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Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system

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Abstract

For landslide susceptibility mapping, this study applied, verified and compared the Bayesian probability model, the weights-of-evidence to Panaon Island, Philippines, using a geographic information system. Landslide locations were identified in the study area from the interpretation of aerial photographs and field surveys, and a spatial database was extracted from SRTM (Shuttle Radar Topographic Mission) DEM (Digital Elevation Model) imagery, aerial photograph, topographic map, and geological map. The factors that influence landslide occurrence, such as slope, aspect, curvature, topographic wetness index and stream power index of topography, were calculated from SRTM imagery. Distance from drainage was extracted from topographic database. Lithology and distance from fault were extracted and calculated from geological database. Terrain mapping unit was classified from aerial photographs. The spatial association between the factors and the landslides was calculated as the contrast values, W + and W using the weights-of-evidence model. Tests of conditional independence were performed for the selection of the factors, allowing the large number of combinations of factors to be analyzed. For each factor rating, the contrast values, W + and W were overlaid for landslide susceptibility mapping. The results of the analysis showed that contrast rating (78.60%) for each factor’s multiclass had better accuracy of 5.90% than combinations of factor assigned to binary class with W + and W (72.70%).

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Acknowledgments

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Knowledge and Economy of Korea. We thank Digna G. Evangelista of the Mines and Geosciences Bureau who provided valuable metadata used in this study.

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Correspondence to Saro Lee.

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Oh, HJ., Lee, S. Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environ Earth Sci 62, 935–951 (2011). https://doi.org/10.1007/s12665-010-0579-2

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