Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE) accessed in January 2025, we applied remote sensing techniques to assess burn severity and post-fire regrowth. Sentinel-2 imagery was used to compute the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI); burn severity was derived from differenced NBR (dNBR), and vegetation recovery was monitored via differenced NDVI (dNDVI) and multi-year NDVI time series. We uniquely compare recovery across four zones with different fire histories—unburned, single-burn (2017 or 2021), and repeated-burn (2017 and 2021)—providing a novel perspective on post-fire dynamics in Mediterranean ecosystems. Results show that low-severity zones recovered more quickly than high-severity areas. Repeated-burn zones experienced the slowest and least complete recovery, while unburned areas remained stable. These findings suggest that repeated fires may shift vegetation from forest to shrubland. This study highlights the importance of remote sensing for post-fire assessment and supports adaptive land management to enhance long-term ecological resilience.

Background: High-severity burned areas can have lasting impacts on vegetation regeneration, carbon dynamics, hydrology, and erosion. While landscape models can predict erosion from burned areas using the differenced normalized burn ratio (dNBR), post-fire erosion modeling has predominantly focused on areas that have recently burned. Here, we developed and validated a predictive burn severity model that produces continuous dNBR predictions for recently unburned forest land in Utah. Results: Vegetation productivity, elevation, and canopy fuels were the most important predictor variables in the model, highlighting the strong control of fuels and vegetation on burn severity in Utah. Final model out-of-bag R² was 67.1%, residuals showed a correlation coefficient of 0.89 and classification accuracy into three classes was 85%. We demonstrated that dNBR can be empirically modeled relative to fuels and topography and found burn severity was highest in productive vegetation and at relatively cooler sites. Conclusions: We found that prediction accuracy was higher when fuel moisture was lower, suggesting drier weather conditions drive more consistent and predictable burn severity patterns across a range of burn severity, vegetation types, and geographic locations. Moreover, burn severity predictions from this model can be used to inform hydro-erosion models and subsequent management actions aimed at reducing burn severity and post-wildfire erosion risks.

Wildfires serve both ecological and destructive roles—supporting biodiversity and nu-trient cycling while also threatening ecosystems and economies, especially as climate change drives increases in fire frequency and intensity. This study investigates the impact of wildfires and subsequent vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE), remote sensing techniques were applied to assess burn severity and post-fire re-growth. The analysis utilized Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery. Burn severity was classified through differenced NBR (dNBR), while vegetation recovery was monitored via differ-enced NDVI (dNDVI) and multi-year NDVI time series. Results show that low-severity zones recovered more quickly than high-severity areas, which often failed to regain pre-fire vegetation levels. These findings suggest a potential shift from forested areas to shrubland or mixed vegetation in severely burned zones. Key recovery influencers in-clude climate variability, soil erosion, and repeated fire exposure. This study highlights the value of remote sensing for post-fire assessment and emphasizes the need for adaptive land management and ecological restoration strategies to support long-term ecosystem resilience in Mediterranean fire-prone landscapes.