• 물과 미래
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• 제10권2호
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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.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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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.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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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.

• 한국조경학회지
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• 제31권3호
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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.

• 대한환경공학회지
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• 제36권6호
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• pp.402-411
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• 2014

본 연구는 그린인프라 기법이 개별 요소시설로 뿐만 아니라 토지이용 및 설계과정에서 적용될 수 있도록 적용 대상 토지의 특성을 평가하여 그린인프라를 적용하는 방법을 제안한다. 이를 위한 토지 특성 평가 지표로 토지피복, 파편화 정도, 주거지와의 인접도, 유사한 토지 파편들 간의 군집 정도를 통한 녹지 네트워크 수준을 이용하였다. 평가 결과는 토지가 개발에 가까운 적성을 가졌는지, 보전에 가까운 적성을 가졌는지를 결정해준다. 결정된 토지의 적성이 개발과 가깝다면 구조적 그린인프라 기법의 분산 배치를 적용할 수 있다. 결정된 토지의 적성이 보전과 가깝다면 계획 및 설계 단계에서 적용할 수 있는 비구조적 그린인프라 기법이 바람직하다. 구축된 분석 방법을 국내 사례지에 적용하고 분석 결과를 기존의 토지이용계획과 비교하여 이를 통해 그린인프라 적용기법이 가지는 시사점을 도출하는 것이 본 연구의 최종 목적이다.

• 한국습지학회지
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• 제24권2호
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• pp.93-101
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• 2022

본 연구는 지형 및 토지피복에 따른 묵논습지의 분포 특성을 분석하기 위해 수행되었다. 충남도 서산시, 당진시, 보령시, 태안군을 대상으로 GIS와 현장답사를 통해 묵논습지를 찾아내어 경사도와 고도 및 토지피복유형에 따른 분포현황을 분석하였다. 연구결과, 4개시군의 묵논습지는 총106개로 확인되었고, 각 묵논습지가 위치한 지점의 평균 고도는 38.85m(S.D. 32.76)이고 평균 경사도는 6.27˚(S.D. 5.39), 총면적은 24,200km²로 나타났다. 경사도 5˚ 미만의 평지에 63개(12,121.07km²), 5~10˚ 27개(9,524.15km²) 등 10˚ 미만의 평지 또는 완경사지에 90개(84.9%)의 묵논습지가 분포하고 있었다. 면적은 21,645.22km²로서 전체 묵논습지 면적의 89.5%에 이른다. 고도 25m 미만의 저지대에 48개(12,326km²), 50m미만 29개(4,909.4km²) 등으로서, 모두 77개(72.7%)의 묵논습지가 고도 50m 이내의 저지대에 분포하고 있으며, 면적으로는 17,235.8km²로서 전체 묵논습지 면적의 71.2%에 이른다. 묵논습지 환경요인 중 경사도와 고도 사이에 통계적으로 상관관계는 없는 것으로 나타났다. 토지피복 분류에 따라서는 인공초지(38), 논(33), 밭(22) 등에 많이 분포하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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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.