Forest structural diversity characterization in Mediterranean landscapes affected by fires using Airborne Laser Scanning dataForest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as the Light Detection and Ranging (LiDAR) Height Diversity Index (LHDI), the LiDAR Height Evenness Index (LHEI), and vertical and horizontal continuity of vegetation, as well as topographic metrics were obtained in raster format from low point density data. In order to map burned and unburned areas, differentiate fire occurrence dates, and distinguish between old and more recent fires, a sample of pixels was previously selected to assess the existence of differences in forest structure using the Kruskal-Wallis test. Then, k-nearest neighbors algorithm (k-NN), support vector machine (SVM) and random forest (RF) classifiers were compared to select the most accurate technique. The results showed that, in more recent fires, around 70% of the laser returns came from grass and shrub layers, yielding low LHDI and LHEI values (0.37-0.65 and 0.28-0.46, respectively). In contrast, the areas burned more than 20 years ago had higher LHDI and LHEI values due to the growth of the shrub and tree strata. The classification of burned and unburned areas yielded an overall accuracy of 89.64% using the RF method. SVM was the best classifier for identifying the structural differences between fires occurring on different dates, with an overall accuracy of 68.79%. Furthermore, SVM yielded an overall accuracy of 75.49% for the classification between old and more recent fires.

Wildfires in the Mediterranean are strongly tied to human activities. Given their particular link with humans, which act as both initiators and suppressors, wildfire hazard is highly sensitive to socioeconomic changes and patterns. Many researchers have prompted the perils of sustaining the current management policy, the so-called ‘total fire exclusion’. This policy, coupled to increasingly fire-prone weather conditions, may lead to more hazardous fires in the mid-long run. Under this framework, the irruption of the COVID-19 pandemic adds to the ongoing situation. Facing the lack of an effective treatment, the only alternative was the implementation of strict lockdown strategies. The virtual halt of the system undoubtedly affected economic and social behavior, triggering cascading effects such as the drop in winter-spring wildfire activity. In this work, we discuss the main impacts, challenges and consequences that wildfire science may experience due to the pandemic situation, and identify potential opportunities for wildfire management. We investigate the recent evolution of burned area (retrieved from the MCD64A1 v006 MODIS product) in the EU Mediterranean region (Portugal, Spain, France, Italy and Greece) to ascertain to what extent the 2020 winter-spring season was impacted by the public health response to COVID-19 (curfews and lockdowns). We accounted for weather conditions (characterized using the 6-month Standardized Precipitation Evapotranspiration Index; SPEI6) to disregard possible weather effects mediating fire activity. Our results suggest that, under similar drought-related circumstances (SPEI6 ≈ −0.7), the expected burned area in 2020 during the lockdown period in the EU (March–May) would lay somewhere within the range of 38,800 ha ± 18,379 ha. Instead, the affected area stands one order of magnitude below average (3325 ha). This stresses the need of considering the social dimension in the analysis of current and future wildfire impacts in the Mediterranean region.

Despite escalating expenditures in firefighting, extreme fire events continue to pose a major threat to ecosystem services and human communities in Mediterranean areas. Developing a safe and effective fire response is paramount to efficiently restrict fire spread, reduce negative effects to natural values, prevent residential housing losses, and avoid causalties. Though current fire policies in most countries demand full suppression, few studies have attempted to identify the strategic locations where firefighting efforts would likely contain catastrophic fire events. The success in containing those fires that escape initial attack is determined by diverse structural factors such as ground accessibility, airborne support, barriers to surface fire spread, and vegetation impedance. In this study, we predicted the success in fire containment across Catalonia (northeastern Spain) using a model generated with random forest from detailed geospatial data and a set of 73 fire perimeters for the period 2008-2016. The model attained a high predictive performance (AUC = 0.88), and the results were provided at fine resolution (25 m) for the entire study area (32,108 km 2). The highest success rates were found in agricultural plains along the nonburnable barriers such as major road corridors and largest rivers. Low levels of containment likelihood were predicted for dense forest lands and steep-relief mountainous areas. The results can assist in suppression resource pre-positioning and extended attack decision making, but also in strategic fuels management oriented at creating defensive locations and fragmenting the landscape in operational firefighting areas. Our modeling workflow and methods may serve as a baseline to generate locally adapted models in fire-prone areas elsewhere.

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