Geosciences Journal

, 8:51 | Cite as

The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea



The authors have evaluated the effect of spatial resolution on the accuracy of landslide susceptibility mapping. For this purpose, landslide locations were identified from the interpretation of aerial photographs and field surveys in the Boun region of Korea. Topography, soil, forest, geological, lineament and landuse data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. The 15 factors that influenced landslide occurrence were extracted and calculated from the spatial database at 5, 10, 30, 100 and 200 m spatial resolution. Hazardous landslide areas were analyzed and mapped using the landslide-occurrence factors by employing a probability models frequency ratio for the five spatial resolutions. The results of the analysis were verified using the landslide location data and area under success rate curve. The spatial resolutions of 5, 10 and 30 m showed similar results (the normalized area values 0.97, 1.00 and 0.92, respectively), but the 100 and 200 m spatial resolutions showed less well-verified data (the normalized area values 0.48, and 0.00, respectively). Because the scale of the input data was 1∶5,000–1∶50,000, the 5, 10 and 30 m spatial resolutions had a similar accuracy, but the 100 and 200 m spatial resolutions had a lower accuracy. From this, we conclude that spatial resolution has an effect on the accuracy of landslide susceptibility, as it is dependent on the input map. At least, less than 30 m resolution is need for landslide analysis in Korea where most of map scale is in the range 1∶5,000–1∶50,000.

Key words

landslide frequency ratio GIS resolution ventification Korea 


  1. Bonham-Carter, G.F., 1994, Geographic Information Systems for geoscientists, modeling with GIS. Pergamon Press, Oxford, 398p.Google Scholar
  2. Chung, C.F. and Fabbri, A.G., 1999, Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Sensing, 65, 1389–1399.Google Scholar
  3. Clerici, A., Perego, S., Tellini, C. and Vescovi, P., 2002, A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48, 349–364.CrossRefGoogle Scholar
  4. Dai, F.C., Lee, C.F., Li, J. and Xu, Z.W., 2001, Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40, 381–391.CrossRefGoogle Scholar
  5. Donati, L. and Turrini, M.C., 2002, An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, 63, 277–289.CrossRefGoogle Scholar
  6. Guzzetti, F., Carrarr, A., Cardinali, M. and Reichenbach, P., 1999, Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31, 181–216.CrossRefGoogle Scholar
  7. Larsen, M. and Torres-Sanchez, A., 1998, The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico. Geomorphology, 24, 309–331.CrossRefGoogle Scholar
  8. Lee, S. and Choi, U., 2003, Development of GIS-based geological hazard information system and its application for landslide analysis in Korea. Geosciences Journal, 7, 243–252.CrossRefGoogle Scholar
  9. Lee, S and Min, K., 2001, Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40, 1095–1113.CrossRefGoogle Scholar
  10. Lee, S., Choi, J. and Min K., 2002a, Landslide susceptibility analysis and verification using the Bayesian probability model. Environmental Geology, 43, 120–131.CrossRefGoogle Scholar
  11. Lee, S., Chwae, U. and Min K., 2002b. Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea. Geomorphology, 46, 149–162.CrossRefGoogle Scholar
  12. Lee, S., Ryu J., Lee, M. and Won, J., 2003a, Landslide susceptibility analysis using artificial neural network at Boun, Korea. Environmental Geology, 44, 820–833.CrossRefGoogle Scholar
  13. Lee, S., Ryu, J., Min, K. and Won, J., 2003b, Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms, 28, 1361–1376.CrossRefGoogle Scholar
  14. Lee, S., Ryu, J., Won, J. and Park, H., 2003c. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71, 289–302.CrossRefGoogle Scholar
  15. Mandy, L.G., Andrew, M.W., Richard, A. and Stephan, G.C., 2001, Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology, 37, 149–165.CrossRefGoogle Scholar
  16. Randall, W.J., Edwin, L.H. and John, A.M., 2000, A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology, 58, 271–289.CrossRefGoogle Scholar
  17. Rautelai, P. and Lakheraza, R.C., 2000, Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India). International Journal of Applied Earth Observation and Geoinformation, 2, 153–160.CrossRefGoogle Scholar
  18. Refice, A. and Capolongo D., 2002, Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Computers & Geosciences, 28, 735–749.CrossRefGoogle Scholar
  19. Turrini, M.C. and Visintainer, P., 1998, Proposal of a method to define areas of landslide hazard and application to an area of the Dolomites, Italy. Engineering Geology, 50, 255–265.CrossRefGoogle Scholar

Copyright information

© Springer 2004

Authors and Affiliations

  1. 1.Geoscience Information CenterKorea Institute of Geoscience & Mineral Resources (KIGAM)DaejeonKorea
  2. 2.Department of Earth System ScienceYonsei UniversitySeoulKorea
  3. 3.Department of Geoinformation EngineeringSejong UniversitySeoulKorea

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