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Landslide Stability Analysis and Prediction Modeling with Landslide Occurrences on KOMPSAT EOC Imagery
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  • Landslide Stability Analysis and Prediction Modeling with Landslide Occurrences on KOMPSAT EOC Imagery
  • Landslide Stability Analysis and Prediction Modeling with Landslide Occurrences on KOMPSAT EOC Imagery
저자명
Chi. Kwang-Hoon,Lee. Ki-Won,Park. No-Wook
간행물명
大韓遠隔探査學會誌
권/호정보
2002년|18권 1호|pp.1-12 (12 pages)
발행정보
대한원격탐사학회
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

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.