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, flo...
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.
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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가설 설정
2. (a) Landslide occurrences superimposed on the KOMPSAT EOC imagery, (b) Sink-removed DEM-based 3D View of the case study area in meter unit: Boeun region, Korea: Coverage (1270 x 722) with cell unit of 10 meters, in the rectangle region of left top: 127° 38' 53〃 E, 36° 29’ 58〃 N and right bottom: 127° 47, 21〃 E, 36° 26' 01 ” N, (c) 10m-interval Topographic map of the study area coverage with actual landslide scarped zones as point feature, and (d) Stream network model in the study area with landslide point features.
제안 방법
As for the landslide locations, the aerial photographs taken in 1996 and 1999, and KOMPSAT EOC imagery were firstly used to detect them, and the locations were verified in the field, done in site investigation after landslide events. While, those landslide occurrences were recognized on the KOMPSAT EOC imagery in Fig.
(1998). Furthermore, for testing on the space-robustness of landslide prediction models, we exemplify whether and to what extent a prediction can be extended, in space, to neighboring areas with similar geomorphology or geology using the validation and stability analysis of the model, using spatial data sets from Boeun, Korea (Fig. 1).
In this paper, these two tasks were independently processed for preparation of unbiased criteria, and then results of those were qualitatively compared. As results, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, results in the form of land stability map by landslide prediction model is not fully matched due to heuristic scheme within prediction approach.
In this study, a case study for landslide stability analysis under GIS environment and landslide prediction modeling was carried out using data sets of Boeun area, Korea. After extraction of hydrological factors and topographically based indices of hydrological landslide characteristics, stability index mapping approach and the followed S-A plot were also performed with landslide point features.
대상 데이터
The Boeun area, Korea, which had suffered from lots of landslide damages due to the heavy rain in summer, 1998, is selected for the case study area. The input data (termed "causal factor”)for landslide prediction modeling consists of several layers of map information (Table 1).
While, the lithological map is obtained from a geological map of KIGAM. These data sets are built on a cell-based database in GIS environment, and the whole study area consists of 1,270X722 pixels, covering approximately 92km2. Each pixel on these data is referenced to a 10m by 10m area on the ground for data integration for landslide hazard mapping.
이론/모형
Among many prediction models for landslides, in this study, we have implemented a landslide prediction model based on the likelihood ratio model proposed by Chung and Fabbri (1998). The likelihood ratio model employed in this study is described briefly here.
In this study, the landslide analysis with hydrological variables and the landslide prediction modeling are performed using Arcinfo and ArcView with hydrological application programs under Spatial Analyst, proposed and implemented by Tarboton (1997) and Pack et al. (1998). Furthermore, for testing on the space-robustness of landslide prediction models, we exemplify whether and to what extent a prediction can be extended, in space, to neighboring areas with similar geomorphology or geology using the validation and stability analysis of the model, using spatial data sets from Boeun, Korea (Fig.
후속연구
As results, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, results in the form of land stability map by landslide prediction model is not fully matched due to heuristic scheme within prediction approach. Therefore, it is expected that the results 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. Also, this kind of approach will be further utilized to delineate hydrological characteristics of large-scale watersheds, even to small-scale ones, related to landslide occurrence.
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