• Journal of Environmental Science International
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• v.18 no.11
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• pp.1189-1197
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• 2009

The purpose of this research was develop tailored landslide hazard assessment table (LHAT) in Gyeongsangbuk-do Province and propose building strategies on environmental information system to estimate landslide hazard area according to LHAT. To accomplish this purpose, this research investigated factors occurring landslide at 172 landslide occurred sites in 23 city and county of Gyeongsangbuk-do Province and analyzed what factors effected landslide occurrence quantity using the multiple statistics of quantification method(I). The results of analysis, factors affecting landslide occurrence quantity were shown in order of slope position, slope length, bedrock, aspect, forest age, slope form and slope. And results of the development of LHAT for predict mapping of landslide-susceptible area in Gyeongsangbuk-do Province, total score range was divided that 107 under is stable area(IV class), 107~176 is area with little susceptibility to landslide(III class), 177~246 is area with moderate susceptibility to landslide(II class), above 247 area with severe susceptibility to landslide(I class). According to LHAT, this research built landslide attribute database and made 7 digital theme maps at mountainous area located in Goryeong Gun, Seongju-Gun, and Kimcheon-City. The results of prediction on degree of landslide hazard using environmental information system, area with little susceptibility to landslide(III class) occupied 65.56% and severe susceptibility to landslide(I class) occupied 0.51%.

• Spatial Information Research
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• v.3 no.1
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• pp.81-90
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• 1995

Landslide data base system is necessitated to make a mid-long term master plan to prevent landslide from past landslide data and their statistical analysis. This paper emphasis on application of the efficient management system of GIS to reduce landslide disasters basis on the result of survey analysis of landslide problems. In this paper explains landslide data base system by the cause of landslide from past landslide data & application of GIS to it.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.71-76
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• 2003

The purpose of this study is the development, application and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence, For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.119-132
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• 2012

Recently, forest soil sediment disasters resulting from locally concentrated heavy rainfall have been occurring frequently in steep slope areas. The importance of landslide hazard map is emerging to analyze landslide vulnerable areas. This study was carried out to develop HyGIS-Landslide based on Hydro Geographic Information System in order to analyze forest soil sediment disaster in the mountainous river basin. HyGIS-Landslide is one of HyGIS components designed by considering the landslide hazard criteria of Korea Forest Service. It could show the distribution of landslide hazard areas after calculating the spatial data. In this system, the user could reset the weight of hazard criteria to reflect the regional characteristics of the landslide area. This component provided user interface that could make the latest spatial data available in the area of interest. HyGIS-Landslide could be applied to the surveyor's compensation score and it was possible to reflect the landslide risk exactly through it. Also, it could be used in topographic analysis techniques providing spatial analysis and making topographical parameters in HyGIS. Finally the accuracy could be acquired by calculating the landslide hazard grade map and landslide mapping data. This study applied HyGIS-Landslide at the Gangwon-do province sample site. As a result, HyGIS-Landslide could be applied to a decision support system searching for mountainous disaster risk region; it could be classified more effectively by re-weighting the landslide hazard criteria.

• Proceedings of the KSRS Conference
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• v.2
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• pp.840-843
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• 2006

The aim of this study was to detect landslide using satellite image and apply the landslide to probabilistic landslide-susceptibility mapping at Gangneung area, Korea using a Geographic Information System (GIS). Landslide locations were identified by change detection technique of KOMSAT-1 (Korea Multipurpose Satellite) EOC (Electro Optical Camera) images and checked in field. For landslide-susceptibility mapping, maps of the topography, geology, soil, forest, lineaments, and land cover were constructed from the spatial data sets. Then, the sixteen factors that influence landslide occurrence were extracted from the database. Using the factors and detected landslide, the relationships were calculated using frequency ratio, one of the probabilistic model. Then, landslide-susceptibility map was drawn using the frequency ration and finally, the map was verified by comparing with existing landslide locations. As the verification result, the prediction accuracy showed 86.76%. The landslide-susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1260-1265
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• 2009

Topographical features in South Korea is characterized that 70% of territory is composed of the mountains that can experience intense rainfall during storms in the summer and autumn. Efficient planning and management of landscape becomes utmost important since the cutting slopes in the mountain areas have been increased due to the limited construction areas for the roadway and residential development. This paper proposed an efficient way of slope management for the landslide risk by developing Web-GIS landslide risk management system. By deploying the Logistic Regression Analysis, the system could increase the prediction accuracy that the landslide disaster might be occurred. High resolution survey technology using GPS and Total-Station could extract the exact position and visual shape of the slopes that accurately describe the slope information. Through the proposed system, the prediction of damage areas from the landslide could also make it easy to efficiently identify the level of landslide risks via web-based user interface. It is expected that the proposed landslide risk management system can support the decision making framework during the identification, prediction, and management of the landslide risks.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.85-93
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• 2015

This paper mainly introduces the methods of extracting landslide information using ALOS(Advanced Land Observing Satellite) images and GIS(Geographical Information System) technology. In this study, we classified images using three different methods which are the unsupervised the supervised and the PCA(Principal Components Analysis) for extracting landslide information based on characteristics of ALOS image. From the image classification results, we found out that the quality of classified image extracted with PCA supervised method was superior than the other images extracted with the other methods. But the accuracy of landslide information extracted from this image classification was still very low as the pixels were very similar between the landslide and safety regions. It means that it is really difficult to distinguish those areas with an image classification method alone because the values of pixels between the landslide and other areas were similar, particularly in a region where the landslide and other areas coexist. To solve this problem, we used the LSM(Landslide Susceptibility Map) created with ArcView software through weighted overlay GIS method in the areas. Finally, the developed LSM was applied to the image classification process using the ALOS images. The accuracy of the extracted landslide information was improved after adopting the PCA and LSM methods. Finally, we found that the landslide region in the study area can be calculated and the accuracy can also be improved with the LSM and PCA image classification methods using GIS tools.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.363-366
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• 2004

The purpose of this study is to reveal the spatial relationship between landslides and geospatial data set and to map the landslide susceptibility using this relationship, and the landslide occurrence data in Kangneung area in 2002. Landslide locations were identified from interpretation of satellite images. Landslide susceptibility was analyzed using an artificial neural network. The weights of each factor were determined by the back-propagation training method. Susceptibility maps were constructed from Geographic Information System (GIS), The cases were overlaid and cross overlaid for landslide susceptibility mapping in each study area in Kangneung.

• Spatial Information Research
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• v.8 no.1
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• pp.141-153
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• 2000

The purpose of this study is to develop and apply spatial landslide information system using Geographic information system (GIS) in concerned with spatial data. Landslide locations detected from interpretation of aerial photo and field survey, and topographic , soil , forest , and geological maps of the study area, Yongin were collected and constructed into spatial database using GIS. As landslide occurrence factors, slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective thickness of soil were extracted from the soil database, and type, age, diameter and density of wood were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. In addition, landslide damageable objects such as building, road, rail and other facility were extracted from the topographic database. Landslide susceptibility was analyzed using the landslide occurrence factors by probability, logistic regression and neural network methods. The spatial landslide information system was developed to retrieve the constructed GIS database and landslide susceptibility . The system was developed using Arc View script language(Avenue), and consisted of pull-down and icon menus for easy use. Also, the constructed database can be retrieved through Internet World Wide Web (WWW) using Internet GIS technology.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.579-587
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• 2015

Landslides are major natural geological hazards that result in a large amount of property damage each year, with both direct and indirect costs. Many researchers have produced landslide susceptibility maps using various techniques over the last few decades. This paper presents the landslide susceptibility results from the geographically weighted regression model using remote sensing and geographic information system data for landslide susceptibility in the Inje area of South Korea. Landslide locations were identified from aerial photographs. The eleven landslide-related factors were calculated and extracted from the spatial database and used to analyze landslide susceptibility. Compared with the global logistic regression model, the Akaike Information Criteria was improved by 109.12, the adjusted R-squared was improved from 0.165 to 0.304, and the Moran’s I index of this analysis was improved from 0.4258 to 0.0553. The comparisons of susceptibility obtained from the models show that geographically weighted regression has higher predictive performance.