Sichuan Province, China. Landslides 6(1): 47–54. Scholar
  43. Wang K, Shen ZK (2011) Location and focal mechanism of the 1933 Diexi earthquake and its associated regional tectonics. Acta Seismologica Sinica 33(5): 557–567. (In Chinese)Google Scholar
  44. Wheaton JM, Brasington J, Darby SE, et al. (2010) Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surface Processes and Landforms 35(2): 136–156. Scholar
  45. Xu Q, Fan XM, Huang RQ, et al. (2009) Landslide dams triggered by the Wenchuan Earthquake, Sichuan Province, south west China. Bulletin of engineering geology and the environment 68(3): 373–386. Scholar
  46. Xu Q, Li WL (2010) Distribution of large scale landslides induced by the Wenchuan earthquake. Journal of Engineering Geology 18(6): 818–826. (In Chinese)Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Land, Environment, Agriculture and ForestryUniversity of Padova, AgripolisLegnaro (PD)Italy
  4. 4.Sichuan Remote Sensing InformationSurveying and Mapping InstituteChengduChina
  5. 5.Sichuan Engineering Research Center for Emergency Mapping & Disaster Reduction/Sichuan Geomatics CenterChengduChina

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