The couple rapid urbanization and policy failure in controlling urban expansion was sometimes associated to soil and land degradation phenomena in both developing and developed countries. This work points to investigate the link between exurban development and soil/land degradation in the region of Athens (Greece) as a typical semiarid Mediterranean area experiencing a shift from a land-saving compact urban form to a dispersed, low-density urban expansion. The examined area is among the most populated areas in the Mediterranean basin showing an enormous population growth during the last 60 years. While low-density, dispersed urban settlements mainly developed over the decade (2000–2010) in the investigated area occupied mostly land classified at poor soil quality, the overall assessment of vegetation, climate and soil quality layers renders a complex picture in which exurban development consumed high-quality land that were classified as non-vulnerable to land degradation. On the contrary, compact urban settlements prevail in land with intermediate (or even high) soil quality but with inadequate climatic and vegetation conditions, which are classified as highly vulnerable to land degradation. Urban planning should integrate multi-dimensional indicators of soil, climate and vegetation quality to evaluate the environmental impact of exurban development.

The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current status of local vegetation and climate shows evident departures from the mean conditions of the investigated region. Our procedure was tested in Basilicata (Italy), which is a typical bio-geographic example of vulnerable Mediterranean landscape. We grouped Landsat TM/ETM+ NDVI and air temperature (T) data by vegetation cover type to estimate the statistical distributions of the departures of NDVI and T from the respective land cover class means. The pixels characterized by contextual left tail NDVI values and right tail T values that persisted in time (2002–2006) were classified as critical to land degradation. According to our results, most of the critical areas (88.6%) corresponded to forests affected by erosion and to riparian buffers that are shaped by fragmentation, as confirmed by aerial and in-situ surveys. Our procedure enables cost-effective screenings of complex areas able to identify raising hotspots that require urgent and deeper investigations.

The use of satellite data to detect forest areas impacted by extreme events, such as droughts, heatwaves, or fires is largely documented, however, the use of these data to identify the heterogeneity of the forests’ response to determine fine scale spatially irregular damage is less explored. This paper evaluates the health status of forests in southern Italy affected by adverse climate conditions during the hot and dry summer of 2017, using Sentinel-2 images (10m) and in situ data. Our analysis shows that the post-event—NDVI (Normalized Difference Vegetation Index) decrease, observed in five experimental sites, well accounts for the heterogeneity of the local response to the climate event evaluated in situ through the Mannerucci and the Raunkiaer methods. As a result, Sentinel-2 data can be effectively integrated with biological information from field surveys to introduce continuity in the estimation of climate change impacts even in very heterogeneous areas whose details could not be captured by lower resolution observations. This integration appears to be a successful strategy in the study of the relationships between the climate and forests from a dynamical perspective.

The spatial mismatch between population growth and settlement expansion is at the base of current models of urban growth. Empirical evidence is increasingly required to inform planning measures promoting urban containment in the context of a stable (or declining) population. In these regards, per-capita indicators of land-use change can be adopted with the aim at evaluating long-term sustainability of urbanization processes. The present study assesses spatial variations in per-capita indicators of land-use change in Rome, Central Italy, at five years (1949, 1974, 1999, 2008, and 2016) with the final objective of quantifying the mismatch between urban expansion and population growth. Originally specialized in agricultural productions, Rome’s metropolitan area is a paradigmatic example of dispersed urban expansion in the Mediterranean basin. By considering multiple land-use dynamics, per-capita indicators of landscape change delineated three distinctive waves of growth corresponding with urbanization, suburbanization, and a more mixed stage with counter-urbanization and re-urbanization impulses. By reflecting different socioeconomic contexts on a local scale, urban fabric and forests were identified as the ‘winner’ classes, expanding homogeneously over time at the expense of cropland. Agricultural landscapes experienced a more heterogeneous trend with arable land and pastures declining systematically and more fragmented land classes (e.g., vineyards and olive groves) displaying stable (or slightly increasing) trends. The continuous reduction of per-capita surface area of cropland that’s supports a reduced production base, which is now insufficient to satisfy the rising demand for fresh food at the metropolitan scale, indicates the unsustainability of the current development in Rome and more generally in the whole Mediterranean basin, a region specialized traditionally in (proximity) agricultural productions.

Satellite remote sensing is a highly valuable data source useful in the monitoring of surface water dynamics and an essential tool in flood risk management although several factors can interfere with the detection of water features. This study explores an approach that integrates satellite optical images and DEM-based hydrogeomorphic features to enhance real‐time identification of river flooding. Spectral indices, namely MNDWI and the combination of NDWI/NDTI, were taken from Sentinel-2 images and used to detect flooded areas, while DEMs were used to identify areas more exposed to river flooding using the Geomorphic Flood Index. The integration/overlapping of these layers allowed us to refine the final map with a significant detection improvement compared to those based exclusively on the segmentation of a spectral index. The proposed integrated approach was tested in five areas of the Piave river (Northern Italy) where a flooding event caused by the Vaia storm took place in October 2018. Results have been evaluated against reference maps produced manually in five selected areas, demonstrating good capabilities of the proposed procedure (mean accuracy 0.90), significantly reducing false alarms (Matthews Correlation Coefficient improvements among 0.02–0.43 in 4 out of 5 areas). The key advantages of such a procedure are the rapid application and the free and easy availability of the data that, in turn, allow for the investigation of large areas in the aftermath of an event.