[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.7780/kjrs.2008.24.5.453

Geostatistical Integration of Different Sources of Elevation and its Effect on Landslide Hazard Mapping

Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Kyriakidis, Phaedon C. (Department of Geography, University of California Santa Barbara)

Publication Information

Korean Journal of Remote Sensing / v.24, no.5, 2008 , pp. 453-462 More about this Journal

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.

Keywords

Elevation; Landslide hazard; Geostatistics;

Citations & Related Records

Reference

1	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 DOI ScienceOn
2	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 DOI
3	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
4	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 DOI ScienceOn
5	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 DOI
6	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
7	Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY, USA
8	Chung, C. F., and A. G. Fabbri, 1999. Probabilistic prediction models for landslide hazard mapping, Photogrammetric Engineering & Remote Sensing, 65(12): 1389-1399
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 DOI ScienceOn