Bruno Barbosa

The delineation of the Wildland-Urban Interface (WUI) is fundamental for wildfire risk management, yet it is highly sensitive to the underlying building data used. Global building datasets offer unprecedented coverage but may introduce biases by including different types of built-up structures, potentially leading to an overestimation of exposure and a misallocation of critical resources. This study aims to map the WUI and assess wildfire exposure in mainland Portugal by comparing the efficacy of different building datasets: the official residential database (BGE21), Microsoft Building Footprints (MSB24), and the World Settlement Footprint (WSF19). We employed a point-based mapping methodology (100-m radius, > 6.17 buildings/km ² ) to classify WUI into Intermix and Interface zones. Our analysis revealed that the choice of dataset drastically alters WUI estimates. MSB24, which includes all structure types, identified 67% more buildings than the residential-focused BGE21, resulting in a 73% larger WUI area. Spatial agreement was low, with only 46% of the total WUI area being identified by all three datasets, falling to just 29% for the more vulnerable Intermix zones. While MSB24 showed high recall (0.97 Intermix , 0.99 Interface ), its precision was low (0.43 Intermix , 0.67 Interface ), confirming a significant overestimation of critical zones. Analysis of wildfire perimeters (2000–2023) showed that burned area within the WUI was disproportionately higher in years with smaller total fire extent (e.g., 2006, 2018) rather than in megafire years (e.g., 2003, 2017). We conclude that while global datasets like MSB24 are valuable for emergency response due to their high coverage, their use for preventive planning and resource allocation may cause inefficiencies. We recommend that local authorities prioritize validated residential data, like BGE21, for strategic wildfire prevention and mitigation planning in Portugal to ensure resources are targeted efficiently.

Wildfire regimes in Mediterranean landscapes are becoming increasingly unpredictable, driven by the combined effects of climate change, land-use transitions, and socio-economic pressures. Traditional metrics such as burned area or ignition points often fail to capture the complexity of the temporal and spatial recurrence of fire events. To address this gap, we apply, for the first time, a trajectory analysis framework to wildfire occurrence data across mainland Portugal (1975–2024), using pixel-level binary time series at 100 m resolution. Originally developed for land cover change detection, this method classifies each pixel into sequences representing distinct temporal patterns (e.g., stability, gains, losses, or alternations) over defined periods. Results reveal a predominance of stable absence and alternation-type trajectories, particularly “All alternation gain first”, which points to recurrent yet irregular fire activity. Regional differences further highlight the influence of divergent socio-ecological contexts. The findings suggest that fire regimes in Portugal are not only recurrent but structurally dynamic, and that trajectory-based classification offers a novel and valuable tool for long-term monitoring and regionally adapted fire management. Applying this method to wildfire data required specific adjustments to account for the unique temporal and thematic characteristics of fire regimes, ensuring a meaningful interpretation of the results.

Forest fires are considered by Portuguese civil protection as one of the most serious natural disasters due to their frequency and extent. To address the problem, the Fire Forest Defense System establishes the implementation of fuel management bands to aid firefighting. The aim of this study was to develop a model capable of identifying vegetation removal in the urban–rural interface defined by law for fuel management actions. The model uses normalised difference vegetation index (NDVI) of Sentinel-2 images time series and is based on the Welch t-test to find statistically significant differences between (i) the value of the NDVI in the pixel; (ii) the mean of the NDVI in the pixels of the same land cover type in a radius of 500 m; and (iii) their difference. The model identifies a change when the t-test points for a significant difference of the NDVI value in the ‘pixel’ as comparted to the ‘difference’ but not the ‘mean’. We use a moving window limited to 60 days before and after the analysed date to reduce the phenological variations of vegetation. The model was applied in five municipalities of Portugal and the results are promising to identify the places where the management of fuel bands was not carried out. This indicates which model could be used to assist in the verification of the annual management of the fuel bands defined in the law.