David Beltrán-Marcos

Wildfires are becoming more frequent and severe in Mediterranean ecosystems, underscoring the urgent need for forest managers to rely on rapid and reliable methodologies to map fire-induced impacts and support post-fire management efforts. This study evaluates the accuracy and transferability of conventional spectral indices and physically-based remote sensing metrics, both retrieved from pre- and/or post-fire Sentinel-2 Level-2A scenes, for assessing photointerpreted fire severity (n = 646) through high-resolution satellite imagery (SPOT 6/7). The study sites are forest ecosystems located across wide environmental gradients and affected by four large wildfires (> 10,000 ha) that occurred in Spain during summer 2022. The physically-based metrics included the ratio of the fractional vegetation cover (FCOVER ratio) retrieved from the inversion of radiative transfer models (RTMs) and the canopy fraction burned (CFB) retrieved from multiple endmember spectral mixture analysis (MESMA). The calculated spectral indices were the dNBR, RBR, and the recently-proposed dNBR-EVI. A Random Forest classification algorithm was implemented to estimate categorized fire ecological impact levels (low, moderate and high) in a fivefold nested cross-validation approach from physically-based metrics and spectral indices. Findings highlighted the enhanced effectiveness of physically-based metrics, along with the dNBR-EVI index, as compared to conventional spectral indices in classifying fire severity across broadleaf and conifer forests, as well as with pooled data from both forest types. Particularly, the CFB (overall accuracy = 84.40%; Kappa = 0.77) minimized the underestimation of the high-severity class and showed high accuracy in classifying moderate fire severity, a category where traditional spectral indices showed significant limitations. The findings underscore the potential value of physically-based metrics and new spectral indices combined with photointerpretation of high-resolution satellite and aerial imagery as a rapid, scalable, and ecologically relevant approach to fire severity assessment.

Background: Designing effective land management actions addressed to increase ecosystem resilience requires us to understand how shifting fire regimes are shaping landscapes. In this study, we aim to assess the link between fire regime and pre-fire vegetation biophysical characteristics (type, amount, and structure) in controlling extreme fire behavior across Atlantic-Transition-Mediterranean bioregions in Spain marked by different summer drought conditions and dominant plant regenerative traits. We used remote sensing metrics to estimate fire severity and pre-fire vegetation characteristics in eight study areas recently affected by large and highly severe wildfires under different environmental contexts. Furthermore, to account for fire regime attributes, we retrieved, for each target wildfire, the perimeter of the past wildfires that occurred between 1985 and 2022 and calculated fire recurrence, the time the since last fire (TSLF), and fire severity of previous wildfires (FSPW). The effect of fire regime attributes on pre-fire vegetation was examined using generalized linear mixed models (GLMMs). Results: During the study period, fire recurrence decreased significantly in all bioregions analyzed. Fire severity increased under Atlantic conditions and decreased under Mediterranean environmental context, where the time since the last fire was the highest. Pre-fire fuel type and amount were identified as primary drivers of fire severity, being both strongly modulated by fire regime but following distinct mechanisms depending on the environmental context of each bioregion. In Atlantic sites, more frequent past wildfires of low to moderate fire severity were associated with a greater dominance of fire-prone shrublands with moderate fuel amounts, which increases the risk of severe wildfires. Similar trends occurred in Transition and Mediterranean sites but under the previous occurrence of highly severe wildfires. Specifically, long times after highly severe wildfires (> 30 years) increased fuel amount in conifer-dominated ecosystems in all bioregions analyzed, heightening susceptibility to extreme fire behavior. Conclusions: Our findings highlight that fire-prone ecosystems need adaptative management strategies to mitigate the effects of fire regime changes, but these actions should be specific to the climatic and ecological context.

The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R²cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R²cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.