Assessing probability of failure of urban landslides through rapid characterization of soil properties and vegetation distribution

Landslides are a major natural hazard, threatening communities and infrastructure worldwide. The mitigation of these hazards relies on the understanding of their causes and triggering processes, which depends directly on soil properties, land use, and their changes over time. In this study, we propose a novel framework to estimate the probability of failure in highly developed urban areas. The framework combines remote sensing and geophysical data to estimate soil properties and land covers. Such estimate properties are then integrated into a hydrogeomechanical model to provide a robust estimate of the probability of failure. To assess the importance and sensitivity of the input parameters to the probability of failure assessment, a sensitivity analysis was performed on the seven main parameters (density, friction angle, cohesion, soil thickness, slope, water recharge and saturated hydraulic conductivity) of the hydro-geomechanical model. Slope angle, soil thickness and cohesion are shown to be the most important parameters. While the slope angle can be derived from high-resolution digital elevation models, soil thickness and cohesion cannot be assessed. To incorporate the variability of these two parameters into the model, seismic noise measurements were performed to estimate soil thickness. Supervised classification of remote sensing data was used to map vegetation type and related root cohesion, which can impact the cohesion significantly. The results show that slopes with relatively thick soil layers (above 2 m) have up to four times higher probability of failure. Slopes with tall vegetation cover, and hence comparably high root cohesion, reduce the probability of failure, particularly when the soil layer is relatively thin (< 3 m). The developed approach makes use of rapid to acquire geophysical and easily to obtain remote sensing data, and hence is transferable to other study sites. This approach may be of particular importance to areas of active vegetation management that may cause considerable changes in landslide hazard maps.

Automated summary

This study proposes a novel framework to estimate the probability of failure in highly developed urban areas by combining remote sensing and geophysical data. The results show that slope angle, soil thickness, and cohesion are the most important parameters. Seismic noise measurements were performed to estimate soil thickness, and supervised classification of remote sensing data was used to map vegetation type and related root cohesion. The developed approach can be applied to other study sites and is particularly important in areas of active vegetation management.