• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.141-143
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• 2003

The aim of this study is to evaluate the susceptibility of landslides at Clicap area, Indonesia , using a Geographic Information System (GIS). Landslide locations were identified from field surveys. The topographic and geological map were collected and constructed into a spatial database using GIS. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database and lihology and fault was extracted from the geological database. Then landslide susceptibility was analyzed using the landslide-occurrence factors by likelihood methods. The results of the analysis were verified using the landslide location data. The GIS was used to analyze the vast amount of data efficiently . The results can be used to reduce associated hazards, and to plan land use and construction.

• 한국측량학회지
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• 제8권2호
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• pp.45-51
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• 1990

수치표고모델은 실제지형모델의 참값과 비교하여 충분하고 높은 정확도를 지녀야 하며, 미리 주어져 있는 지형데이타와 보간법을 사용하여 임의의 평면위치(X, Y)에 대응하는 표고 h를 구하고 경사를 구할 수 있는 모텔을 만들어 놓아야 한다. 보간의 정확도는 지형의 기복 상태와 자료의 밀도에 우선적으로 영향을 받게 되므로, 지형을 객관적인 방법으로 분류할 필요가 있다. 평균경사도와 면적비는 지형이 경사상태에 따라 비슷한 분류 결과를 가져오는 변수이지만, 지형의 국소적인 변화크기를 동시에 표시하는 변수는 면적비이다. 그러므로 본 연구는 지형의 분류를 좀 더 객관화하기 위하여 비고에 의해서가 아니라 경사도에 의해 분류하고, 경사와 곡률경중율의 영향을 분석하여 그 경사도에 합당한 매개변수를 도입하므로써 보다 현실에 가까운 모델을 재현하는데 연구의 목적이 있다. 연구의 결과는 첫째, 경사에 의한 지형의 분류는 평지는 pl6과 p24 준경사지 pl6과 S, 급경사지는 S 와 p24가 적합하고 곡률에 의한 분류는 평지와 준경사지에서 모두 p24와 S가 적합하며, 급경사지에서는 pl6이 적합하였다. 경사와 기복 변화량을 조합한 경우는 평지는 pl6, 준경사지는 p24, 급경사지는 S가 각각 적합하였다. 둘째, 유형화율은 경사 50%, 곡률경중율 0.0015에서 50∼80%로 가장 컸다.

• 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.

• 한국지형학회지
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• 제26권2호
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권11호
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• pp.1350-1358
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• 1999

이 논문에서는 유전밴드 영상신호에 포함되어 있는 지형적 특징을 이용하여 밝기의 변화가 일정하지 않은 유전밴드를 인식하는 방법을 연구하였다. 유전밴드는 동일인을 식별하는데 있어서 지문보다 높은 신뢰성을 가지고 있으므로, 유전밴드 영상에서 유전밴드의 유무와 위치를 자동적으로 검출하는 것은 매우 중요하다. 레인내의 밝기의 변화가 일정한 유전밴드는 미분연산자에 의해 검출할 수 있지만, 밝기의 변화가 일정하지 않은 레인내의 유전밴드는 일반적인 인식방법에 의해서는 검출하기 어렵다. 따라서 유전밴드 영상으로부터 지형적 특징을 추출하고, 이것으로부터 계산한 곡률(curvature)의 크기에 의해 유전밴드를 인식함으로써 레인의 밝기가 변화하는 경우에도 효과적으로 인식하였다.Abstract This paper presents recognition of DNA band using the topographical features of DNA band images. The DNA band provides a more reliable way of identification than fingerprints. Recognition based on differentiation operators can easily detect the DNA band if the brightness of lane in the image is almost uniform. When the brightness of the lane changes gradually, the DNA bands are hard to be recognized. Using the curvature magnitude of the lane computed from topographic features extracted from DNA images, the DNA bands are efficiently recognized in the lane whose brightness changes.

• 자원환경지질
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• 제33권4호
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• pp.321-332
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• 2000

The purpose of this study is to analyze landslide that occurred in Yongin area in 1991 using spatial database. For this, landslide locations are detected from aerial photographs interpretation and field survey. The locations of landslide, topography, soil, forest and geology were constructed to spatial database using Geographic Information System (GIS). To establish occurrence factors of landslide, 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 TM satellite image. Landslide was analyzed using spatial correlation between the landslide and the landslide occurrence factors by bivariate probability methods. GIS was used to analyze vast data efficiently and statistical programs were used to maintain specialty and accuracy. The result can be used to prevention of hazard, land use planning and construction planning as basic data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.135-137
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• 2003

The purpose of this preliminary study is the applied to geology using the Landsat TM satellite image of the Tanggung area of southern part of the Bandung, Indonesia to provide basic information for geological survey. For this, topography, geology and satellite image were constructed to spatial database. Digital elevation, slope, aspect, curvature, hill shade of topography were calculated from the topographic database and lithology was imported from the geological database. Lineament, lineament density, and NDVI were extracted the Landsat TM satellite image. The results showed the close relationship between geology and terrain and satellite image. Each sedimentary rock seldom corresponds with geology and analyses of topography but as a whole for sedimentary rocks coincide with them. Tuff and volcanic breccia in the volcanic rocks correspond with the result of terrain analyses. Talus deposits is well matched with the analyses of opography/satellite image.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 가을 학술발표회 논문집
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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.

• 한국지형학회지
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• 제25권2호
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• pp.1-14
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• 2018

The main objective of this paper is to classify properties of the locational environment for each debris type by calculating likelihood ratio based on the correlation between the distributions for each type of debris landform. A total of 8 thematic maps, like as elevation, slope, aspect, curvature, topographic wetness index (TWI), soil drainage, geology, and landcover including with GIS spatial information generally used in this type of debris landform analysis. The results of this study showed that the block stream had a high likelihood ratio compared to talus in areas with relatively high elevation; and concerning slope, the block stream had a high likelihood ratio in a relatively low region than talus. Concerning aspect, a clear correlation could not be analyzed for each debristype, and concerning curvature, the block stream displayed a developed slope on the more concave valley than the talus. Analysis concerning TWI, the block stream displayed a higher likelihood ratio in wider sections than talus, and concerning soil drainage, the talus and block stream both displayed a high likelihood ratio in regions with well-drained soil. The talus displayed a high likelihood ratio in the order of metamorphic rocks, sedimentary rocks, and granite, while the block stream displayed a high likelihood ratio in the order of volcanic rocks, granite, and sedimentary rocks. In addition, concerning landcover, the likelihood ratio had the most concentrated distributed compared to natural bare land only concerning talus. Based on the likelihood ratio result, it can be used as basic data for extracting the possible areas of distribution for each debris type through the GIS spatial integration method.

• 한국안광학회지
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• 제17권2호
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• pp.223-232
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• 2012

목적: 본 연구는 각막 전면의 지형과 각막의 두께를 이용하여 각막 후면 정점 곡률과 asphericity(Q)를 측정하기 위해 고안된 새로운 방법의 신뢰도 평가를 위해서 시행 되었다. 방법: 각막 후면의 정점 곡률 및 Q는 Medmont E300 corneal topographer로 측정한 각막 전면의 지형 data와 Holden-Payor optical pachometer로 측정한 각막 수평 경선의 두께 data를 이용하여 계산 되었다. 정확한 각막 두께를 계산 하기위하여 각막 전면 측정 위치의 곡률반경과 각막의 겉보기 두께로부터 각막의 실제 두께를 계산 할 때 정확한 방정식을 이용하였으며, 이는 선행 연구와 구별되는 점이다. 그리고 각막 전면과 후면의 지형은 각막 전면의 지형 data와 계산된 각막 후면의 좌표를 best fit 알고리즘을 이용하여 계산 되었다. 각막 후면의 지형 측정의 신뢰도는 10개의 polymethyl methacrylate(PMMA) lens와 성인 5명의 각막을 측정 하여 평가 하였다. 결과: 10개의 PMMA lens를 이용한 평가에서는 후면 정점 곡률과 후면 Q의 mean absolute accuracy(${\pm}SD$)는 각각 $0.053{\pm}0.044mm$(95% 신뢰구간(CI) -0.033~0.139)와 $0.10{\pm}0.10$(95% CI -0.10~0.31)이였다. 그리고 5명의 각막을 이용한 평가에서의 각막 후면 정점 곡률과 후면 Q의 mean absolute repeatability coefficient(${\pm}SD$)는 각각 $0.07{\pm}0.06mm$(95% CI -0.05~0.19)와 $0.09{\pm}0.07$(95% CI -0.05~0.23) 이였다. 결론: 새로운 방법을 이용하여 신뢰할 수 있는 각막 후면의 지형(정점 곡률과 Q)을 계산 할 수 있었다. 이러한 새로운 방법은 살아있는 인체 각막의 정확한 후면 지형 계산에 적용 될 수 있다.