Landslides Susceptibility Mapping in Oklahoma State Using GIS-Based Weighted Linear Combination Method

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.

[1]  Yang Hong,et al.  Use of satellite remote sensing data in the mapping of global landslide susceptibility , 2007 .

[2]  Saro Lee,et al.  Statistical analysis of landslide susceptibility at Yongin, Korea , 2001 .

[3]  Yang Hong,et al.  Evaluation of the potential of NASA multi‐satellite precipitation analysis in global landslide hazard assessment , 2006 .

[4]  D. P. Kanungo,et al.  An Integrated Approach for Landslide Susceptibility Mapping Using Remote Sensing and GIS , 2004 .

[5]  M. Arora,et al.  GIS-based Landslide Hazard Zonation in the Bhagirathi (Ganga) Valley, Himalayas , 2002 .

[6]  L. Ayalew,et al.  Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan , 2004 .

[7]  J. Coe,et al.  Landslide susceptibility from topography in Guatemala , 2004 .

[8]  Chang-Jo Chung,et al.  Is Prediction of Future Landslides Possible with a GIS? , 2003 .

[9]  Roger B. Colton,et al.  Landslide overview map of the conterminous United States , 1982 .

[10]  B. Pradhan,et al.  Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran , 2012, Natural Hazards.

[11]  W. Lacerda,et al.  Landslides : evaluation and stabilization , 2004 .

[12]  P. Reichenbach,et al.  GIS techniques and statistical models in evaluating landslide hazard , 1991 .

[13]  Ning Lu,et al.  Landsliding in partially saturated materials , 2009 .

[14]  Mapas de susceptibilidad de deslizamientos basados en GIS aplicados a la planificación natural y urbanística en Trikala, Grecia Central , 2009 .

[15]  R. Anbalagan,et al.  Landslide hazard evaluation and zonation mapping in mountainous terrain , 1992 .

[16]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[17]  J. Godt,et al.  Hillslope Hydrology and Stability , 2013 .

[18]  Matthew C. Larsen,et al.  The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico , 1998 .