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.

Effective landscape-scale fuel management strategies are essential for reducing wildfire risk in Mediterranean fire-prone areas. In this study, the minimum travel time (MTT) fire-spread algorithm as implemented in FlamMap was applied to assess the potential of alternative fuel treatments for lowering wildfire losses in a 5,740-ha study area in eastern Sardinia, Italy. Twenty-seven wildfires at 10-m resolution were simulated considering three wind speeds (15, 18, and 21 km h⁻¹) to compare fuel treatments: no treatment (NT), irrigated agroforestry areas with shrub clearing (T1), prescribed fire in eucalyptus stands (T2), and irrigated grasslands (T3). The simulations replicated a recent large wildfire that occurred in the study area (Orrì wildfire, 2019) and considered the weather and fuel moisture conditions associated with this event. The average wildfire exposure outputs (burned area, probability of burning, conditional flame length, potential crown fire occurrence, and surfaces withflame lengths above 2.5 m) decreased after fuel treatments, compared to no treatment. T1 was the most effective strategy in mitigating wildfire hazards and provided the most significant performance for several wildfire exposure indicators. Treating only 0.5% of the study area (~ 30 ha) resulted in a decrease in all wildfire exposure metrics to ~ 10% within the study area. In addition, the total surface characterized by high flame length (average > 2.5 m) was the lowest in the T1 treatment. This study can help land and fire managers optimize fuel treatment opportunities and wildfire risk mitigation strategies in Mediterranean areas.

We applied a geographical information system analysis to reclassify and characterize anthropic buildings based on structure density and area covered, land type, and proximity to wildlands able to originate intense wildfires and spot fires. The methodology was carried out in the 93,000 km² Italy-France Maritime cooperation area (which includes the Regions of Sardinia, Tuscany, and Liguria, in Italy, and Corsica, and Provence-Alpes-Côte d'Azur, in France). We produced a 100-m raster dataset that characterizes and maps medium-high anthropic presence, wildland-anthropic areas, dispersed anthropic areas, and non-anthropic zones, in the whole study area. The study allowed to highlight variations in wildland anthropic interfaces among and within Regions as a function of anthropic presence and types and the surrounding wildlands. The spatial dataset provided with this work represents a valuable contribution to support landscape and urban planning and inform strategies to limit wildfire impacts nearby anthropic areas.

This research analyzes the spatiotemporal patterns of wildfire regime attributes (e.g., seasonality, size, frequency, and burn rate) across the Golestan National Park (GNP), northeast Iran over the last two decades. We used a variety of data, including existing vegetation data, current vegetation survey, and historical wildfire data, and then data were processed through ArcMap. We also predicted fire exposure profiles (burn probability (BP), conditional flame length (CFL (m)), and fire size (FS (ha)) by the application of the minimum travel time (MTT) fire spread algorithm. The kernel density estimation (KDE) method was used to estimate wildfire likelihood, based on recent wildfires (2000–2020) that occurred in the GNP. Finally, we developed a logistic regression model to investigate how independent variables such as weather, fuel, and topographic data influence wildfires in the park. Wildfires in the landscape have not been constant in either space or time. Their extent, seasonality, frequency, and other wildfire regime characters varied considerably across the landscape. Our results highlighted that shrublands in the southern part of the park showed, in general, the highest values in terms of the wildfire regime attributes. Large fires (10–100 ha, 51%) and very large fires (>100 ha, 24%), fire intervals greater than 10 years (90%), and high burn rates (>1% y⁻¹, 35%) are all characteristics that contribute to high wildfire activity in shrublands. Similarly, areas predicted to have high wildfire exposure levels (average BP = 0.004; average CFL = 1.60 m; average FS = 840 ha) are found in the fuel models of high-load grass and medium-load shrub. Finally, the regression model results revealed that weather and fuel were the most influential parameters (R² ≥ 0.2), while topography had comparatively less influence in the study area. In light of these results, we suggest proactively incorporating this information into fire and fuel management which can help develop a fire prevention plan, predict fire ignition probability and frequency, and finally address altered fire regimes threatening the park.

Wildfires represent a major threat to Mediterranean ecosystems and are responsible for relevant impacts to environmental, economic and social values. In the period 2010-2016, the cross-border Interreg Italy-France Maritime territory, which includes Sardinia, Corsica, Tuscany, Liguria and PACA Regions, had about 20,000 wildfire ignitions and a total burned area of about 122,000 ha. In the face of social and environmental conditions and risks of the Maritime Regions, strengthening and developing innovative common guidelines and systems of wildfire management, from the monitoring and forecast to suppression, can provide more effective solutions to the wildfire problem, and can help strengthen cross-border cooperation in case of days with high risk. This work is devoted to introduce the MED-Star project, and to describe his main activities and results, with a focus on the tasks and activities coordinated by the National Research Council of Italy, Institute of BioEconomy (CNR-IBE) of Sassari. MED-Star is a 3-years strategic project supported by the Interreg Italy-France Maritime Program 2014-2020, which is co-financed by the European Regional Development Fund (ERDF). MED-Star is closely linked to 4 joint simple projects (Intermed; Med-Coopfire; Med-Foreste; Med-PSS), which mainly focus on investments in small infrastructures for wildfire risk prevention and support to wildfire suppression operations. The MED-Star project aims to share and discuss fire management policies and the most advanced strategies that can reduce the risk associated with wildfires, also through the combination of joint action plans and pilot / demonstration actions. The partnerships of MED-Star and the related 4 simple projects include the main actors competent at the administrative, technical and scientific level on the wildfire topic in the Maritime area of cooperation, and are able to meet the abovementioned challenges, contributing to 1) the reduction of wildfire risk in the five Regions involved, 2) the definition of strategic and operational solutions, 3) the implementation of operational actions and investments for wildfire prevention, monitoring, forecast and suppression, and 4) the strengthening of joint early warning and risk monitoring systems.

On 29 October 2018, the Vaia storm hit the Alpine forests of Italy with winds at 200 km h⁻¹. The forest areas totally or partially damaged by the storm were about 41,000 ha, of which about 12,000 ha were in the Veneto Region. Large areas and stands of spruce and beech forests, usually not very prone to wildfires, were blown down. Only limited portions of areas with downed trees were harvested in the first year after the storm. Due to the changes in surface fuel conditions, timber understory fuel models in the area affected by the storm were converted to slash-blowdown models, which results in significant short-term changes in potential wildfire behavior. To investigate how wildfire behavior and spread were affected by the above changes, we applied wildfire simulation modeling to assess the variations in wildfire exposure characteristics (such as burn probability and conditional flame length) before and after the storm in the three Veneto provinces (Belluno, Treviso, and Vicenza) where the blown down trees were concentrated. We simulated 50,000 fire events using the MTT algorithm as implemented in Randig, and considering the historical wildfire occurrence data in the study area. Regarding fuel input data for the simulations, we modified the existing fuel map (pre-storm) of the Veneto Region and modified the fuel models of the damaged forest areas considering different standard models of slash, according to the percentage of downed trees. The results highlighted spatial variation in wildfire exposure before and after the storm, with an overall increase of wildfire risk conditions in those areas characterized by both significant levels of blowdown and high historical likelihood of fire ignitions. The methodology proposed and the findings of this study can be useful to inform future wildfire risk and land management in the areas affected by the storm.