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[국내논문] APPLICATION OF LOGISTIC REGRESSION MODEL AND ITS VALIDATION FOR LANDSLIDE SUSCEPTIBILITY MAPPING USING GIS AND REMOTE SENSING DATA AT PENANG, MALAYSIA 원문보기

대한원격탐사학회 2004년도 Proceedings of ISRS 2004, 2004 Oct. 01, 2004년, pp.310 - 313  

LEE, SARO (Geoscience Information Center, Korea Institute of Geology & Mineral Resources (KIGAM))

Abstract AI-Helper 아이콘AI-Helper

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...

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제안 방법

  • Landslide occurrence areas were detected in the Penang area, Malaysia by interpretation of aerial photographs and field surveys. A map of landslides was developed from aerial photographs, in combination with the GIS, and this were used to evaluate the frequency and distribution of shallow landslides in the area. Topography and lithology databases were constructed and lineament, land use and vegetation index value extracted from Landsat TM and SPOT XS satellite image for the analysis.
  • The formula was used for calculate the landslide susceptibility index, and the index was mapped to represent landslide susceptibility. Finally, the susceptibility map was verified using known landslide locations and success rates were calculated for quantitative validation. To compare the validation result, probabilistic model, frequency ratio was applied using the same database.
  • Where the dependent variable is binary, the logistic link function is applicable (Atkinson and Massari, 1998). For this study, the dependent variable must be input as either 0 or 1, so the model applies well to landslide possibility analysis. Logistic regression coefficients can be used to estimate ratios for each of the independent variables in the model.
  • The lineament buffer was calculated in 100 m intervals. Land use data was classified using  a LAND SAT TM image employing an unsupervised classification method and field survey. The 11 classes identified, such as urban, water, forest, agricultural area, and barren area were extracted for land use mapping.
  • The landslide susceptibility map was made using the LSV values and for interpretation.
  • A map of landslides was developed from aerial photographs, in combination with the GIS, and this were used to evaluate the frequency and distribution of shallow landslides in the area. Topography and lithology databases were constructed and lineament, land use and vegetation index value extracted from Landsat TM and SPOT XS satellite image for the analysis. Then, the calculated and extracted factors were converted to a 10m x 10m grid (ARC/INFO GRID type).
  • The landslide susceptibility analysis result was validated using known landslide locations. Validation was performed by comparing the known landslide location data with the landslide susceptibility map. Each factor used and frequency ratio was compared.

대상 데이터

  • The application of remote sensing methods, such as aerial photographs and satellite images, are used to obtain significant and cost-effective information on landslides. In this study, 1:10, 000-1:50, 000-scale aerial photographs were used to detect the landslide locations. These photographs were taken during the period 1981- 2000, and the landslide locations were detected by photo interpretation and the locations verified by fieldwork.
  • Recent landslides were observed in aerial photographs from breaks in the forest canopy, bare soil, or other geo- morphic characteristics typical of landslide scars, for example, head and side scarps, flow tracks, and soil and debris deposits below a scar. To assemble a database to assess the surface area and number of landslides in each of three study areas, a total of 541 landslides were mapped in a mapped area of 293 km2.
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