Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.     

Structural geology mapping using PALSAR data in the Bau gold mining district,Sarawak, Malaysia

The application of optical remote sensing data for geological mapping is difficult in the tropical environment. The persistent cloud coverage, dominated vegetation in the landscape and limited bedrock exposures are constraints imposed by the tropical climate. Structural geology investigations that are searching for epithermal or polymetallic vein-type ore deposits can be developed using Synthetic Aperture Radar (SAR) remote sensing data in tropical/sub-tropical regions. The Bau gold mining district in the State of Sarawak, East Malaysia, on the island of Borneo has been selected for this study. The Bau is a gold field similar to Carlin style gold deposits, but gold mineralization at Bau is much more structurally controlled. Geological analyses coupled with the Phased Array type L-band Synthetic Aperture Radar (PALSAR) remote sensing data were used to detect structural elements associated with gold mineralization. The PALSAR data were used to perform lithological-structural mapping of mineralized zones in the study area and surrounding terrain. Structural elements were detected along the SSW to NNE trend of the Tuban fault zone and Tai Parit fault that corresponds to the areas of occurrence of the gold mineralization in the Bau Limestone. Most of quartz-gold bearing veins occur in high-angle faults, fractures and joints within massive units of the Bau Limestone. The results show that four deformation events (D1–D4) in the structures of the Bau district and structurally controlled gold mineralization indicators, including faults, joints and fractures are detectable using PALSAR data at both regional and district scales. The approach used in this study can be more broadly applicable to provide preliminary information for exploration potentially interesting areas of epithermal or polymetallic vein-type mineralization using the PALSAR data in the tropical/sub-tropical regions.