• 대한공간정보학회지
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• 제15권3호
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• pp.69-76
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• 2007

강원도 지역은 산지지형이 많고 여름철 장마나 이상기후에 의한 국지적인 집중호우에 의해서 산사태가 발생하기 쉬운 조건을 가지고 있으며 최근, 수년간 계속되고 있는 대형 산불들로 인하여 지표상태가 변화되어 강우사상에 의해 쉽게 산사태가 일어나고 있다. 또한 앞서 발표된 연구논문에 의하면 우리나라의 경우 강우조건을 제외하고는 토지피복상태가 산사태 취약성 평가에 가장 큰 영향을 미치는 것으로 보고되고 있다. 따라서 이 연구에서는 산사태 취약지를 선정하고 산사태 취약 A등급지에 대해서 4개의 서로 다른 시기에 촬영된 인공위성 영상(Landsat ETM)으로 부터 토지 피복도를 추출하여 그 분포변화를 분석하였다. 또한 정규식생지수를 추출하고 식생활력도의 분포변화를 분석하여 지표상태의 변화가 산사태에 미치는 영향을 분석하고자 하였다. 그 결과 산사태 취약지에서 산림과 식생지수가 감소하고 있음을 알 수 있었다.

• 대한공간정보학회지
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• 제14권1호
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• pp.3-12
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• 2006

강원도 지역은 산지지형이 많고 여름철 장마나 이상기후에 의한 국지적인 집중호우에 의해서 산사태가 자주 발생하고 있다. 산사태를 유발하는 인자들은 매우 다양하고 붕괴 메커니즘이 매우 복잡하기 때문에 산사태와 같은 자연현상을 분석하고 연구하기에는 많은 어려움이 따른다. 그러나 GIS를 이용하면 효과적으로 자료를 분류하고 분석할 수 있으며, 컴퓨터에 의해 실세계를 모델링함으로서 분석결과를 시각적이고 객관적으로 설명할 수 있다. 따라서 이 연구에서는 과거 산사태가 발생하였던 지역에서의 산사태 발생 원인에 대한 분석을 통해서 산사태를 유발하는 인자를 결정하고, 각 유발인자들을 등급별로 분류하여 GIS DB를 구축하였으며, AHP법에 의해 경중률을 계산하고 GIS를 이용하여 연구지역에 대한 산사태 발생취약성을 평가한 후, 각각의 산사태 유발인자의 영향을 분석한 결과 임상인자의 영향이 가장 큰 것으로 분석되었다.

• 한국측량학회지
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• 제32권4_1호
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• pp.281-292
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• 2014

본 연구는 산사태 관련 인자를 달리하여 산사태 취약성을 분석한 후, 정확도를 비교하고자 한다. 이를 위해 항공사진을 이용하여 산사태 위치를 추출하였고, 항공 LiDAR와 수치지도를 이용한 지형인자, 각종 주제도를 이용한 토양, 임상, 토지피복 인자를 추출하여 공간데이터베이스를 구축하였다. 산사태 취약성 지도는 로지스틱 회귀분석과 빈도비를 이용하여 산사태 취약지수를 산정하는 것에 의해 작성되었다. 분석결과, 항공 LiDAR와 수치지도의 상관관계는 거의 일치하였으며, 각 방법별로 작성된 산사태 취약성 지도 사이에는 강한 상관관계가 존재하였다. 각 방법별로 작성된 산사태 취약성 지도는 높은 예측 정확도를 보였다. 특히, 빈도비와 항공 LiDAR를 이용할 경우 성능이 더욱 향상되었다. 이를 통해 항공 LiDAR 자료는 효과적인 산사태 발생 예측 및 피해저감대책을 수립하는데 기여할 것으로 판단된다.

• 한국지형학회지
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• 제27권3호
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• 한국지형학회지
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• 제27권2호
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• 산업기술연구
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• 제25권B호
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• pp.47-54
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• 2005

The goal of this study is to generate a landslide potential map using GIS(Geographic Information System) based method. A simple and efficient algorithm is proposed to generate a landslide potentialities map from DEM(Digital Elevation Model) and existing maps. The categories of controlling factors for landslides, aspect of slope, soil, vegetation are defined. The weight value for landslide potentialities is calculated from AHP(Analytic Hierarchy Process) method. Slope and Slope-direction is extracted from DEM, and soil information is extracted from digital soil map. Also, vegetation information is extracted from digital vegetation map. Finally, as overlaying, landslide potentialities map is made out, and it is compared with landslide place.

• 한국환경복원기술학회지
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• 제2권2호
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• pp.9-23
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• 1999

Pusan's reputation as the nation's most crowded city in terms of population density is attributable to its huge mountains which allow only small portion of residential area to its large population. Rapid increase of urban population on limited amount of land had naturally led its developments efforts to mountainous area giving rise to the concern of potential landslide. This study on urban Pusan and "Landslide Hazard Map" thereof is prepared in an attempt to avoid disasters created by landslide and also as a reference for city planners. The Map shows that the area covering 38% to 43% of urban Pusan has the potential for landslide. The study also shows that various civil works involving massive land excavation had been more direct cause of landslides in Pusan than such traditional factors as locations, ground slopes, rock types and topography of the area concerned.

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

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.310-313
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• 2004

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from TM satellite images; and the vegetation index value from SPOT satellite images. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better prediction accuracy than probabilistic model.

• 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.