• Economic and Environmental Geology
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• v.37 no.5
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• pp.521-532
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• 2004

The aim of this study is to evaluate the susceptibility of landslides at Kangneung area, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified from interpretation of satellite image and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. Using frequency ratio model which is one of the probability model, the relationships between landslides and related factors such as 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 were calculated as frequency ratios. Then, the frequency ratio were summed to calculate a landslide susceptibility indexes and the landslide susceptibility maps were generated using the indexes. The results of the analysis were verified and cross-validated using actual landslide location data. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.277-286
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• 1997

The purpose of this study is to construct the AM/FM system for a university. The sample area used in this study is the Gyeongsang National University in Chinju City, Korea. The layout maps, which scale are 1/1200, 1/3000 and 1/5000, a map of planning of common duct, which scale is 1/1200, and the facility maps which scale is 1/200 were used for input of spatial data. The year of pipe buried, materials of pipe, pipe diameter, length of pipe, depth of pipe buried and slope of pipe buried were purpose of efficient data searching, analyzing, and processing. It was revealed that rapid providing of facility informations and efficient facility management are possible using the constructed the AM/FM system for a university.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.481-487
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• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.499-506
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the newly developed techniques for assessment of landslide susceptibility to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial Photographs and field survey data, and a spatial database of the topography, soil type and timber cover were constructed. The landslide-related factors such as topographic slope, topographic curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter were extracted from the spatial database. Using those factors, landslide susceptibility and weights of each factor were analyzed by two artificial neural network methods. In the first method, the landslide susceptibility index was calculated by the back propagation method, which is a type of artificial neural network method. Then, the susceptibility map was made with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. The verification results show satisfactory agreement between the susceptibility index and existing landslide location data. In the second method, weights of each factor were determinated. The weights, relative importance of each factor, were calculated using importance-free characteristics method of artificial neural networks.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.22-32
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• 1995

The purpose of this study is to estimate not only the watershed soil loss but also its temporal changes of Kyungan River basin, the study area, due to the land development. To analyze the soil loss of the river basin, USLE was employed. GIS and remote sensing were also utilized to estimate the soil loss. The data for this analysis consist of a series of thematic map and remotely sensed data. The remotely sensed images for this study are Landsat TM(Oct, 28, 1997 & Sep. 22, 1992), In Kyungan River basin, not only the detection of temporal changes of land use and GVI, but also the estimation of soil loss provided very significant factors that affect to the watershed environment quality. The management of the factors of vegetative cover, slope steepness and length were the keys to reduce soil loss and solve conservation and protection issues of Kyungan River basin. GIS application with USLE to the watershed analysis allows the planner to recognize sensitive sites and to plan strategies to minimize soil loss.

• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.82-84
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• 2003

Watershed boundaries and flow paths within the watershed are the most important factors required in watershed analysis. Most often the derivation of watershed boundaries and stream network and flow paths is based on topographical maps but spatial variation of flow direction is not clearly understandable using this method. Water resources projects currently use 1: 50, 000-scale ground survey or aerial photography-based topographical maps to derive watershed boundary and stream network. In basins, where these maps are not available or not accessible it creates a real barrier to watershed geo-spatial analysis. Such situations require the use of global datasets, like GTOPO30. Global data sets like ETOPO5, GTOPO30 are the only data sets, which can be used to derive basin boundaries and stream network and other terrain variations like slope aspects and flow direction and flow accumulation of the watershed in the absence of topographic maps. Approximately 1-km grid-based GTOPO 30 data sets can derive better outputs for larger basins, but they fail in flat areas like the Karkheh basin in Iran and the Amudarya in Uzbekistan. A new window in geo-spatial hydrology has opened after the launching of the space-borne satellite stereo pair of the Terra ASTER sensor. ASTER data sets are available at very low cost for most areas of the world and global coverage is expected within the next four years. The DEM generated from ASTER data has a reasonably good accuracy, which can be used effectively for hydrology application, even in small basins. This paper demonstrates the use of stereo pairs in the generation of ASTER DEMs, the application of ASTER DEM for watershed boundary delineation, sub-watershed delineation and explores the possibility of understanding the drainage flow paths in irrigation command areas. All the ASTER derived products were compared with GTOPO and 1:50,000-based topographic map products and this comparison showed that ASTER stereo pairs can derive very good data sets for all the basins with good spatial variation, which are equal in quality to 1:50,000 scale maps-based products.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.721-730
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• 1999

A Grid-based daily evapotranspiration(ET) prediction model which calculates temporal and spatial ET with a complementary relationship of Morton(1983) was developed. The model was programmed by C-language and uses ASCII formatted map data of DEM(Digital Elevation Model) and land use. Daily ET within the watershed is calculated and the results of temporal variations and spatial distributions of ET are presented by using GRASS(Geographic Resources Analysis Support System). To verify the applicability of the model, it was applied to the part of Bocheong stream basin (76.5$\textrm{km}^2$) located in the upstream of Dacheong Dam watershed. The result shows that the estimated evapotranspiration in 1995 was 766.1mm and 22% increased after correction radiation for slope and area.

• Journal of the Korean Geotechnical Society
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• v.37 no.12
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• pp.117-125
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• 2021

The occurrence of ground subsidence and sinkhole in downtown areas, which threatens the safety of citizens, has been frequently reported. Among the various mechanisms of a sinkhole, soil erosion through the damaged part of the sewer pipe was found to be the main cause in Seoul. In this study, a random forest model for predicting the occurrence of sinkholes caused by damaged sewer pipes based on sewage pipe information was trained using the information on the sewage pipe and the locations of the sinkhole occurrence case in Seoul. The random forest model showed excellent performance in the prediction of sinkhole occurrence after the optimization of its hyperparameters. In addition, it was confirmed that the sewage pipe length, elevation above sea level, slope, depth of landfill, and the risk of ground subsidence were affected in the order of sewage pipe information used as input variables. The results of this study are expected to be used as basic data for the preparation of a sinkhole susceptibility map and the establishment of an underground cavity exploration plan and a sewage pipe maintenance plan.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.179-193
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• 2019

Landslides are one of the most damaging geological hazards worldwide, threating both humans and property. Hence, there have been many efforts to prevent landslides and mitigate the damage that they cause. Among such efforts, there have been many studies on mapping landslide susceptibility. Geographic information system (GIS)-based techniques have been developed and applied widely, and are now the main tools used to map landslide susceptibility. We reviewed the status of landslide susceptibility mapping using GIS by number of papers, year, study area, number of landslides, cause, and models applied, based on 776 articles over the last 20 years (1999-2018). The number of studies published annually increased rapidly over time. The total study area spanned 65 countries, and 47.7% of study areas were in China, India, South Korea, and Iran, where more than 500 landslides, 27.3% of all landslides, have occurred. Slope (97.6% of total articles) and geology (82.7% of total articles) were most often implicated as causes, and logistic regression (26.9% of total articles) and frequency ratio (24.7% of total article) models were the most widely used models. We analyzed trends in the causes of and models used to simulate landslides. The main causes were similar each year, but machine learning models have increased in popularity over time. In the future, more study areas should be investigated to improve the generalizability and accuracy of the results. Furthermore, more causes, especially those related to topography and soil, should be considered and more machine learning models should be applied. Finally, landslide hazard and risk maps should be studied in addition to landslide susceptibility maps.