Open image in new window Landslides Susceptibility Mapping in Oklahoma State Using GIS-Based Weighted Linear Combination Method

  • Xiaogang He
  • Yang Hong
  • Xiaodi Yu
  • Amy B. Cerato
  • Xinhua Zhang
  • Marko Komac
Conference paper

Abstract

Oklahoma experiences approximately 20 reported landslides per year, which cause damage to transportation corridors and infrastructure. A refined regional hazard map has the potential ability to assist the state with detecting landslide hotspots and prevent future transportation corridor blockages. Combining the Geographic Information System (GIS) and high resolution satellite images, a first-cut landslide susceptibility map over the state of Oklahoma has been generated through the following two steps. The top four key landslide-controlling factors, including slope, soil texture type, land cover and elevation, were derived from a comprehensive geospatial database. After that, GIS-based weighted linear combination (WLC) method was utilized to assign the factor weight for each controlling parameter to generate the landslide susceptibility values, which are classified into five categories. Our study indicates that the entire state can be divided into five levels of susceptibility, namely very low (7.80 %), low (38.32 %), medium (45.15 %), high (8.09 %) and very high (0.64 %). These results match the historical landslide risk map well, especially in the south eastern and north western corner of the state. Further comparison with the landslide inventory data provided by the Oklahoma Department of Transportation (ODOT) and U.S. Geological Survey (USGS) shows that, 17 out of 19 (ODOT) and 60 out of 86 (USGS) events are located in category “high” or “very high”, which demonstrates the ability of WLC method in predicting landslide prone areas.

Keywords

Landslide susceptibility Remote Sensing GIS Oklahoma 

References

  1. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277CrossRefGoogle Scholar
  2. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecure, Japan. Landslide 1:73–81CrossRefGoogle Scholar
  3. Bathrellos G, Kavilas D, Skilodimou H (2009) GIS-based landslide susceptibility mapping models applied to natural and urban planning in Trikala, Central Greece. Estudios Geologicos 65:49–65CrossRefGoogle Scholar
  4. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16:427–445CrossRefGoogle Scholar
  5. Cerato A, Nevels J (2007) Shallow landslide analysis: McCurtain County, Oklahoma. In: Proceedings of the 1st North American landslide conference: landslides and society. Integrated Science, Engineering, Management, and Mitigation, June 3–8. Vail, CO, pp 21–30Google Scholar
  6. Cerato A, Oleski R, Puklin C (2006) Case study: compacted embankment landslide in Grady County, Oklahoma. In: Proceedings of the 40th annual symposium on engineering geology and geotechnical engineering. Landslides-investigation, analysis and mitigation. Utah State University, Logan, Utah, May 24–26Google Scholar
  7. Coe J, Godt J, Baum R, Bucknam R, Michael J (2004) Landslide susceptibility from topography in Guatemala. In: Lacerda WA, Ehrlich M, Fontura SAB, Sayao ASF (eds) Landslides: evaluation and stabilization. Taylor & Francis Group, London, pp 69–78Google Scholar
  8. Dai F, Lee C (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–238CrossRefGoogle Scholar
  9. Fabbri A, Chung C, Cendrero A, Remondo J (2003) Is prediction of future landslides possible with GIS? J Nat Hazards 30:487–499CrossRefGoogle Scholar
  10. Godt J, Baum R, Lu N (2009) Landsliding in partially saturated materials. Geophys Res Lett 36: L02403. doi: 10.1029/2008GL035996
  11. Hong Y, Adler R, Huffman G (2006) Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys Res Lett 33: L22402. doi: 10.1029/2006GL028010
  12. Hong Y, Adler R, Huffman G (2007) Use of satellite remote sensing data in the mapping of global landslide susceptibility. J Nat Hazards 43:245–256CrossRefGoogle Scholar
  13. Larsen M, Torres-Sanchez A (1998) The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico. Geomorphology 24:309–331CrossRefGoogle Scholar
  14. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113CrossRefGoogle Scholar
  15. Lu N, Godt J (2013) Hillslope hydrology and stability. Cambridge University Press, Cambridge, UK and New YorkCrossRefGoogle Scholar
  16. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996CrossRefGoogle Scholar
  17. Radbruch-Hall D, Colton R, Davies W, Luccitta I, Skipp B, Varnes D (1982) Landslide overview map of the conterminous United States. US Geological Survey Professional Paper 1183, 25 ppGoogle Scholar
  18. Saha A, Gupta R, Arora M (2002) GIS-based landslide hazard zonation in the Bagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 23:357–369CrossRefGoogle Scholar
  19. Sarkar S, Kanungo D (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogramm Eng Remote Sens 70:617–625CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaogang He
    • 1
  • Yang Hong
    • 1
  • Xiaodi Yu
    • 1
  • Amy B. Cerato
    • 2
  • Xinhua Zhang
    • 3
  • Marko Komac
    • 4
  1. 1.Hydrometeorology and Remote Sensing Lab, Department of Civil Engineering and Environmental ScienceUniversity of OklahomaNormanUSA
  2. 2.Department of Civil Engineering and Environmental ScienceUniversity of OklahomaNormanUSA
  3. 3.State Key Laboratory of Hydraulics and Mountain River EngineeringSichuan UniversityChengduChina
  4. 4.Geological Survey of SloveniaLjubljanaSlovenia

Personalised recommendations