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 Su
  • Yong-gang Ge
  • Paolo Tarolli
  • Zheng-yin Yang
  • Chao Zeng
  • Zhen Zeng
Article

Abstract

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.

Keywords

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

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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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