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Many of the UNESCO World Heritage Sites face geological threats which could have negative effects on the value, integrity and accessibility of their heritage assets. A relevant example is the Derwent Valley Mills UNESCO World Heritage site, one of the key sites of Britain's industrial revolution of the 18th century and located along the Derwent River Valley. Individual susceptibility scenarios of natural hazards in the area like collapsible deposits, compressible ground, debris flow, landslide, running sands, shrink-swell, soluble rock and flooding (both riverine and groundwater) are available, but a comprehensive product able to support disaster mitigation measurement and land planning still does not exist. On this basis, a multi-hazard susceptibility analysis was completed with the added benefit of reducing the complexity and providing a methodological framework for multi-hazard estimation. The analysis was completed in a GIS environment through an Analytical Hierarchy Process (AHP) multicriteria decision-making process. Since the AHP method is affected by a user selection bias, a quantitative Relative significance index was derived to rank the AHP factors during the susceptibility estimation. This index suggests that flooding is the principal natural hazard for the Derwent Valley Mills UNESCO World Heritage site. The multi-hazard susceptibility map also indicates that most of the areas where the mills are located are subject to significant susceptibility to natural hazards.

Land subsidence due to underground resources exploitation is a well-known problem that affects many cities in the world, especially the ones located along the coastal areas where the combined effect of subsidence and sea level rise increases the flooding risk. In this study, 25 years of land subsidence affecting the Municipality of Ravenna (Italy) are monitored using Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) techniques. In particular, the exploitation of the new Sentinel-1A SAR data allowed us to extend the monitoring period till 2016, giving a better understanding of the temporal evolution of the phenomenon in the area. Two statistical approaches are applied to fully exploit the informative potential of the A-DInSAR results in a fast and systematic way. Thanks to the applied analyses, we described the behavior of the subsidence during the monitored period along with the relationship between the occurrence of the displacement and its main driving factors.