• Economic and Environmental Geology
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• v.34 no.2
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• pp.205-215
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• 2001

The purpose of this study is to analyze the landslide susceptibility, containing the process, which reveals spatial relationships between landslides and geospatial data sets, which occurred in Janghung area in 1998. Landslide locations were detected from remotely sensed image and field survey and topography, soil, forest, and land use data sets were constructed as a spatial database in GIS. As the landslide occurrence factors, slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of wood and land use were used. To extract the relationship between landslides and geospatial database, likelihood ratio was calculated and compared with the result of Yongin area. Also, the landslide susceptibility index was calculated by summation of the likelihood ratio and the landslide susceptibility map was generated using the index. As a result, it is expected that spatial relationships between landslides and geospatial database is helpful to explain the characteristics of lilndslide and the landslide susceptibility map is used to reduce associated hazards, and to plan land use and construction.

• Economic and Environmental Geology
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• v.37 no.2
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• pp.207-223
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• 2004

The purpose of this study is to reveal spatial relationships between landslide and geospatial data set, to map the landslide susceptibility using the relationship and to verify the landslide susceptibility using the landslide occurrence data in Boun area in 1998. Landslide locations were detected from aerial photography and field survey, and then topography, soil, forest, and land cover data set were constructed as a spatial database using GIS. Various spatial parameters were used as the landslide occurrence factors. They are slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil. type, age, diameter and density of wood, lithology, distance from lineament and land cover. To calculate the relationship between landslides and geospatial database, Bayesian probability methods, weight of evidence. were applied and the contrast value that is >$W^{+}$->$W^{-}$ were calculated. The landslide susceptibility index was calculated by summation of the contrast value and the landslide susceptibility maps were generated using the index. The landslide susceptibility map can be used to reduce associated hazards, and to plan land cover and construction.

• Spatial Information Research
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• v.20 no.3
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• pp.57-66
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• 2012

Gangwon-do has been suffering extensive landslide dam age, because its geography consists mainly of mountains. Analyzing the related factors is crucial for landslide prediction. We digitized the landslide and non-landslide spots on an aerial photo obtained right after a disaster in Inje, Gangwon-do. Three landslide factors-topographic, forest type, and soil factors-w ere statistically analyzed through GIS overlap analysis between topographic map, forest type map, and soil map. The analysis showed that landslides occurred mainly between the inclination of $20^{\circ}$ and $35^{\circ}$, and needleleaf tree area is more vulnerable to a landslide. About soil properties, an area with shallow effective soil depth and parent material of acidic rock has a greater chance of landslide.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.116-127
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• 2011

This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.281-292
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• 2014

This study examined the accuracy that produced using various types and combinations of landslide-related factors from landslide susceptibility index maps. A database of landslide-related factors was adopted by the landslide locations that obtained from aerial photographs, and the topographic factors that derived from airborne LiDAR observations and digital maps, and various soil, forest, and land cover. Landslide susceptibility index maps were calculated by logistic regression and frequency ratio from the landslide susceptibility index. The correlation between airborne LiDAR data and digital map was shown strong similarities with one another. Landslide susceptibility index maps indicated the existence of a strong correlation and high prediction accuracy, especially when the frequency ratio and airborne LiDAR were used. Therefore, we concluded that the Airborne LiDAR will contribute to the development of effective landslide prediction methods and damage reduction measures.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.585-596
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• 2022

This study determined major factors influencing landslide occurrence along a forest road near Sangsan village, Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea. Within a 2 km radius of the study area, landslides occur intensively during periods of heavy rainfall (August 2020). This makes study of the area advantageous, as it allows examination of the influence of only geological and tomographic factors while excluding the effects of rainfall and vegetation. Data for 82 locations (37 experiencing landslides and 45 not) were obtained from geological surveys, laboratory tests, and geo-spatial analysis. After some data preprocessing (e.g., error filtering, minimum-maximum normalization, and multicollinearity), structural equation model (SEM) and logistic regression (LR) analyses were conducted. These showed the regolith thickness, porosity, and saturated unit weight to be the factors most influential of landslide risk in the study area. The sums of the influence magnitudes of these factors are 71% in SEM and 83% in LR.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.507-514
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• 2018

TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.33-39
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• 2008

Typhoon Lusa in 2002 and Typhoon Maemi in 2003 caused the worst damage of landslide and debris flow to Gangwon-do. This damage includes severe damage in riverside road. The damage register indicates that this damage is concentrated on mountain areas in Gangwon-do. In recent years, the studies on GIS application to predicting landslide and debris flow have been progressing actively. Landslide risk map managed by The Forest Service is the representative one. In this study, we generated landslide and debris flow hazard maps using statistical analysis and deterministic analysis in Gangnung area where Typhoons caused severe damage to riverside roads. We built damage point GIS DB from damage registers of National Road Maintenance Agency and field survey, and verified accuracy of landslide and debris flow hazard maps using GIS methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.57-70
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• 2012

The aim of this study is to analyse the landslide susceptibility and the future hazard in Inje, Korea using probability rainfalls and artificial neural network (ANN) environment based on geographic information system (GIS). Data for rainfall probability, topography, and geology were collected, processed, and compiled in a spatial database using GIS. Deokjeok-ri that had experienced 694 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 1-day rainfall of 202 mm or 3-day cumulative rainfalls of 449 mm.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.39-46
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• 2007

Most areas in Kangwon Province are mountainous and vulnerable to landslide due to the rainy season in summer and the localized torrential downpour triggered by abnormal climate. In particular, the rainfall is one of direct reasons for landslide. In accordance with the analysis of the relevance between the landslide areas and the accumulated rainfall for four months, there are severe damages of landslide to the areas having more than 1,100 mm of rainfall during three(3) months. Further, it indicates that the more the accumulated rainfall is the greater the size of landslide. These analyses show that the rainfall causes the possible and potential landslide in the vulnerable areas. And also, it means that there exist strong possibilities of landslide even in the areas of lower vulnerability if the amount of rainfall is above certain standard level. Accordingly, in this study we stored the GIS database on the causes and factors of landslide in the southern parts of Kangwon province and conducted simulations on the change of distribution of vulnerable areas by varying the rainfall conditions and by using the evaluation data of landslide vulnerability. As such a result, we found that the landslide could potentially occur if the amount of rainfall is 200 mm and more.