Every year worldwide some extraordinary wildfires occur, overwhelming suppression capabilities, causing substantial damages, and often resulting in fatalities. Given their increasing frequency, there is a debate about how to address these wildfires with significant social impacts, but there is no agreement upon terminology to describe them. The concept of extreme wildfire event (EWE) has emerged to bring some coherence on this kind of events. It is increasingly used, often as a synonym of other terms related to wildfires of high intensity and size, but its definition remains elusive. The goal of this paper is to go beyond drawing on distinct disciplinary perspectives to develop a holistic view of EWE as a social-ecological phenomenon. Based on literature review and using a transdisciplinary approach, this paper proposes a definition of EWE as a process and an outcome. Considering the lack of a consistent “scale of gravity” to leverage extreme wildfire events such as in natural hazards (e.g., tornados, hurricanes and earthquakes) we present a proposal of wildfire classification with seven categories based on measurable fire spread and behavior parameters and suppression difficulty. The categories 5 to 7 are labeled as EWE.

Five methods for the evaluation of fire danger, based on meteorological factors, were tested using statistical data for daily number of fires and burned area from six different regions in France, Italy and Portugal, for a period of 3-9 years, both for Winter and Summer fires. A normalised scale for the output of the various methods was adopted to compare the variation of number of fires and burned area predicted by each method. Based on a statistical test, the relative performance of the various methods is assessed. From the five methods that were analysed in this research, the Canadian and the modified Nesterov methods showed the best overall performance.

Wildfire fatalities remain a significant problem in Mediterranean Europe. Although there is a strong inter-annual variability with regard to their number, repeated tragic accidents remind us of this grim occurrence, despite the increasing firefighting capacity aimed to improve human safety. In this paper, we present an analysis of the 865 fatalities caused by wildfires in the 1945–2016 period. Data originating from national databases were merged, contextual and weather factors related to the accidents that caused these deaths were documented and analysed to explore probable relationships with the number and type of fatalities. Results show a major rise of fatalities in late 1970s in the four regions of Greece, Sardinia (Italy), Spain and Portugal. Fatalities present a strong seasonality in summer months, as expected. Overall, Spain has the highest absolute numbers of fatalities; however, normalisations by population, and burned and forest area show that annual number of fatalities is comparatively smaller. Certain other factors showed correlation with mortality. Civilians were the most affected group in Greece (65%) and Sardinia (58%), but not in Spain and Portugal. Findings indicate that an in-depth revision of fire-management policies and practices is required, with emphasis on prevention planning in urban areas, and better training of the firefighting resources.

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.

Field measurements of moisture content of several fine fuels (shrub vegetation and live foliage) were performed in Central Portugal and in Catalunya (NE Spain) for 1–10 years. Seasonal and interannual variation of live fine fuels of several species in two regions of the Iberian Peninsula are analysed. The species were grouped in three sets according to their relatively high, intermediate or low seasonal variability. Meteorological data from nearby stations were collected in each study area and used in the evaluation of some indicators of fuel moisture that are used in the Canadian Forest Fire Danger Rating System, namely the Drought Code (DC). It was found that in the summer season the slow response of live fine fuel moisture content (LFFMC) to meteorological conditions, namely to precipitation, was well described by the DC. Empirical correlations between LFFMC and DC for each species and site are proposed.

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.