Wildland-urban interface typologies prone to high severity fires in Spain

Due to complex interactions between climate and land use changes, large forest fires have increased in frequency and severity over the last decades, impacting dramatically on biodiversity and society. In southern European countries affected by demographic challenges, fire risk and danger play special relevance at the wildland-urban interfaces (WUIs), where decision-making and land management have strong socio-ecological implications. WUIs have been historically typified according to both fire occurrence probability and settlement vulnerability, but those classifications lack generality regarding fire regime components. We aim to develop an integrated and comprehensive scheme for identifying the WUI typologies most at risk to fire severity across large territories. We selected fourteen large wildfires (over than 500 ha) occurred in Spain (2016-2021) containing different WUI scenarios. First, based on a building cartography and a multi-temporal series of Sentinel-2 imagery, each WUI was delimited and spatially characterized according to building density and pre-fire fuel characteristics (type, amount, and structure). Afterwards, a decision tree regression model was applied to identify the most relevant pre-fire vegetation parameters driving burn severity. The combined effect of the selected pre-fire vegetation drivers and the building density patterns on fire severity was evaluated using linear mixed models. Finally, the WUI typologies most prone to high burn severity were recognized using Tukey post-hoc tests. Results indicated that building density, land cover class and vegetation cover fraction determined fire severity in areas close to human settlements. Specifically, isolated, scattered and sparsely clustered buildings enclosed in a high-cover shrub matrix were the WUI typologies most susceptible to high-severity fires. These findings contribute to the development of appropriate strategies to minimize the risk of severe fires in WUIs and avoid potential losses of multiple ecosystem services valuable for society.

Predicting potential wildfire severity across Southern Europe with global data sources

The large environmental and socioeconomic impacts of wildfires in Southern Europe require the development of efficient generalizable tools for fire danger analysis and proactive environmental management. With this premise, we aimed to study the influence of different environmental variables on burn severity, as well as to develop accurate and generalizable models to predict burn severity. To address these objectives, we selected 23 wildfires (131,490 ha) across Southern Europe. Using satellite imagery and geospatial data available at the planetary scale, we spatialized burn severity as well as 20 pre-burn environmental variables, which were grouped into climatic, topographic, fuel load-type, fuel load-moisture and fuel continuity predictors. We sampled all variables and divided the data into three independent datasets: a training dataset, used to perform univariant regression models, random forest (RF) models by groups of variables, and RF models including all predictors (full and parsimonious models); a second dataset to analyze interpolation capacity within the training wildfires; and a third dataset to study extrapolation capacity to independent wildfires. Results showed that all environmental variables determined burn severity, which increased towards the mildest climatic conditions, sloping terrain, high fuel loads, and coniferous vegetation. In general, the highest predictive and generalization capacities were found for fuel load proxies obtained though multispectral imagery, both in the individual analysis and by groups of variables. The full and parsimonious models outperformed all, the individual models, models by groups, and formerly developed predictive models of burn severity, as they were able to explain up to 95%, 59% and 25% of variance when applied to the training, interpolation and extrapolation datasets respectively. Our study is a benchmark for progress in the prediction of fire danger, provides operational tools for the identification of areas at risk, and sets the basis for the design of pre-burn management actions.