Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Landslide Susceptibility Assessment Using TPI-Slope Combination

TPI와 경사도 조합을 이용한 산사태 위험도 평가

  • Lee, Han Na (Industry-Academia Collaboration Foundation, Gangneung-Wonju National University) ;
  • Kim, Gihong (Dept. of Civil Engineering, Gangneung-Wonju National University)
  • Received : 2018.11.11
  • Accepted : 2018.12.18
  • Published : 2018.12.31
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Abstract

TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.

TPI (Topographic Position Index)와 경사도를 조합하여 새로운 산사태 인자인 TSI (TPI-Slope Index)를 제안하고 산사태 예측모형에 적용하였다. 이를 위해, 먼저 다양한 분석 반경의 TPI를 서로 비교하여 TPI350이 연구 대상 지역에 가장 적합함을 알아내었고, 이를 경사도와 조합하여 TSI를 제작하였다. 본 논문에서 제안한 TSI의 적용성을 평가하기 위해 로지스틱 회귀분석을 이용한 결과, 산사태 예측 모형에 활용할 수 있다는 결론을 얻었다. 그 후, 기타 지형 정보들과 토양 및 임상 정보를 추가하여 산사태 위험도를 평가하는 로지스틱 회귀 모형을 제작하였다. 이를 위해 DEM (Digital Elevation Model), 토양도, 임상도로부터 추출할 수 있는 산사태 관련 인자들을 수집하고 이들을 검토하여 다른 인자와 상관도가 높거나 산사태와의 연관성이 낮은 인자들은 우선 제외하였다. 그 결과, TSI, 고도, 사면 길이, 경사향, 유효 토심, 영급, 나무 밀도, 임상 등 8개의 인자가 선정되어 회귀분석에 독립변수로 입력되었다. 변수의 입력 방법(전진 선택법, 후진 제거법, 직접 선택법)에 따라 3가지 모형을 생성하였고, 이들에 대한 평가를 수행하였다. 세 모형에서 선택된 변수는 조금씩 다르지만, 공통적으로 유효 토심, 나무 밀도, TSI 인자의 중요도가 높은 것으로 나타났다.

Keywords

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Fig. 1. Study area and landslide/non-landslide points

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Fig. 2. Flow chart

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Fig. 3. -2Lnℒ of each logistic regression model

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Fig. 4. Variation of slope, TPI350, and elevation at a slope

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Fig. 5. Distribution of landslide/non-landslide points in slope-TPI350 plane and TSI

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Fig. 6. Geological factors - Subsoil texture and soil type

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Fig. 7. Geological factors - Subsoil gravel content and soil structure

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Fig. 8. Geological factors – Soil series, effective soil depth, parent material, topsoil texture, and drainage

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Fig. 9. Forestial factors

Table 1. TPI classification

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Table 2. Evaluation of the factors: TPI350, slope, and TSI

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Table 3. Evaluation of topographic factors

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Table 4. Evaluation of geological factors

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Table 6. Performance of 3 logistic regression models

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Table 5. Evaluation of forestial factors

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