Engineered slope failure susceptibility modelling using high spatial resolution geospatial data

Given the increased hazards faced by transport corridors such as climate induced extreme weather, it is essential that local spatial hotspots of potential landslide susceptibility can be recognised. In this research, an evidential reasoning multi-source geospatial integration approach for the broad-scale recognition and prediction of landslide susceptibility in transport corridors was developed. Airborne laser scanning and Ordnance Survey DTM data is used to derive slope stability parameters, while Compact Airborne Spectrographic Imager (CASI) imagery and existing national scale digital map datasets are used to characterise the spatial variability of land cover, land use and soil type. A novel approach to characterisation of soil moisture distribution within transport corridors was developed that incorporates the effects of the catchment contribution to local zones of moisture concentration in earthworks. The derived topographic and land use properties are integrated within the evidential reasoning approach to characterise numeric measures of belief, disbelief and uncertainty regarding slope instability spatially within the transport corridor. The model highlighted the importance of slope, concave curvature and permeable soils with variable intercalations accounting for over 80% of slope instability and an overall predictive capability of 77.75% based on independent validation dataset.

Automated summary

An evidential reasoning multi-source geospatial integration approach was developed to recognize and predict landslide susceptibility in transport corridors. By integrating airborne laser scanning, digital map datasets, and imagery data, along with a novel method for characterizing soil moisture distribution, numeric measures of slope instability were spatially characterized.