Development of landslide risk maps using high resolution airborne LiDAR data

Landslides are one of the catastrophic that often cause severe property damages, economic loss, and high maintenance costs. Slope failures are a result of multiple triggering parameters, including anthropogenic activities, intense earthquakes, and intense rainfall, and physical properties of unstable surface materials related to geology, land cover, slope geometry, moisture content, and vegetation. This thesis presents a set of novel GIS-based statistical approaches developed for the hazard mapping of rainfall-induced landslides using LiDAR derived data and parameters especially along the highway corridor. These approaches were tested in two areas along the PLUS Expressways Berhad in Perak, Malaysia: (1) Jelapang area (2) Gua Tempurung area. The objective of this research is firstly aims to identify optimized landslide conditioning parameters that influence the characteristic of landslides and optimise a spatial prediction of landslide hazard areas along the Jelapang and Gua Tempurung area of the North-South Expressway in Malaysia by using two statistical models, namely, logistic regression (LR) and evidential belief function (EBF). The second objective is to design and implement probabilistic (EBF) and statistical (LR) based analysis. LR and EBF determine the correlation between conditioning parameters and landslide occurrence. EBF can also be applied in bivariate statistical analysis. Thus, EBF can be used to assess the effect of each class of conditioning parameters on landslide occurrence. A landslide inventory map with historical landslide locations were recorded using field measurements for both study areas. Subsequently, the landslide inventory was randomly divided into two data sets. Approximately 70 % of the data were used for training the models, and 30 % were used for validating the results. Eight landslide conditioning parameters were prepared for landslide susceptibility analysis: altitude, slope, aspect, curvature, stream power index, topographic wetness index, terrain roughness index, and distance from river. The landslide probability index was derived using both methods (i.e. LR and EBF) and subsequently classified into five susceptible classes by using the quantile method. The resultant landslide susceptibility maps were evaluated using the area under the curve technique. The success rates of the EBF and LR models in Gua Tempurung were 73.93% and 84.91%, respectively while for Jelapang were 53.95% and 90.12%, respectively. The predicted accuracy rates of EBF and LR models in Gua Tempurung were 67.73% and 83.00%, respectively while Jelapang were 50.1% and 88.78%, respectively. Results revealed the proficiency of the LR method in landslide susceptibility mapping. The third objective of this research is to produce landslide hazard and vulnerability maps and implement landslide risk assessment which determines the expected degree of loss due to a landslide and the expected number of lives lost, people injured, damage to property and disruption of economic activity. To achieve this objective, the landslide susceptibility maps were transformed into a hazard map considering the main landslide triggering parameter (rainfall) recorded in the landslide inventory database in both study areas. Vulnerability to landslides is also regarded as another main parameter for risk analysis. In order to determine landslide risk in the study areas, the quantitative approach was used. For this purpose, the obtained landslide hazard and vulnerability maps were multiplied to produce risk map and a final landslide risk index map was obtained. Finally, after obtaining risk map through quantitative approach (i.e. LR), a comparison was carried out with risk maps derived from the “TEMAN” for both of study areas. The comparison of the results from TEMAN and LR method for the category of high risk slopes alone for Gua Tempurung and Jelapamg areas have been reduced to 96.2 % and 79%, respectively. The results proved that the method can be significantly effective for an accurate risk assessment for both study areas. Consequently, produced maps in this research may be helpful for planners, decision makers at PLUS, and government agencies in landslide management and planning in the study area.

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