• Water for future
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• v.10 no.2
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• pp.101-111
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• 1977

The analysis performed here is aimed to increase the familiarity of hydrologic process especially for the small basins which are densely gaged. Kyung An and Mu Shim river basins are selected as a represectative basin according to the criteria which UNESCO has establisheed back in 1964 and being operated under the auspice of Ministry of Construction. The data exerted from these basins is utilized for the determination of the characteristics of precipitation and runoff phenomena for the small basin, which is considerred as a typical Korean samll watershed. The methodology developed by Soil Conservation Service, USA for determination of runoff value from precipitation is applied to find the suitability of the method to Korean River Basin. The soil cover complex number or runoff curve number was determined by considering the type of soil, soil cover, land use and other factor such as antecent moisture content. The averag values of CN for Kyung An and Mushim river basins were found to be 63.9 and 63.1 under AMC II, however, the values obtained from soil cover complex was less than those from total precipitation and effective precicpitation by 10-30%. It may be worth to note that an attention has to be paid in the application of SCS method lo Korean river basin by adjusting 10-30% increase to the value obtained from soil cover complex. Finally, the design flood hydrograph was consturcted by employing unit hydrograph technique to the dimensionless mass curve. Also a stepwise multiple regression was performed to find the relationship between runoff and API, evapotranspiration rate, 5 days antecedent precipitation and daily temperature.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.64-64
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• 2003

The aim of this study is to apply and verify of logistic regression at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database.13${\times}$1ure, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the logistic regression coefficient were overlaid for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.63-63
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• 2003

The aim of this study is to apply and verify of Bayesian probability model, the likelihood ratio and statistical model, at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the likelihood ratio coefficient were overlaid for landslide susceptibility mapping, Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.148-148
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• 2018

Land subsidence caused by excessive groundwater pumping is a serious hydro-geological hazard. The spatial variability in land use, unbalanced groundwater extraction and aquifer characteristics are the key factors which make the problem more difficult to monitor using conventional methods. This study uses the European Space Agency (ESA) Sentinel satellites to investigate and monitor land subsidence varying with different land covers and groundwater use in the arid Quetta valley, Pakistan. The Persistent Scattering Differential Interferometry of Synthetic Aperture Radar (PS-DInSAR) method was used to develop 28 subsidence interferograms of the study area for the period between 16 Oct 2014 and 06 Oct 2016 using ESA's Sentinel-1 SAR data. The uncertainty of DInSAR result is first minimized by removing the dynamic effect caused by atmospheric factors and then filtered using the radar Amplitude Dispersion Index (ADI) to select only the stable pixels. Finally the subsidence maps were generated by spatially interpolating the land subsidence at the stable pixels, the comparison of DInSAR subsidence with GPS readings showed an R 2 of 0.94 and mean absolute error of $5.7{\pm}4.1mm$. The subsidence maps were also analysed for the effect of aquifer type and 4 land covers which were derived from Sentienl-2 multispectral images. The analysis show that during the two year period, the study area experienced highly non-linear land subsidence ranging from 10 to 280 mm. The subsidence at different land covers was significantly different from each other except between the urban and barren land. The barren land and seasonally cultivated area show minor to moderate subsidence while the orchard and urban area with high groundwater extraction rate showed excessive amount of land subsidence. Moreover, the land subsidence and groundwater drawdown was found to be linearly proportional to each other.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.256-272
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and to apply the newly developed techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs, field survey data, and a spatial database of the topography, soil type, timber cover, geology and land use. The landslide-related factors (slope, aspect, curvature, topographic type, soil texture, soil material, soil drainage, soil effective thickness, timber type, timber age, and timber diameter, timber density, geology and land use) were extracted from the spatial database. Using those factors, landslide susceptibility was analyzed by artificial neural network methods. For this, the weights of each factor were determinated in 3 cases by the backpropagation method, which is a type of artificial neural network method. Then the landslide susceptibility indexes were calculated and the susceptibility maps were made with a GIS program. The results of the landslide susceptibility maps were verified and compared using landslide location data. A GIS was used to efficiently analyze the vast amount of data, and an artificial neural network was turned out be an effective tool to maintain precision and accuracy.

• Journal of the Korean Institute of Landscape Architecture
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• v.31 no.3
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• pp.83-90
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• 2003

This study explored effects of urban greenspace on improving atmospheric environment, which is concerned with $CO_2$, SO$_2$ and NO$_2$ uptake, and with reduction of summer air temperatures. The site of this study was focused on Jung-gu in Seoul. Tree density and cover were 1.1 trees/100 $m^2$ and 12.5% respectively for the study area except forest lands. Atmospheric purification by greenspace was associated with changes in tree cover per unit area of each land use type. The mean $CO_2$ storage by woody plants was 19.4t/ha, and annual uptake averaged 2.2t/ha/yr for $CO_2$, 1.9kg/ha/yr for SO$_2$ and 5.0kg/ha/yr for NO$_2$. Entire tree plantings in the study area played a significant role by annually offsetting $CO_2$ emissions of about 1,830t from fossil fuel consumption by 330 persons, SO$_2$ emissions of 1,620kg by 1,080 persons, and NO$_2$ emissions of 4,230kg by 450 persons. The summer air temperature was 3.6$^{\circ}C$ cooler at a location with 54% cover of woody plants and 4.5$^{\circ}C$ cooler at a forest site with 100% cover, compared to a place with no planting. A 10% increase of woody plant cover was estimated to decrease summer air temperature by approximately 0.6$^{\circ}C$ until a certain level of canopy cover. Analyzing data from the Automatic Weather Stations in Seoul revealed that increasing tree cover decreased mean air temperature for the summer season (Jun~Aug) in a nonlinear function. Woody plant cover was the best predictive variable of summer temperature reduction. The results from this study are expected to be useful in emphasizing the environmental benefits and importance of urban greenspace enlargement, and in urging the necessity for planting and management budgets.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.6
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• pp.402-411
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• 2014

This study suggests the method of green infrastructure (GI) application which helps proper distribution of structural GI and non-structural GI by using land characteristics assessment and performs the case study. Land assessment standard consists of land cover type, fragmentation degree, proximity degree to residential districts, and cluster degree of fragmented areas which represents the quality of green network. The result of assessment proposes the land suitability to preserve or develop and it can be utilized to choose the type of the green infrastructures.

• Journal of Wetlands Research
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• v.24 no.2
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• pp.93-101
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• 2022

This study was conducted to analyze the distribution characteristics of abandoned paddy wetlands according to topography and land cover. In Seosan-si, Dangjin-si, Boryeong-si, and Taean-gun, Chungcheongnam-do, abandoned rice wetlands were found through GIS and field surveys, and the distribution status according to slope, elevation and land cover type was analyzed. As a result of the study, a total of 106 abandoned paddy wetlands were identified, and the average elevation of each abandoned paddy wetlands was 38.85m (S.D.32.76), the average slope was 6.27˚ (S.D.5.39), and the total area was 24,200km². 90 sites (84.9%) of abandoned paddy wetlands were distributed on flat land with less than 5˚ slope, 63 sites (12,121.07km²), and 27 sites(9,524.15km²) at 5-10˚ (9,524.15km²) on flat land with less than 10˚. The area is 21,645.22km²(89.5%) of the total area of abandoned paddy wetlands. 48 sites(12,326km²) in the lowlands with an altitude of less than 25 m, 29 sites(4,909.4km²) below 50m. It accounts for 71.2% of the total area of abandoned paddy wetlands. Among environmental factors of abandoned paddy wetlands, there was no statistically significant correlation between slope and altitude. According to the land cover classification, it was widely distributed in artificial grasslands (38), paddy fields (33), and fields (22).

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.291-293
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• 2004

The aim of this study was to cross-validate a spatial probabilistic model of landslide likelihood ratios at Boun, Janghung and Yongin, in Korea, using a Geographic Information System (GIS). Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and field surveys. Maps of the topography, soil type, forest cover, lineaments and land cover were constructed from the spatial data sets. The 14 factors that influence landslide occurrence were extracted from the database and the likelihood ratio of each factor was computed. 'Landslide susceptibility maps were drawn for these three areas using likelihood ratios derived not only from the data for that area but also using the likelihood ratios calculated from each of the other two areas (nine maps in all) as a cross-check of the validity of the method For validation and cross-validation, the results of the analyses were compared, in each study area, with actual landslide locations. The validation and cross-validation of the results showed satisfactory agreement between the susceptibility map and the existing landslide locations.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.298-301
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• 2004

The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.