The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 33–36, 2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 33–36, 2016

  22 Jun 2016

22 Jun 2016


K. T. Chang1, J. Dou2, Y. Chang3, C. P. Kuo1, K. M. Xu1, and J. K. Liu4 K. T. Chang et al.
  • 1Dept. of Civil Eng. and Environmental Informatics, Ming Hsin University of Science and Technology, Hsinchu County 30401, Taiwan
  • 2Center for Spatial Information Science, The University of Tokyo, Kashiwa 277-8568, Japan
  • 3Institute of Ocean Technology and Marine Affairs, National Cheng Kung University, Tainan 701 Taiwan
  • 4LIDAR Technology Co., Hsinchu County 30274, Taiwan

Keywords: Landslide, Susceptibility analysis, Certainty factor, Artificial neural networks, Remote sensing

Abstract. The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.