Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Geostatistical Integration of Different Sources of Elevation and its Effect on Landslide Hazard Mapping

  • Published : 2008.10.31
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Abstract

The objective of this paper is to compare the prediction performances of different landslide hazard maps based on topographic data stemming from different sources of elevation. The geostatistical framework of kriging, which can properly integrate spatial data with different accuracy, is applied for generating more reliable elevation estimates from both sparse elevation spot heights and exhaustive ASTER-based elevation values. A case study from Boeun, Korea illustrates that the integration of elevation and slope maps derived from different data yielded different prediction performances for landslide hazard mapping. The landslide hazard map constructed by using the elevation and the associated slope maps based on geostatistical integration of spot heights and ASTER-based elevation resulted in the best prediction performance. Landslide hazard mapping using elevation and slope maps derived from the interpolation of only sparse spot heights showed the worst prediction performance.

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References

  1. Chung, C. F., 2006. Using likelihood ratio functions for modeling the conditional probability of occurrence of future landslides for risk assessment, Computers & Geosciences, 32(8): 1052-1068 https://doi.org/10.1016/j.cageo.2006.02.003
  2. Chung, C. F., and A. G. Fabbri, 1999. Probabilistic prediction models for landslide hazard mapping, Photogrammetric Engineering & Remote Sensing, 65(12): 1389-1399
  3. Deutsch, C. V. and A. G. Journel, 1998. GSLIB: Geostatistical Software Library and User's Guide, 2nd Edition, Oxford University Press, New York, NY, USA
  4. Ercanoglu, M. and C. Gokceoglu, 2002. Assessment of landslide susceptibility for a landslideprone area (north of Yenice, NW Turkey) by fuzzy approach, Environmental Geology, 41: 720-730 https://doi.org/10.1007/s00254-001-0454-2
  5. Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY, USA
  6. Kyriakidis, P. C., A. M. Shortridge, and M. F. Goodchild, 1999. Geostatistics for conflation and accuracy assessment of digital elevation models, International Journal of Geographical Information Science, 13(7): 677-707 https://doi.org/10.1080/136588199241067
  7. Lee, S., J. H. Ryu, M. J. Lee, and J. S. Won, 2006. The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea, Mathematical Geology, 38(2): 199-220 https://doi.org/10.1007/s11004-005-9012-x
  8. Lee, S., H. J. Oh, N. W. Park, and S. S. Lee, 2008. Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping using GIS, Geomorphology, under revision
  9. Park, N. W. and K. H. Chi, 2008. Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model, International Journal of Remote Sensing, 29(1): 247-264 https://doi.org/10.1080/01431160701227661