• 지질공학
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• 제32권1호
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• pp.27-40
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• 2022

산사태는 매년 전 세계적으로 상당한 규모의 인명 및 재산 피해를 발생시키고 있다. 따라서 이러한 산사태를 정확하게 예측하는 것은 매우 중요한 일이며 많은 연구자들이 다양한 접근방식을 활용하여 많은 연구를 수행하여 왔다. 산사태 예측과 관련된 분석은 고려되는 영향인자들과 분석되는 위험성의 예측 수준 그리고 산사태 발생 시 예상되는 피해에 대한 고려에 따라 산사태 취약성 분석, 산사태 위험성 분석 그리고 산사태 리스크 분석으로 구분된다. 이들 분석 중 현재 획득 가능한 관련 자료의 수준을 감안하여 국내외적으로 산사태 취약성 분석이 주로 수행되고 있다. 산사태 취약성 분석은 다양한 정성적 및 정량적 분석기법이 적용되어 왔으며 최근 들어서는 예측성능이 우수한 것으로 알려진 물리사면모델을 활용한 분석기법이 폭넓게 사용되고 있다. 특히 물리사면모델은 산사태 발생 이력의 존재여부와 상관없이 적용이 가능하고 산사태 발생 프로세스를 재현할 수 있다는 장점을 가지고 있다. 따라서 본 논문에서는 물리사면모델을 활용한 취약성 분석기법에 대하여 검토해 보았다.

• 한국농림기상학회지
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• 제25권4호
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• pp.376-386
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• 2023

기후변화로 인해 우리나라의 산사태 피해는 지속적으로 증가하고 있다. 사방사업 등 산사태 피해저감을 효과적으로 수립하기 위해서는 기후변화 영향을 고려하여 장기간의 산사태 위험도를 추정할 필요가 있다. 이 연구에서는 다변량 회귀분석을 통해 기후변화에 따른 산사태 피해면적의 변화를 예측하였다. 1980-2010 년의 산사태 피해면적과 강우관측자료를 학습자료로 적용하여 다변량 회귀모형을 구축하였다. 이때 강우관측자료를 통해 SSP 시나리오에서는 제공하는 7가지 강우인자를 추출하였다. 이후 분산팽창지수로 다중공선성을 검정하고 주성분 분석을 통해 차원을 축소하여 2개의 주성분을 독립변인으로 하여 산사태 피해면적 추정 모형을 도출하였다. 기후변화 시나리오를 활용하여 2030-2100년까지의 산사태 피해면적 변화를 추정한 결과, 산사태 피해면적은 1981년-2010년의 연평균 산사태 면적의 최대 2배 이상으로 증가하는 것으로 나타났다. 이 연구의 결과는 미래 기후변화를 고려한 산사태 피해저감 대책 수립 및 보강의 필요성을 제시하는 기초자료로 활용 가능할 것으로 보인다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.371-374
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• 2007

For development of landslide risk assessment techniques using GIS(Geographic Information System), this study classifies the category of socioeconomic factors. The landslide quantitative risk assessment performs first prediction of flow trajectory and runout distance of debris flow over natural terrain. Based on those results, it can be analyzed the factors of socioeconomic which are directly related to the magnitude of risk due to landslide hazards. Those risk assessment results can deliver factual damage situation prediction to policy making for the landslide damage mitigation. Therefore, this study is based on feature classification of the digital map ver. 2.0 provided by the National Geographic Information Institute. The category of factors can be used as useful data in preventing landslide.

• 한국지반환경공학회 논문집
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• 제18권2호
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• pp.21-27
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• 2017

In order to evaluate landslide susceptibility in Yanbian region, this study analyzed 7 factors related to landslide occurrence, such as soil, geology, land use, slope, slope aspect, fault and river by Analytic Hierarchy Process (AHP), and calculated the weights of these 7 hazard-induced factors, determined the internal weights and the relative weights between various factors. According to these weights, combining the Remote Sensing technology (RS) with Geographic Information System technology (GIS), the selected area was evaluated by using GIS raster data analysis function, then landslide susceptibility chart was mapped out. The comprehensive analysis of AHP and GIS showed that there has unstable area with the potential risk of sliding in the research area. The result of landslide susceptibility agrees well with the historical landslides, which proves the accuracy of adopted methods and hazard-induced factors.

• 자원환경지질
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• 제33권6호
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• pp.547-556
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• 2000

The purpose of this study is to analyze shear fracture system using landslide location occurred 1998 at Janghung area. For the geological implication, foliation was surveyed and analyzed, and location of landslide, geological structure and topography were constructed into spatial database using GIS. With the constructed spatial database, shear fracture system was assessed by the relation analysis between strike and dip of the foliation and aspect and slope of the topography. We compared strike and dip of foliation and aspect and slope of topography and recognized the typical fracture pattern, strike and dip of joint, that coincided with shear fracture system. The result tells us that foliation of gneiss has geometrical relation to joint or fault that leading landslide. GIS was used to analyze vast data efficiently and the result can be used to assess the landslide susceptibility as important factor.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.547-550
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• 2006

This study is to suggest a methodology to produce landslide hazard map by combining LRA (Logistic Regression Analysis) and AHP (Analytic Hierarchy Program) Approach. Topographic factors (slope, aspect, elevation), soil drain, soil depth and land use were adopted to classify landslide hazard areas. The method was applied to a 520 $km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9 % matching rate for the real landslide sites comparing with the classified areas of high-risk landslide while LRA and AHP showed 46.1 % and 48.7 % matching rates respectively.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.381-384
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• 2004

The goal of this study is to generate a landslide susceptibility map using GIS(geographic information system) based method. A simple and efficient algorithm is proposed to generate a landslide susceptibility map from DEM(digital elevation model) and existing maps. The categories of controlling factors for landslides, aspect of slope, soil, topographical index, landuse, vegetation are defined, because those factors are said to have relevance to landslide and are easy to obtain theirs sources. The weight value for landslide susceptibility is calculated from the density of the area of landslide blocks in each class. Finally, a map of susceptibility zones is produced using the weight value of all controlling factors, and then each susceptibility zone is evaluated by comparing with the distribution of each controlling factor class.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 춘계 학술발표회
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• pp.1020-1025
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• 2009

Recently, unusual change of weather occurred in world wide region causes localized heavy rainfall and consequently disasters like landslide and debris flow in steep slope area. And the main factors of these disasters are rainfall and forest fire. To verify the existing landslide prediction and warning system, information about landslide and rainfall were collected for a data base system and analysed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.257-259
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• 2003

Landuse maps are prepared from satellite imagery and field observations were conducted at various locations in the study area. Compared to the field data and NDVI and RVI thematic maps, NDVI is better than RVI, because it compensates for changing illumination conditions, surface slope, aspect and other factors. Clouds, water and snow have negative values for RVI and NDVI. Rock and bare soils have similar reflectance in both NIR and visible band, so RVI and NDVI are near zero. In forest areas with good vegetation cover, NDVI is high and landslide occurrence is less. But if annual and biennial vegetations are present and if cultivation practices are changed frequently, NDVI is medium and landslide occurrence is moderate. In areas where deforestation and settlement is in progress, NDVI is less and landslide occurrence is more. The NDVI FCC thematic map may be used as an important layer in GIS application for landslide studies. Analyzing other layers such as slope, rainfall, soil, geology, drainage, lineament, etc with NDVI FCC layer will give a better idea about the identity of landslide prone areas.

• 대한원격탐사학회지
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• 제24권5호
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• pp.453-462
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• 2008

The objective of this paper is to compare the prediction performances of different landslide hazard maps based on topographic data stemming from different sources of elevation. The geostatistical framework of kriging, which can properly integrate spatial data with different accuracy, is applied for generating more reliable elevation estimates from both sparse elevation spot heights and exhaustive ASTER-based elevation values. A case study from Boeun, Korea illustrates that the integration of elevation and slope maps derived from different data yielded different prediction performances for landslide hazard mapping. The landslide hazard map constructed by using the elevation and the associated slope maps based on geostatistical integration of spot heights and ASTER-based elevation resulted in the best prediction performance. Landslide hazard mapping using elevation and slope maps derived from the interpolation of only sparse spot heights showed the worst prediction performance.