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We implemented a fine-scale fire modeling approach to assess wildfire exposure in the highly valued resources and assets (HVRAs) of Ardabil Province (18,000 km(2)), northwestern Iran. For this purpose, we used the minimum travel time algorithm and simulated 60,000 wildfires under wildfire season most frequent weather scenarios. Wildfire exposure was analyzed on different vegetation types and municipalities using burn probability (BP), conditional flame length (CFL), and fire size (FS) modeling outputs. Also, we obtained the fire potential index (FPI) and source-sink ratio metrics to assess wildfire transmission across the study area. The BP ranged from 0.0003 to 0.013 (mean = 0.0008) and varied substantially among and within the HVRAs of the study area. While the lowest BP values located in broadleaf forests, the highest BP values concentrated on flashy fuel areas, including cereal crops, mountain meadows, and grazed pastures. The average CFL was 0.3 m, with the highest values peaking in cereal crops and wooded pastures located on slopes. FS ranged from about 1-1700 ha, with an average value of 225 ha. Fires ignited in the northern part of the study area resulted in the most significant FS values, due to the large contiguous patches of high fuel loads. High FPI values were associated with large fire ignition areas and anthropic fire occurrence hotspots in the northern and southern parts of the study area. Cereal crops and grazed pastures behaved as relevant wildfire sources of fires exposing rural communities. The results of this study may help support the development of an improved wildfire risk management policy in the study area. The methods from this study could be replicated in neighboring areas and other cultural landscapes of the Middle East, where wildfires pose a threat to human assets and natural values.

Forest stands plays an important role in Western Mediterranean ecosystems, their characterization it is needed for a whole comprehension of natural dynamics and for an efficient forestry management. The definition of the structural parameters of forest vegetation is a useful information for different environmental applications, such as studies on carbon dynamics, sustainable forest management, ecological studies, forest fuel studies and fire risk management. A precise description of the forest is particularly important for fire hazard mitigation planning because allows predictions of the potential fire behavior and its destructive effects. Obtaining detailed information on forest stand and canopy variables requires extensive, difficult, and laborious field campaigns. Remote and proximal sensing techniques for forest monitoring have become popular in recent decades. Specifically, Terrestrial Laser Scanner (TLS), based on Lidar technology, has demonstrated its potential to overcome the limitations of the conventional ground-based forest inventory techniques, but the accuracy and applicability of TLS techniques for estimation of tree attributes and canopy characterization, presupposes a correct separation between points representing shrubs, woody material, leaves and small branches and needs further investigations. In this work we developed and tested an automatic procedure based on the point density algorithms DBSCAN, to correctly separate points representing shrubs, woody material, leaves, and small branches at plot level, in order to identify woody material volumes, tree density and canopy cover on a forest stand. The study was carried out in several areas located in Sardinia, Italy, mainly covered by pine forest, mixed forest, and oak forest with different understory types. Destructive and non-destructive measurements were done inside circular plots of 10 m radius. TLS data sets were collected in field by multiple scanning of the plots. The 3D point clouds were processed for isolating trees, ground and understory and subsequently for separating wood from foliage. Cloud points were partitioned in cubic volumes (voxels) that were used as input to separate stand components (by applying principal component analysis) and to generate wood and no-wood clusters (by applying the point density algorithm DBSCAN). The first results obtained show that the proposed method allows to correctly identify foliage, trunk and main branches especially when the underlying layer is dominated by low herbaceous vegetation. However, further studies are needed to assess the ability of this method in forest stands characterized by high and dense undergrowth and with different species of trees.

Fire exposure describes the spatial juxtaposition of values with fire behaviour in terms of likelihood and intensity. Wildfire exposure analysis is based on the estimation of the potential wildfire intensity and on the burn probability. Fire modelling can produce spatially explicit information on fire spread and behaviour, and offers a feasible method to simulate, map, and analyse fire exposure. FlamMap Minimum Travel Time (MTT) algorithm (Finney, 2006) was used to conduct wildfire simulations considering historical data of fuel moisture conditions and winds, as well as the most frequent wind directions and historical ignition locations (2005-2018). Analysis was conducted on spatial and quantitative variations in selected fire hazard and exposure factors, namely Burn Probability (BP), Conditional Flame Length (CFL) and Fire Size (F). We observed pronounced spatial variations among and between municipalities in the factors, especially for those in the northern and southern parts of Ardabil. The variations across the burnable area of the municipalities can be fundamentally related to a number of factors, including spatial variation in ignition locations, fuel moisture and load, weather conditions, and topography of the terrain. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.

Este trabajo trata del Capacidad predictiva del Índice de Haines (HI) en relación al desarrollo de grandes incendios forestales en Cerdeña, Italia.

The fire danger (FD) defines the conditions less or more favourable for a fire ignition success and its propagation. FD indexes, that integrates environmental variables related to FD in more or less complex equations and systems, are widely used in wildfire prone countries for both scientific and operational purposes. Assessing the performance of FD indexes is challenging and this issue is quite debated within the fire community, which has been trying to apply several methodologies to evaluate FD indexes. The main aim of this work is to give a contribution to this effort. The analysis was conducted using data from a fire-prone Mediterranean area (Sardinia island, Italy), where 8 FD indexes were evaluated and compared using different statistical approaches. We calculated the daily FD values for the period 2000-2007 over the study area. A set of statistical tools (namely Spearman rank correlation, Index Value Distribution and Percentile Analysis, and Logistic Regression) were applied to evaluate the performance of each FD index by comparing FD values with fire occurrence indicators. The statistical tests revealed a large variability in FD indexes performance, depending also on fire activity conditions. Our results showed that two of the tested FD indexes reached a good overall performance. Findings from this study can help both the scientific community and local fire managers, supporting the evaluation of early warning systems and fire prevention strategies in the Mediterranean Basin.

Wildfire spread and behavior can be limited by fuel treatments, even if their effects can vary according to a number of factors including type, intensity, extension, and spatial arrangement. In this work, we simulated the response of key wildfire exposure metrics to variations in the percentage of treated area, treatment unit size, and spatial arrangement of fuel treatments under different wind intensities. The study was carried out in a fire-prone 625 km2 agro-pastoral area mostly covered by herbaceous fuels, and located in Northern Sardinia, Italy. We constrained the selection of fuel treatment units to areas covered by specific herbaceous land use classes and low terrain slope (

High severity wildfires can have many negative impacts on ecosystems. In this work, we coupled wildfire spread and erosion prediction modelling to evaluate the effects of fuel reduction treatments in preventing soil runoff in Mediterranean ecosystems. The study was carried out in a 68,000-ha forest area located in Northern Sardinia, Italy. We treated 15% of the study area, and compared no-treatment conditions vs alternative strategic fuel treatments. We estimated pre- and post-treatment fire behaviour by using the Minimum Travel Time (MTT) fire spread algorithm. For each fuel treatment scenario, we simulated 25,000 wildfires replicating the historic weather conditions associated with severe wildfires in the area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within the fuel treatment scenarios tested. The treatments realized nearby roads were the most efficient. We also evaluated the effects of other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimize proactive risk management activities aimed at reducing post-fire erosion in Mediterranean areas.

Surface biomass characterization plays a key role in wildfire management. It allows classifying vegetation fuels flammability for fire risk analysis, to define silvicultural prescriptions for fire hazard reduction, to plan prescribed burning, or to model fire behavior and its effects, such as greenhouse gas and pollutant emissions. To facilitate fuel classification and analysis of potential fire behavior and effects in Italy, we harmonized 634 measurements of surface wildland fuels from Alpine, temperate and Mediterranean environments. The dataset provides quantitative data for duff, fine dead fuels and downed woody material, live grasses and shrubs fuel components. Surface fuel data were harmonized by subdividing loads (Mg ha-1) to standard size classes for dead (0-6, 6-25 and 25-75 mm) and live (0-6, 6-25 mm) fuels, collecting percent cover and depth/height (cm) of the various fuel components, and classifying observations into 19 fuelbed categories. To ensure comparability with existing vegetation classification systems, we classified each observation according to the European Fuel Map, the Corine Land Cover classes (level IV), the European Forest Types, and the forest categories of the Italian National Forest Inventory. The dataset and a photo description of each fuelbed category are available as Supplementary material. This dataset is the first step to develop several products at the national scale such as: (i) fuel type classification and mapping; (ii) carbon stock and wildfire emission estimates; (iii) calibration of fuel models for the simulation of fire behavior and effects.

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.

Wildland fires represent a major threat to Quercus suber L. ecosystems, which provide relevant socioeconomic and ecological services in the Mediterranean Basin. In this work, we analyzed recent wildland fire dynamics in cork oak woodlands along the fire-prone areas of Sardinia (Italy), Corsica (France) and Catalonia (Spain). We first characterized geographic extent and main characteristics of cork oak woodlands in these regions and analyzed how environmental (climate and elevation) and socioeconomic factors (population and land uses) vary in the areas covered by Quercus suber L. We then evaluated how wildfires affected cork oak stands and, by logistic regression analysis, to what extent wildfires in cork oak areas were related to the above set of environmental and anthropic explanatory variables. Results revealed specific variations across study areas in cork oak characteristics as well as in environmental and social factors. We highlighted the spatial and temporal patterns of wildfires on cork oak woodlands, in terms of extent, seasonality, frequency and main driving factors. In the period 2003–2015, the percentage of cork oak woodlands burned ranged from 3.42% in Corsica to 11.30% in Sardinia. A few large and severe wildfires accounted for most of the area burned in cork oak woodlands. The most significant predictive variable that explained the spatial variation in wildland fire ignitions inside or nearby cork oaks was summer precipitation, while the weight of other factors varied depending on the region. This study provides evidence on recent fire dynamics in cork oak woodlands and gives valuable information and insights for the implementation of forest management and planning strategies in the Mediterranean area. This work was supported by the Sardinia Region under the project “Multifunzionalità delle foreste a quercia da sughero in Sardegna” (Multifunctionality of cork oak forests in Sardinia) funded within L.R. n. 7/2007 “Promozione della ricerca scientifica e della innovazione tecnologica in Sardegna,/Tender no. 2″, and by the “Proterina-2” Project funded by the Italia Francia-Marittimo Programme.

The development of comprehensive fire management and risk assessment strategies is of prominent concern in Southern Europe, due to the expanding scale of wildfire risk. In this work, we applied simulation modeling to analyze fine-scale (100-m resolution) wildfire exposure and risk transmission in the 24,000 km2 island of Sardinia (Italy). Sardinia contains a variety of ecological, cultural, anthropic and touristic resources that each summer are threatened by wildfires, and represents well the Mediterranean Basin environments and conditions. Wildfire simulations based on the minimum travel time algorithm were used to characterize wildfire exposure and risk transmission in terms of annual burn probability, flame length, structures exposed and type and amount of transmission. We focused on the historical conditions associated with large (>50 ha) and very large (>200 ha) wildfires that occurred in Sardinia in the period 1998–2016, and combined outputs from wildfire simulation modeling with land uses, building footprint locations, weather, and historical ignition data. The outputs were summarized for weather zones, main wind scenarios and land uses. Our study characterized spatial variations in wildfire spread, exposure and risk transmission among and within weather zones and the main winds associated with large events. This work provides a novel quantitative approach to inform wildfire risk management and planning in Mediterranean areas. The proposed methodology can serve as reference for wildfire risk assessment and can be replicated elsewhere. Findings can be used to better understand the spatial dynamics and patterns of wildfire risk and evaluate expected wildfire behavior or transmission potential in Sardinia and neighboring regions.

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981–2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981–2010, 2011–2040, and 2041–2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041–2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.

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We used a fire simulation modeling approach to assess landscape scale wildfire exposure for highly valued resources and assets (HVR) on a fire-prone area of 680 km(2) located in central Sardinia, Italy. The study area was affected by several wildfires in the last half century: some large and intense fire events threatened wildland urban interfaces as well as other socioeconomic and cultural values. Historical wildfire and weather data were used to inform wildfire simulations, which were based on the minimum travel time algorithm as implemented in FlamMap. We simulated 90,000 fires that replicated recent large fire events in the area spreading under severe weather conditions to generate detailed maps of wildfire likelihood and intensity. Then, we linked fire modeling outputs to a geospatial risk assessment framework focusing on buffer areas around HVR. The results highlighted a large variation in burn probability and fire intensity in the vicinity of HVRs, and allowed us to identify the areas most exposed to wildfires and thus to a higher potential damage. Fire intensity in the HVR buffers was mainly related to fuel types, while wind direction, topographic features, and historically based ignition pattern were the key factors affecting fire likelihood. The methodology presented in this work can have numerous applications, in the study area and elsewhere, particularly to address and inform fire risk management, landscape planning and people safety on the vicinity of HVRs.

We analyzed wildland fire occurrence and size data from Sardinia, Italy, and Corsica, France, to examine spatiotemporal patterns of fire occurrence in relation to weather, land use, anthropogenic features, and time of year. Fires on these islands are largely human caused and can be attributed to negligence, agro-pastoral land use, and arson. Of particular interest was the predictive value of a fire weather index (FWI) that is widely used by fire managers to alert suppression crews. We found that an increase in the FWI from 30 to 60 produced on average an approximate eightfold increase in the odds of a large fire, regardless of the time of year during the fire season or land use type. Total area burned per fire season was positively correlated with the number of days with FWI > 40 over the period studied. Strong interactions between time of year and land type were also observed for both the probability of ignition and large fire. For example, the estimated odds of a large fire on agricultural lands in southern Sardinia was approximately 10 times larger than the forest and shrubland land type for areas close to roads, early (May) in the fire season. Conversely, toward the end of the fire season (September), we estimated the odds of a large fire in these same areas at about half the value estimated for the forest land classes. Of the explanatory variables analyzed, only FWI had an effect on the probability of a large fire (P

In the last decades a rapid and intense development of the tourism industry led to an increasing of anthropic pressure on several coastal areas of Sardinia. This fact not only modified the coastal aesthetics, but has also generated an increase of risk for the environment. This phenomenon affected also the ancient structure of the landscape with a negative impact mainly caused by the following factors: land abandonment, wildfire occurrence, post-fire erosion, urbanization. These regional changes can be analyzed in detail by considering the geo-diachronic dynamics. The main objectives of this work were i) to perform a diachronic analysis of land use and land cover dynamics, ii) to analyse the recent dynamics of wildfires, and iii) to predict the soil erosion risk in relation to land use change occurred between the 1950s and the 2000s. The study was realized in a coastal area located in North-East Sardinia where the geo-historical processes were summarized and organized in a geographic information system that has been employed to examine the landscape variations at three different time steps: 1954, 1977 and 2000. In addition, different scenarios of wildfire propagation were simulated by FlamMap in order to estimate the spatial pattern of fire danger factors in the study area. Afterwards, maps of post-fire soil erosion were produced to identify the temporal and spatial variations of the erosion risk. The results show how the changes in land use and the significant and rapid increase of the residential areas affect the risk of both wildfires and post-fire soil erosion. The study reveals the capabilities of this type of approach and can be used by management agencies and policy makers e in sustainable landscape management planning. This approach can be extended to other regions of the Mediterranean basin characterized by complex interactions among landscape and anthropic factors affecting the environmental risk.

Wildfires are known to change post-fire watershed conditions such that hillslopes can become prone to increased erosion and sediment delivery. In this work, we coupled wildfire spread and erosion prediction modelling to assess the benefits of fuel reduction treatments in preventing soil runoff. The study was conducted in a 68000-ha forest area located in Sardinia, Italy. We compared no-treatment conditions v. alternative strategic fuel treatments performed in 15% of the area. Fire behaviour before and after treatments was estimated by simulating 25000 wildfires for each condition using the minimum travel time fire-spread algorithm. The fire simulations replicated historic conditions associated with severe wildfires in the study area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within fuel treatment scenarios. The most efficient treatment alternative was that implemented near the road network. We also evaluated other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimise proactive risk management activities intended to reduce post-fire erosion.

This paper investigates relationships between the emission of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) from five representative Mediterranean plant species and bio-polymers extracted from the leaves. The experimental results showed that there are differences in bio-polymer composition according to the species. Erica arborea is the fuel that contains most of holocellulose (cellulose and hemicellulose) and Pinus laricio is the fuels containing most of lignin. The plant samples were burned using an epiradiator. Automated Thermal Desorption–Gas Chromatography–Flame Ionization Detector (ATD–GC–FID) and Automated Thermal Desorption-Gas Chromatography/Mass Spectrometry (ATD–GC/MS) analysis identified 71 compounds, the compounds emitted from ligno-cellulosic fuel are VOCs and SVOCs. These emissions represented from 1164.5 ± 163.4 to 1437.9 ± 244.0 μg g −1 dw of combustible fuel. There are not significant differences in the total emissions according to the species, but differences in the proportions of lignin, cellulose, hemicellulose and extractives led to quantitative variations in the class compounds present in the smoke. Partial Redundancy Analysis (RDA) highlighted presence of strong relationships between the emissions of some subfamilies of compounds and the proportions of the various bio-polymers.

The use of spatially explicit fire spread models to assess fire propagation and behaviour has several applications for fire management and research. We used the FARSITE simulator to predict the spread of a set of wildfires that occurred along an east–west gradient of the Euro-Mediterranean countries. The main purpose of this work was to evaluate the overall accuracy of the simulator and to quantify the effects of standard vs custom fuel models on fire simulation performance. We also analysed the effects of different fuel models and slope classes on the accuracy of FARSITE predictions. To run the simulations, several input layers describing each study area were acquired, and their effect on simulation outputs was analysed. Site-specific fuel models and canopy inputs were derived either from existing vegetation information and field sampling or through remote-sensing data. The custom fuel models produced an increase in simulation accuracy, and results were nearly unequivocal for all the case studies examined. We suggest that spatially explicit fire spread simulators and custom fuel models specifically developed for the heterogeneous landscapes of Mediterranean ecosystems can help improve fire hazard mapping and optimise fuel management practices across the Euro-Mediterranean region.

The goal of this work is to evaluate by a modeling approach the effectiveness of alternative fuel treatment strategies to reduce potential losses from wildfires in Mediterranean areas. We compared strategic fuel treatments located near specific human values vs random locations, and treated 3, 9 and 15% of a 68,000 ha study area located in Sardinia, Italy. The effectiveness of each fuel treatment was assessed by simulating 25,000 wildfires using the MTT fire spread algorithm. The simulations replicated severe wildfires observed around the study area, using historic weather and fuel moisture conditions (97th percentile). Wildfire exposure profiles for the study area as a whole and for locations with specific values of interest were analyzed. Results indicated significant variations in wildfire exposure among and within the fuel management strategies and treatment intensities. The simulated mitigation strategies substantially decreased the average wildfire exposure with respect to the untreated condition, and this effect was unequivocal for all strategies. Increasing the percentage of land treated improved the effectiveness of all fuel treatment strategies. The strategy based on road protection provided the highest performances for several wildfire exposure indicators. The methodology presented in this work can be applied to facilitate the design of fuel management programs and support policy decisions to address growing wildfire risk in the region. This work is one of the first applications of fire simulation modeling to evaluate fuel management effectiveness on wildfire risk mitigation in the Mediterranean areas. (C) 2016 Elsevier B.V. All rights reserved.

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981-2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981-2010, 2011-2040, and 2041-2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041-2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.

Providing forecast of water balance components such as precipitation, evapotranspiration, deep percolation and runoff is important for water management and irrigation scheduling. Reference evapotranspiration (ETo) prediction will greatly enhance our capability to manage high-frequency irrigation systems and shallow-rooted crops. Reference evapotranspiration can be calculated on daily or hourly basis using analytical models (Penman-Monteith, Penman, etc.) and meteorological forecasts from numerical weather prediction models. One can also use time series analysis of ETo and meteorological variables related to evapotranspiration process. For example, autoregressive integrated moving average (ARIMA) models and artificial neural networks (ANN) can be applied in time series modeling and forecasting. The main aims of this study were to analyze and compare the performance of the above-mentioned techniques in short-term prediction of hourly and daily ETo. Reference evapotranspiration rates were calculated using the hourly Penman-Monteith equation, weather data provided by the Agrometeorological Service of Sardinia, Italy (SAR), and weather forecasts from a limited area model (BOLAM2000). Both ARIMA and ANN models were developed using four years of hourly meteorological data from three meteorological stations of SAR. Models were validated using a two-year data set from the same locations. The accuracy of models was evaluated comparing the forecasts with ETo values calculated using observed weather data from SAR weather stations. The use of meteorological variables from numerical weather forecast gave better results than those obtained from ARIMA and ANN models. The Limited Area Model gave root mean squared difference values of the forecasted ETo smaller than 0.15 mm on a hourly basis and near 1.0 mm on a daily basis. However, the analysis showed a large scatter of calculated versus predicted ETo values, in particular for hourly values. The evaluation of the effect of weather forecast variables on forecast ETo accuracy showed that solar irradiance is the main parameter affecting ETo forecast.

Wildfire regime in Sardinia has been historically affected by the socio-economic conditions and their relationships with meteo-climatic conditions. A recent abrupt change on fire regime was observed at the end of 1970s, when socio-economic variations, mainly land abandonment and land use changes, were responsible of an increase in fire occurrence and in large catastrophic wildfires. This caused serious damages to forestry, agricultural and anthropic values, but in particular losses of human lives. The analysis of fire time series showed a large inter-annual variability of the burned areas and, as in other countries of the Mediterranean basin. Such variability is mainly due to the occurrence of wildfires with extreme behaviour concentrated in a limited number of days. These days are typically characterised by the advection of warm and dry air masses from North Africa. The aim of this project is (1) to study the synoptic patterns associated with extreme wildfire behaviour, (2) to propose a set of tools for the prediction of wildfire behaviour considering different weather scenarios, (3) to estimate the economic costs potentially associated with large wildfires, and (4) to characterize wildfire exposure and risk at regional and local scale. In order to provide the tools to predict fire behaviour some experimental activities were devoted to the comparison of different methods to obtain the simulation of wind field using both a mass-consistent model and a computational fluid dynamics models from the initial conditions provided by mesoscale meteorological models with different schemes of nesting and resolutions.

The goal of this study is to analyze fire probability and vulnerability of urban settings in a study area of about 46,000 ha located in a coastal area of North-East Sardinia. The study was carried out in a fire-prone area characterized by predominant and dense shrubland vegetation, by extensive rural-urban interfaces and by a relevant touristic pressure in the summer season. The analysis was performed using both the characterization and mapping of housing units, and the modeling of fire behavior by a probabilistic approach. The first part of the work was based on the analysis of the aerial photographs and field surveys. With this approach, we evaluated the vulnerability to fire of about 13,000 housing units through the evaluation and characterization of five factors: type of vegetation, percentage of the housing units in contact with the vegetation within a buffer area of 10 meters, presence of flammable structures, distance of flammable structures from the houses, and percentage of the flammable structures in contact with the vegetation, within a buffer area of 10 meters. The results highlighted that about 58% of the housing units were located in urban settings, while the remaining 42% were classified as rural-urban interfaces. About the latter, only 25% of units showed a reasonable distance from the surrounding vegetation, and 30% of units presented more than half of their perimeters in contact with the vegetation; in addition, 7% of units were close to highly burnable structures. Afterwards, we used simulation modeling based on the MTT algorithm of FlamMap in order to estimate the spatial variation of the main fire probability and severity indicators. Then we selected 32 different scenarios able to represent the most common and critical conditions for fire occurrence and spread in the study area. In more details, we combined two wind directions (210° and 290°), two wind speeds (32 and 20 km h-1), two fuel moisture conditions (arid and extreme), two ignition patterns (historically based and random), and two active fire spread durations (1 and 3 hours). For each scenario, we simulated 10,000 fires. Overall, the analysis of the fire simulations showed that fuel moisture and ignition patterns were the main factors affecting burn probability. Furthermore, the combination of extreme fuel moisture and strong wind speed represented the most critical scenario. The maps of conditional flame length were strongly affected by vegetation types and topography, particularly in some specific areas and nearby the coastline. Also the fire size maps highlighted hot-spot areas able to originate very large fires, particularly when wind direction and slope effects were compounded. We finally combined the results of the WUI mapping and characterization with the fire simulations, and we identified and quantified the level of exposure and risk of the housing units. As expected, the houses surrounded by shrublands and complex topography, particularly if isolated, showed very high fire exposure profiles, while the lowest exposure was observed in compact villages and towns.

Information on forest canopy structure is required at a wide range of spatial scales for several environmental applications (ecosystem productivity model, ecological and forest management, disease and stress detection, fuel properties). Aerial laser scanner (ALS) demonstrated to be a promising techniques and an important source of accurate data and information in forestry studies. More recently several studies reported different potential applications of the terrestrial laser scanner (TLS) for forest stand and canopy variables estimation. The two systems can be seen as mutual: the plotwise forest data, collected through TLS, can be used as a reference for the calibration of large-area inventory data measured by aerial and space-borne remote sensing techniques, by ALS or multispectral scanners. TLS technology can represent an alternative to overcome limitations of the conventional ground based techniques, that are time consuming and characterized by a limited accuracy. TLS allows the collection of high resolution point clouds, which can potentially and productively be used to derive tree attributes. Post-processing of TLS point clouds enables extensive analysis of data by automatic or semi-automatic methods specifically developed. However, the operational use of TLS techniques for vegetation characterization of broadleaf forests needs further investigations. In particular, discriminating between laser pulses that were intercepted by woody material, leaves and small branches is a key factor to improve the accuracy of tree and canopy description. The main objective of this work was to develop a segmentation method for broadleaf tree species in order to automatically (or semi-automatically) extract branches and stems from foliage. A voxel-based approach was developed and tested using a TLS data set collected in field by multiple scanning on four cork oak trees. After using noise reduction filters, voxels were used as input to generate clusters through a point density algorithm. Clustering process led to the identification of wood and leaf voxels. Points belonging to each voxel were then classified and quantified as wood, foliage and noise. Experimental results show that the segmentation algorithm can accurately discriminate wood and foliage clusters and consequently give the points of cloud associated to foliage, trunk and main branches. In conclusion, the method proposed seems to be a promising approach for improving the estimate of both canopy density distribution and woody material volumes In addition, our findings suggest that the terrestrial laser scanner (TLS) can be conveniently applied to characterize forest canopy fuel characteristics at plot level.

This contribution, which presents results of the work being currently in progress, is an update of our presentation in the previous ICFBR conference held in 2011 in Alghero. The updates consist in (i) using new GCM data (CMIP5 are used instead of the old CMIP3), (b) inclusion of the drought indices into the climate change projection (motivated by the fact that the wildfire risk is closely correlated with drought occurrence), (iii) updated stochastic weather generator used to simulate Fire Weather Index (FWI), and (iv) enhanced weather-friendly climate change scenarios, which include changes in climate characteristics not accounted for in our earlier experiments. The contribution will consist of two parts. In the first part, the CMIP5 data are used to create the multi-GCM climate change scenarios for the whole of Mediterranean in terms of temperature, precipitation and selected drought indices (including PDSI and SPEI). In the second part, the climate change impacts in terms of FWI characteristics and set of drought-related indicators will be determined for stations in four south European regions (Sardinia, Apulia, Croatia and Spain). The input weather series will be produced by the updated stochastic weather generator M&Rfi. To represent the future climate, the WG parameters will be modified by GCM-based climate change scenarios. We will focus on two aspects of the methodology: (a) Validity of WG, which will be examined by comparing the indicators obtained with the synthetic weather series vs observed weather series. Various settings of the WG will be considered to optimize its model. (b) Climate change scenarios will include changes in low frequency (monthly) and high-frequency (diurnal and interdiurnal) weather variability, which is mostly not taken into account in the climate change impact studies. We will assess an effect of these changes on results of our impact experiment.

Wildland fires represent an important disturbance for ecosystems in the Mediterranean Basin. Although fire spread and behaviour are dependent on several factors, the water content of live fuel plays an important role in determining fire occurrence and spread, especially in the Mediterranean shrubland, where live fuel is often a relevant component of the available fuel which catches fire. In live plant, water content patterns are related to both environmental conditions (e.g. meteorological variables, soil water availability) and ecophysiological characteristics of the plant species. In addition, more prolonged drought seasons induced by climatic changes is likely to have important repercussions on fuel water status. As a consequence of these remarks, seasonal monitoring of vegetation moisture content, relative to the whole vegetation and the single species, in conjunction with better knowledge of the relationships between weather (i.e. air temperature rainfall, drought) and live fuel moisture content dynamic, can contribute to better understand the effects of prolonged drought season on fuel water status. The aims of this work were i) to analyze the influence of both weather seasonality and inter-annual weather variability on live fuel moisture content within and among some common Mediterranean species, and ii) to investigate the effects of both inter-annual weather variability and prolonged drought season on live moisture content dynamic. The study was carried out in North Sardinia (Italy). Moisture content of live fuel (LFMC) was determined periodically for 8 years on several shrub species. Meteorological variables were also recorded. Relationships between live fuel moisture content and environmental conditions (i.e. rainfall, air temperature and soil moisture) were investigated and the effects of different lengths of drought season on LFMC pattern were analyzed. This work received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement 243888 ("FUME" Project - Forest fires under climate, social and economic changes in Europe, the Mediterranean and other fire-affected areas of the world).