Biogeographic variability in wildfire severity and post-fire vegetation recovery across the European forests via remote sensing-derived spectral metrics

Post-fire spectral recovery and driving factors across the boreal and temperate forests

Increasingly frequent and severe forest fires, exacerbated by warmer and drier conditions, significantly affect forest ecosystems. Understanding the dynamics of post-fire forest recovery is crucial for assessing forest resilience and guiding forest management. However, most post-fire recovery studies focus primarily on spatial variation, while recovery changes over time are relatively less studied. In this study, we examined the patterns, trends and drivers of spectral recovery from forest fires that burned between 2002 and 2018 in boreal and temperate forests. We used relative recovery indicators (RRIs) developed from three spectral indices-the normalized burn ratio, normalized difference vegetation index and near-infrared reflectance of vegetation-to capture post-fire spectral recovery. Our results showed that post-fire spectral recovery rates in temperate forests are faster than those in boreal forests, with quicker recovery in regions with higher percentages of broad-leaved species, less severe fires, higher temperature and precipitation. The decline in spectral forest recovery rates of boreal forests indicates that boreal forest post-fire recovery is becoming increasingly challenging. Our work provides valuable insights into forest management and conservation in the face of increasing fire frequency and intensity.This article is part of the theme issue 'Novel fire regimes under climate changes and human influences: impacts, ecosystem responses and feedbacks'.

Delayed Vegetation Mortality After Wildfire: Insights from a Mediterranean Ecosystem

Wildfires, one of the most important ecological disturbances, influence the composition and dynamics of ecosystems all around the world. Changes in fire regimes brought on by climate change are making their effects worse by increasing the frequency and size of fires. This study examined the issue of delayed mortality at the species and community levels, concentrating on Mediterranean forests dominated by Quercus ilex and Quercus suber. This research examined areas lacking spectral recovery following a megafire, which, although relatively small compared to the total burned area, represented significant ecological disturbances. The results highlighted distinct post-fire dynamics at both the woodland and species levels. Q. ilex experienced higher delayed mortality, particularly in areas of lower fire severity (NR), likely due to increased intra-specific competition. Because of its thick bark, which offers stronger fire resistance and encourages regeneration even in high-severity fire zones (HR), Q. suber showed greater resilience. Responses from the shrub layer varied, and some species, such as Pteridium aquilinum and Cytisus villosus, showed post-fire proliferation. To improve our knowledge of ecosystem resilience and guide forest management in fire-prone areas, these findings highlight the intricacy of post-fire ecological processes and the need to integrate species-specific features with more general community-level patterns.

Extreme Fire Spread Events Burn More Severely and Homogenize Postfire Landscapes in the Southwestern United States

Extreme fire spread events rapidly burn large areas with disproportionate impacts on people and ecosystems. Such events are associated with warmer and drier fire seasons and are expected to increase in the future. Our understanding of the landscape outcomes of extreme events is limited, particularly regarding whether they burn more severely or produce spatial patterns less conducive to ecosystem recovery. To assess relationships between fire spread rates and landscape burn severity patterns, we used satellite fire detections to create day-of-burning maps for 623 fires comprising 4267 single-day events within forested ecoregions of the southwestern United States. We related satellite-measured burn severity and a suite of high-severity patch metrics to daily area burned. Extreme fire spread events (defined here as burning > 4900 ha/day) exhibited higher mean burn severity, a greater proportion of area burned severely, and increased like adjacencies between high-severity pixels. Furthermore, increasing daily area burned also resulted in greater distances within high-severity patches to live tree seed sources. High-severity patch size and total high-severity core area were substantially higher for fires containing one or more extreme spread events than for fires without an extreme event. Larger and more homogenous high-severity patches produced during extreme events can limit tree regeneration and set the stage for protracted forest conversion. These landscape outcomes are expected to be magnified under future climate scenarios, accelerating fire-driven forest loss and long-term ecological change.

Enhancing active fire detection in Sentinel 2 imagery using GLCM texture features in random forest models

The array of wildfire activities instigated by human endeavors has emerged as a significant source of atmospheric pollution, posing considerable risks to both public health and property safety. This study harnesses Sentinel-2 satellite data, employing a variety of methods including spectral index methods, thresholding, and the Random Forest (RF) model for active fire spot detection. The research encompasses a wide range of land cover types across various Chinese regions. Utilizing the Gini coefficient, the study assesses the importance of spectral and texture features in the RF, culminating in the selection of an optimal feature combination for the construction of a bespoke RF model tailored for active fire detection. The research utilized texture features based on the Grey Level Co-occurrence Matrix (GLCM), demonstrating their significant contribution to enhancing the accuracy of fire detection using the RF model. Our analysis reveals that GLCM-based texture features, which form 40% of the model's final feature set, are crucial for improving detection accuracy. The optimized RF model demonstrates a marked superiority in identifying active fires, achieving an overall accuracy of 86.1%. The study results demonstrate that the bespoke RF model is suitable for detecting active fire across various land cover environments in China.

Multi-temporal assessment of a wildfire chronosequence by remote sensing

The study aimed to develop a methodological framework to identify forest ecosystems affected by wildfires and evaluate their recovery chronologically. To do this remote sensing analysis, sites with burn scars were selected based on various criteria (fire severity, affected area, vegetation and soil type, slope, aspect, and one-time occurrence of wildfire in the last 23 years). Spectral vegetation indices (VIs) from satellite imagery were used to estimate burn severity and vegetation cover changes. Images of surface reflectance were obtained from the collection of Landsat 5 ETM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, available and processed on the Google Earth Engine Platform (GEE). Indices VIs (i) the normalized difference vegetation index (NDVI), (ii) the normalized burn ratio (NBR), and (iii) the differenced normalized burn ratio (dNBR) were calculated to classify burn severity. The one-time occurrence selection was performed using the LandTrendr algorithm to monitor changes in land cover and burned areas. To validate the selection, the chosen sites within the chronosequence were clustered on 4 seasons of soil properties and litter accumulation recovery. Our result can guide methodological comparisons and forest management practices on large surfaces by comparing parches of different time-affected ecosystems. Validation sites of the cluster chronosequence shows consistent recovery of soil properties as soil carbon, bulk density and litter accumulation through the studied years •The study developed a framework to identify wildfire-affected forest ecosystems and evaluate their recovery using remote sensing and local data.•Vegetation indices (NDVI, NBR, dNBR) from Landsat satellite imagery processed on the Google Earth Engine were used to assess burn severity and vegetation changes over time.•Selected sites were validated using the LandTrendr algorithm and monitored for seasonal changes in soil properties and litter accumulation.

Coupling heat wave and wildfire occurrence across multiple ecoregions within a Eurasia longitudinal gradient

Understanding the relationship between heat wave occurrence and wildfire spread represents a key priority in global change studies due to the significant threats posed on natural ecosystems and society. Previous studies have not explored the spatial and temporal mechanism underlying the relationship between heat waves and wildfires occurrence, especially over large geographical regions. This study seeks to investigate such a relationship with a focus on 37 ecoregions within a Eurasia longitudinal gradient. The analysis is based on the wildfire dataset provided by the GlobFire Final Fire Event Detection and the meteorological dataset ERA5-land from Copernicus Climate service. In both cases we focused on the 2001-2019 timeframe. By means of a 12 km square grid, three wildfire metrics, i.e., density, seasonality, and severity of wildfires, were computed as proxy of fire regime. Heat waves were also characterized in terms of periods, duration, and intensity for the same period. Statistical tests were performed to evaluate the different patterns of heat wave and wildfire occurrence in the 37 ecoregions within the study area. By using Geographically Weighted Regression (GWR) we modeled the spatial varying relationships between heat wave characteristics and wildfire metrics. As expected, our results suggest that the 37 ecoregions identified within the Eurasia longitudinal gradient differ in terms of fire regimes. However, the occurrence of heat waves did not show significant differences among ecoregions, but a more evident variability in terms of relationship between fire regime metrics and heat waves within the study area. The outcome of the GWR analysis allowed us to identify the spatial locations (i.e., hotspot areas) where the relationship between heat waves and wildfires is positive and significant. Hence, in hotspots the presence of heat waves can be seen as a driver of wildfire occurrence in forest and steppe ecosystems. The findings from this study could contribute to a more comprehensive assessment of wildfire patterns in this geographical region, thus supporting cross-regional prevention strategies for disaster risk mitigation.

Characterization of biophysical contexts leading to severe wildfires in Portugal and their environmental controls

Characterizing the fire regime in regions prone to extreme wildfire behavior is essential for providing comprehensive insights on potential ecosystem response to fire disturbance in the context of global change. We aimed to disentangle the linkage between contemporary damage-related attributes of wildfires as shaped by the environmental controls of fire behavior across mainland Portugal. We selected large wildfires (≥100 ha, n = 292) that occurred during the 2015-2018 period, covering the full spectrum of large fire-size variation. Ward's hierarchical clustering on principal components was used to identify homogeneous wildfire contexts at landscape scale on the basis of fire size, proportion of high fire severity, and fire severity variability, and their bottom-up (pre-fire fuel type fraction, topography) and top-down (fire weather) controls. Piecewise Structural Equation Modeling was used to disentangle the direct and indirect relationships between fire characteristics and fire behavior drivers. Cluster analysis evidenced severe and large wildfires in the central region of Portugal displaying consistent fire severity patterns. Thus, we found a positive relationship between fire size and proportion of high fire severity, which was mediated by distinct fire behavior drivers involving direct and indirect pathways. A high fraction of conifer forest within wildfire perimeters and extreme fire weather were primarily responsible for those interactions. In the context of global change, our results suggest that pre-fire fuel management should be targeted at expanding the fire weather settings in which fire control is feasible and promote less flammable and more resilient forest types.