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Forest management is a complex topic at the interface between sustainability and the resilience of socioeconomic and environmental systems. The influence of market forces, supranational, country and regional policies, as well as climate change, on forest goods and services, is expected to increase in the near future. Such a complex interplay between economic and environmental forces is common to most European countries. The aim of this study is to operationally delineate and discuss the transition of the environmental sustainability and economic viability of forestry in Italy. This country encompasses the typical Mediterranean ecosystems with broad forest coverage in mountainous and hilly areas, where expanding woodland areas have been observed in the last decades mainly as a consequence of the decline of agropastoral activities, especially in disadvantaged and marginal areas. The consequent increase in wood biomass was frequently conceived as an element of environmental criticality, exposing woods to high vulnerability to wildfires and a consequent reduction in their economic value, possibly exacerbated by local warming. These dynamics usually took place in contexts where only a part of the overall forest heritage was subjected to regular management, despite the efforts made through various policies such as the Constitutional Law 3/2001 and the recent Law on Forests and Forestry Supply Chains. The latter policy tool, enhancing the concept of “active forest management” aimed to establish a sustainable approach to forest resources, leading to a broader forest area for formal planning and controlled harvesting. These dynamics took place in parallel with the inherent expansion in forest certification schemes formally promoting long-term environmental sustainability and a wider spectrum of forest ecosystems. Timber and non-wood materials and products are key elements from the perspective of achieving sustainable (climate-neutral) development in advanced economies.

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.

The Basilicata region summarizes many basic features of the biogeographic complexity characterizing Mediterranean countries. The intricate geomorphology and the long history of human management generated the current landscapes, which include both high-value ecosystems and areas prone to desertification. Preserving goods and services provided by such composite land cover mosaics poses many problems due to the interference/overlap of diverse natural and anthropic factors which make the correct selection of relevant parameters and the interpretation of observational data rather difficult. Here, we study interconnections between local climate and vegetation activity by correlating parameters characterizing the interannual statistics of the NDVI (Normalized Difference Vegetation Index), derived from satellite data, with a recently devised multivariate statistical index of meteoclimatic variability. We used a 15-year sequence of remote images concerning a set of plots located around meteorological ground stations of the central-eastern part of the region to pick up spatial structures in the vegetation–climate relationships. Our analyses were able to correlate spatial heterogeneity to variations in water exchanges between vegetation and atmosphere. This study represents a first step to improve the description of relevant processes to protect natural habitats and quality agriculture, therefore combating land degradation and climate change detrimental effects.

In order to characterize soil samples collected in two different agricultural areas of Potenza suburbs (Southern Italy) devoted to durum wheat cultivation, we used the combination of two techniques: magnetic susceptibility measurements and effective radium concentration obtained from radon emanation. Magnetic susceptibility measurements are generally used as a proxy method for monitoring heavy metals in soil. Radium concentration and radon exhalation rate from soil are used for characterizing different soils. First results suggest that effective radium concentrations in soils can be helpful for the interpretation of magnetic susceptibility field surveys.

Seasonality is a fundamental feature of environmental systems which critically depend on the climate annual cycle. The regularity of the precipitation regime, in particular, is a basic factor to sustain equilibrium conditions. An incomplete or biased understanding of precipitation seasonality, in terms of temporal and spatial properties, could severely limit our ability to respond to climate risk, especially in areas with limited water resources or fragile ecosystems. Here, we analyze precipitation data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) at 0.050 resolution to study the spatial features of the precipitation seasonality across different climate zones in Central-Southern Europe during the period 1981−2018. A cluster analysis of the average annual precipitation cycle shows that seasonality under the current climate can be synthesized in the form of a progressive deformation process of the annual cycle, which starts from the northernmost areas with maximum values in summer and ends in the south, where maximum values are recorded in winter. Our analysis is useful to detect local season-dependent changes, enhancing our understanding of the geography of climate change. As an example of application to this issue, we discuss the seasonality analysis in a simulated scenario based on IPCC projections.