In Mediterranean agropastoral areas, land abandonment is a key driver of wildfire risk as fuel load and continuity increase. To gain insights into the potential impacts of land abandonment on wildfire risk in fire-prone areas, a fire-spread modeling approach to evaluate the variations in wildfire potential induced by different spatial patterns and percentages of land abandonment was applied. The study was carried out in a 1200 km² agropastoral area located in north-western Sardinia (Italy) mostly covered by herbaceous fuels. We compared nine land abandonment scenarios, which consisted of the control conditions (NA) and eight scenarios obtained by combining four intensity levels (10, 20, 30, 40%) and two spatial patterns of agropastoral land abandonment. The abandonment scenarios hypothesized a variation in dead fuel load and fuel depth within abandoned polygons with respect to the control conditions. For each abandonment scenario, wildfire hazard and likelihood at the landscape scale was assessed by simulating over 17,000 wildfire seasons using the minimum travel time (MTT) fire spread algorithm. Wildfire simulations replicated the weather conditions associated with the largest fires observed in the study area and were run at 40 m resolution, consistent with the input files. Our results highlighted that growing amounts of land abandonment substantially increased burn probability, high flame length probability and fire size at the landscape level. Considering a given percentage of abandonment, the two spatial patterns of abandonment generated spatial variations in wildfire hazard and likelihood, but at the landscape scale the average values were not significantly different. The average annual area burned increased from about 2400 ha of the control conditions to about 3100 ha with 40% land abandonment. The findings of this work demonstrate that a progressive abandonment of agropastoral lands can lead to severe modifications in potential wildfire spread and behavior in Mediterranean areas, thus promoting the likelihood of large and fast-spreading events. Wildfire spread modeling approaches allow us to estimate the potential risks posed by future wildfires to rural communities, ecosystems and anthropic values in the context of land abandonment, and to adopt and optimize smart prevention and planning strategies to mitigate these threats.

Italy’s rural areas provide significant tourism opportunities which support local economies and sustainable development. This study examines the impact of socio-economic, technological, natural–ecological, and political–legal factors on rural tourism across Italy’s 21 regions. Using statistical data and correlation analysis, five macro factors were evaluated to determine their influence on the prevalence of agritourism as a measure of rural tourism development. The methodology involved a correlation analysis, including the assessment of bivariate relationships between variables, supported by robust statistical tests to ensure reliability. Key findings indicate that technological advancements, particularly in farm digitalization and innovation, are closely associated with rural tourism growth, alongside factors like regional GDP and natural–ecological resources. The study reveals that higher levels of farmer education and rich ecological and cultural assets within regions also positively impact rural tourism. These insights provide valuable guidance for policymakers and stakeholders in formulating strategies that promote rural tourism and regional development, emphasizing digitalization, ecological preservation, and education. This research extends our understanding of the factors driving rural tourism in Italy, offering a basis for targeted interventions that enhance economic resilience and support sustainable tourism in rural areas.

In this work, we will present the transboundary wildfire simulation system used to analyze fine-scale (100-m resolution) wildfire hazard and exposure in the regions of the Italy–France Maritime cooperation area (Sardinia, Corsica, Tuscany, Liguria and Provence–Alpes–Côte d’Azur), which covers about 93,000 km² of land. The study area is characterized by a variety of ecological, cultural, anthropic and touristic values that are heavily threatened by wildfires, mostly during the summer season. The wildfire issues in the area are expected to worsen in future years due to a number of contributing factors, including climate change, the abandonment of agrosilvopastoral areas with the resulting increase in fuel load and continuity and the expansion of urban interfaces into fire-prone wildlands. To perform our analysis, among the large set of wildfire spread models available, in the framework of the MED-Star project, we have selected the Minimum Travel Time (MTT) algorithm of Finney (2002) as implemented in FConstMTT. As inputs for our simulations, we produced underlying fuel maps derived from regional land use and forest maps and focused on the historical moisture and weather conditions and patterns associated with the largest wildfires that affected the study area in the last 20 years. The simulation outputs allowed us to characterize spatial variations in wildfire spread and hazard among and within provinces and regions and to identify the most significant hot-spot areas. In addition, we combined the simulated raster data with several explanatory variables and spatial layers (e.g., wildland–anthropic interface maps; provinces; climatic zones; etc.) to characterize exposure levels at different levels. Findings from this work improve regional awareness and knowledge about the spatial dynamics and patterns of wildfire exposure and hazard in the Italy–France Maritime cooperation area.

Wildfire simulators represent a fundamental tool for the planning of wildfire prevention operations, the real-time prediction of fire propagation and thus the adoption of appropriate firefighting strategies. In this paper, we provide an example of the application of the Web-based Wildfire Simulator (WWS) on fires that occurred during the 2021 fire season in Sardinia (Italy), and in particular on the Montiferru fire, a large fire that occurred in the central-western area of the island and mainly driven by the wind. The simulator used high-resolution data provided by the WRF meteorological model initialized with both GFS forecasts and ERA5-ECMWF reanalysis. Vegetation characteristics and associated fuel pattern maps were derived from the analysis of recent Sentinel2 satellite data. Fuel moisture data were estimated using weather data predicted by WRF. The evaluation of the model was carried out using the data collected in field and related to the actual fire propagation observed during the various time steps, and to the related firefighting interventions. In addition, the meteorological data recorded by a few meteorological stations located within the simulation domain and the data on fire severity collected in field were analyzed. The model was able to predict the temporal development of fire events with a good accuracy, considering the mean rate of spread and the rate of spread of the different wildfire sectors. Most of the accuracy can be explained by the goodness of the weather forecasts obtained through the use of GFS forecasts and of the fuel model map. The model was able to accurately predict the propagation of the main fire front, while several deviations from the real perimeter were observed with regard to the fire flanks propagation. In conclusion, wildfire simulators represent a useful tool for all phases of fire prevention, for their operational use in real-time, and therefore to provide information for the firefighting.

The purpose of the S2IGI (Sistema Satellitare Integrato Gestione Incendi) project is to support the different phases of wildland fire prevention and management by a set of toolboxes, aiming to provide a short- and medium-term forecast of wildfire danger, an early detection of wildland fires, real-time forecasting of wildland fire propagation, and an assessment of fire damages; all the services are based on the integration of: Earth observation (EO) advanced technologies of remote sensing; high-resolution weather numerical models; and a decision support system platform. The project can support the wildland fire management activities along the following three phases: (1) forecast and prevention, (2) monitoring, detection and suppression, and (3) damage assessment and vegetation recovery. One of the main products of the project is an integrated system for the early detection of ignitions, based on an improved version of the SFIDE algorithm, derived from the analysis of the MSG SEVIRI sensor data. The hot-spots provided by the SFIDE algorithm are automatically processed by a wildfire simulator in order to predict a probabilistic wildfire spread map, together with some themes related to the wildfire intensity. The modeling chain can be used for real-time or off-line applications. Again, the project provided a spatially consistent estimation of the wildfire risk and a map of burned areas updated on a weekly basis. The set of products is also completed by the generation of maps of areas suitable for prescribed fire interventions and, regarding the post-fire applications, by maps of the soil erosion risk.

This presentation will provide an overview of some of the recent improvements of the Sardinian Web-based Wildfire Simulator (WWS), a wildfire simulator developed to provide real-time support for wildfire management. We added a midflame wind velocity model integrated within the mass-consistent downscaling wind field module to provide high-resolution wind field maps affected by the vegetation characteristics. This is a generalization of the commonly used wind adjustment factor calculation, suitable for an improved parametrization of the wind calibration framework. The wind adjustment factor adjusts 10 m wind velocity to midflame wind, which affects the surface fire. The model includes a different algorithm for wind above surface fuel that is unsheltered by overstory and wind that is sheltered by forest canopy. In addition, a crown fire spread model was integrated within WWS to predict the onset of crowning, the type of crown fire (i.e., passive or active), and the associated rate of spread and fireline intensity. Crown fires are carried through the crowns of living forests and they are even less common than the surface fire; their impact is severe in terms of fire behavior and fire attack opportunities. We present the preliminary results of a validation of the new codes on a set of actual wildfires that recently occurred in the island of Sardinia, Italy.