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


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.


Landslide susceptibility Remote Sensing GIS Oklahoma 



This study was supported by “Real-time Monitoring of Slope Stability in Eastern Oklahoma” project funded by the Oklahoma Department of Transportation (ODOT) SPR 2241 and the bilateral project funded by Slovenian Research Agency (BI-US/12-13-048). The authors also acknowledge the partial open fund SKHL1310 support from the State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University.


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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

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