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