대한원격탐사학회지 (Korean Journal of Remote Sensing)

  • 제18권1호
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  • Pages.1-12
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  • 2002
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  • 1225-6161(pISSN)
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  • 2287-9307(eISSN)
대한원격탐사학회 (Korean Society of Remote Sensing)
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Landslide Stability Analysis and Prediction Modeling with Landslide Occurrences on KOMPSAT EOC Imagery

  • Chi, Kwang-Hoon (National Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Lee, Ki-Won (Department of Information Systems(GIS/RS), Information and Computer Science Div., Hansung University) ;
  • Park, No-Wook (National Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
  • 발행 : 2002.02.01
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초록

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

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