Search Results (431)

Wildfires accelerate soil erosion. Preventive fuel management and post-fire control measures are two distinct strategies that can be used to mitigate wildfire-induced soil loss with varying effectiveness and costs. Here, we quantified the impacts and effectiveness of pre- versus post-fire treatment strategies on soil loss mitigation. We coupled fire simulations with soil erosion modelling to estimate annual wildfire-induced soil loss for two watersheds in Portugal. We identified optimal treatment locations with the aim of maximizing the reduction in soil loss, and estimated treatment effectiveness using treatment leverage and cost-effectiveness. Both mitigation strategies were predicted to reduce post-fire soil loss, with effects increasing with treatment extent. Treatments had a strong mitigation effect particularly in extreme fire years. Results indicated that there was no single mitigation strategy that fits all watersheds, and the choice was largely influenced by wildfire and treatment frequency. For the most fire-prone watershed, Castelo de Bode, fuel treatments were the most effective strategy, being approximately 2-fold cheaper and more effective than post-fire treatments. Treatments were more effective and exhibited lower variability in years with higher soil loss. Our results show that the most cost-effective combinations of treatment strategies vary with the soil loss reduction objective. Relevant treatment synergies were identified that can help land managers to maximize the attainment of soil loss mitigation goals ensuring the best use of resources. This work contributes to a better understanding of how post-fire soil loss can be mitigated, contributing for better resource allocation while maximizing specific management goals. Full article

Megafires are transforming western boreal forests, and many burned forests are salvage logged, removing more structure from landscapes and delaying forest regeneration. We studied forest-dwelling owls in a post-fire and salvage-logged landscape in central British Columbia, Canada, in 2018–2019 after the 2010 Meldrum Creek Fire and the 2017 Hanceville Fire. We examined owl habitat selection via call surveys compared to the habitats available in this landscape. Owl pellets were dissected to determine owl diets. We detected six owl species, of which Northern Saw-whet Owls (Aegolius acadicus) were the most common. Owls had weak and variable habitat selection within an 800 m radius of detections; all species used some burned area. Great Gray Owls (Strix nebulosa) and Great Horned Owls (Bubo virginanus) obtained more prey from mature forests (e.g., red-backed voles, Myodes gapperi, snowshoe hares, Lepus americanus) than other owls did, whereas other owls primarily consumed small mammals that were common in burned or salvaged areas. These results indicate a diverse community of owls can use landscapes within a decade after wildfire, potentially with some prey switching to take advantage of prey that use disturbed habitats. Despite that, owl numbers were low and some owls consumed prey that were not available in salvage-logged areas, suggesting that impacts on owls were more severe from the combination of fire and salvage logging than from fire alone. Full article

Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate the capacity of different SIs (GCI, MSI, NBR, NDVI, NDII, and EVI2) to reflect the effect of post-wildfire emergency works on early recovery of vegetation in four Spanish ecoregions. It compared vegetation regrowth between treated and untreated sites, identifying the most sensitive SI for monitoring this recovery. All indices except EVI2 detected significantly better recovery in treated areas; among these, GCI was the most sensitive and NDII the least. The effect of treatment on recovery measured through SI is influenced by site covariates (fire severity, physiography, post-fire action period, post-fire climate, and edaphic characteristics). Finally, random mixed models showed that annual precipitation lower than 700 mm, diurnal temperature over 21 °C, soils with finer texture, and water content under 33% are quantitative limits of the treatment effectiveness on vegetation recovery. Overall, the study highlighted the importance of immediate interventions after fires, especially in the first six months, and advocated context-specific management strategies based on fire severity, ecoregion, soil properties, and climate to optimize vegetation recovery. Full article

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans. Full article

With wildfires becoming more frequent and severe in fire-prone regions affected by warmer and drier climate conditions, reducing hazardous fuels is increasingly recognized as a preventative strategy for promoting sustainability and safeguarding valued resources. Prescribed fire is one of the most cost-effective methods for reducing hazardous fuels and hence wildfire severity, yet empirical research on its effectiveness at minimizing damage to highly valued resources and assets (HVRAs) remains limited. The overarching objective of this study was to evaluate wildfire severity under differing weather conditions across various HVRAs characterized by diverse land uses, vegetation types, and treatment histories. The findings from this study reveal that wildfire severity was generally lower in areas treated with prescribed fire, although the significance of this effect varied among HVRAs and diminished as post-treatment duration increased. The wildland–urban interface experienced the greatest initial reduction in wildfire severity following prescribed fire, but burn severity increased more rapidly over time relative to other HVRAs. Elevated drought conditions had a significant effect, increasing wildfire severity across all HVRAs. The implications of this study underscore the role of prescribed fire in promoting sustainable land management by reducing wildfire severity and safeguarding both natural and built environments, particularly in the expanding wildland–urban interface. Full article

In the Mediterranean region, the high frequency of fire events is combined with climatic conditions that hinder vegetation recovery. This underscores the urgent need for a post-fire restoration of natural ecosystems and implementation of emergency rehabilitation measures to prevent further degradation. In this study, we investigated the performance of three types of erosion control structures (log dams, log barriers, and wattles), two years after fire, in three Mediterranean areas that were burnt by severe forest fires in 2021. The wooden structures’ ability to infiltrate precipitation was evaluated by 100 infiltration experiments in 25 plots, one and two years after the wildfires. The unsaturated hydraulic conductivity K was determined at two zones formed between consecutive wooden structures, i.e., the erosion zone (EZ) where soil erosion occurs, and the deposition zone (DZ) where the soil sediment is accumulated. These zones showed significant differences concerning their hydraulic behavior, with DZ presenting enhanced infiltration ability by 130 to 300% higher compared to EZ, during both years of measurements. The findings suggest that the implementation of emergency restoration actions after a wildfire can highly affect the burned forest soils’ ability to infiltrate water, preventing surface runoff and erosion, whereas specific structures such as the log dams can be even more effective. Full article

Wildfires threaten ecosystems, biodiversity, and human livelihood while exacerbating climate change. Accurate identification and monitoring of burned areas (BA) are critical for effective post-fire recovery and management. Although satellite multi-spectral imagery offers a practical solution for BA monitoring, existing methods often prioritize specific spectral bands while neglecting full spectral shape information, which encapsulates overall spectral characteristics. This limitation compromises adaptability to diverse vegetation types and environmental conditions, particularly across varying spatial scales. To address these challenges, we propose the time-series spectral-angle-normalized burn index (TSSA-NBR). This unsupervised BA extraction method integrates normalized spectral angle and normalized burn ratio (NBR) to leverage full spectral shape and temporal features derived from Sentinel-2 time-series data. Seven globally distributed study areas with diverse climatic conditions and vegetation types were selected to evaluate the method’s adaptability and scalability. Evaluations compared Sentinel-2-derived BA with moderate-resolution products and high-resolution PlanetScope-derived BA, focusing on spatial scale and methodological performance. TSSA-NBR achieved a Dice Coefficient (DC) of 87.81%, with commission (CE) and omission errors (OE) of 8.52% and 15.58%, respectively, demonstrating robust performance across all regions. Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. The method effectively captured fire scars and outperformed band-specific and threshold-dependent approaches by integrating spectral shape features with fire indices, establishing a data-driven framework for BA detection. These results underscore its potential for fire monitoring and broader applications in detecting surface anomalies and environmental disturbances, advancing global ecological monitoring and management strategies. Full article

Wildfires, whether natural or human-caused, significantly alter soil properties and increase soil erosion susceptibility, particularly through changes in rill detachment capacity (Dc). This study aimed to evaluate the influence of fire intensity on key soil properties and to recognize their relationships with Dc under controlled laboratory conditions. The research was conducted in the Darestan Forest, Guilan Province, northern Iran, a region characterized by a Mediterranean semi-arid climate. Soil samples were collected from three fire-affected conditions: unburned (NF), low-intensity fire (LF), and high-intensity fire (HF) zones. A total of 225 soil samples were analyzed using flume experiments at five slope gradients and five flow discharges, simulating rill erosion. Soil physical and chemical characteristics were measured, including hydraulic conductivity, organic carbon, sodium content, bulk density, and water repellency. The results showed that HF soils significantly exhibited higher rill detachment capacity (1.43 and 2.26 times the values compared to the LF and NF soils, respectively) and sodium content and lower organic carbon, hydraulic conductivity, and aggregate stability (p < 0.01). Strong correlations were found between Dc and various soil properties, particularly a negative relationship with organic carbon. The multiple linear equation had good accuracy (R² > 0.78) in predicting rill detachment capacity. The findings of the current study show the significant impact of fire on soil degradation and rill erosion potential. The study advocates an urgent need for effective post-fire land management, erosion control, and the development of sustainable soil restoration strategies. Full article

Extreme wildfire events (EWEs) are becoming increasingly frequent in Mediterranean regions, posing significant threats to ecosystems. This study aimed to support post-fire restoration planning by developing a prioritization framework that categorizes areas according to different levels of vulnerability to the adverse impacts of EWEs. We developed a multi-criteria decision analysis (MCDA) approach to classify these areas within a fire perimeter. The process begins with the collection of available spatial data to assess the pre- and post-fire conditions. Following this, a set of criteria and sub-criteria was established through a participatory approach with local stakeholders. The analytic hierarchy process (AHP) was used to determine stakeholders’ preferences, which were then processed using the Criterium Decision Plus (CDP) version 4 software to support problem modeling. A combined consistency check was applied to ensure both individual coherence and group agreement. Finally, the methodology was integrated using the Ecosystem Management Decision Support (EMDS) software version 9, resulting in a spatial prioritization map that visually represents the levels of restoration priority and serves as a decision-support tool for post-fire restoration planning. Both the process and its results are discussed for an application to a large fire perimeter in the Vale do Sousa forested landscape. Full article

Wildfires play a pivotal role in shaping and regulating the structural characteristics of forest ecosystems. This study examined post-fire vegetation dynamics following the 2020 Bighorn Fire in the Santa Catalina Mountains, Arizona, USA, by integrating pre- and post-fire airborne LiDAR data with Landsat-derived burn severity indices from 2019 to 2024. We analyzed structural and functional vegetation traits across 12,500 hectares to assess the changes pre- to post-fire, and to evaluate how these changes were influenced by the burn severity. We applied a correlation analysis to explore the relationships among the structural variables across different vegetation cover types. Non-parametric LOESS regression revealed that the dNBR was more strongly associated with changes in the tree density than with vertical structural attributes. The functional recovery, indicated by the NDVI, generally outpaced the structural recovery captured by the NBR. Densely forested areas experienced greater declines in vegetation volumes and slower regeneration, whereas herbaceous and sparsely vegetated areas showed a more rapid, but compositionally distinct, recovery. The divergence between the NDVI and NBR trajectories underscores the importance of integrating structural and functional indicators to comprehensively assess the post-fire ecosystem resilience and inform targeted restoration efforts. Full article

Wildfires are integral to many forest ecosystems, yet their ecological effects are often influenced by historical land use and management. In this study, we assess the short-term impacts of fire and management on Castanea sativa Mill. stands in the fayal-brezal zone of northern Tenerife (Canary Islands), where traditional agroforestry systems have been widely abandoned. We established 12 transects across four stands: managed-burned, managed-unburned, oldfield-burned, and oldfield-unburned. We analyzed forest structure, understory species richness and composition, and soil nutrient content one year after a large wildfire. Forest structure has primarily been determined by management history, with oldfield plots showing greater tree density, basal area, and basal sprouting. Fire has had a limited effect on tree mortality, affecting ~10% of individuals on average. Understory species richness was significantly higher in managed plots, particularly those affected by fire, suggesting a positive interaction between disturbance and management. Species composition differed significantly among treatments, with Indicator Species Analysis identifying distinct taxa associated with each condition. Fire in oldfield plots led to increased compositional similarity with managed stands, indicating fire’s potential homogenizing effect. Principal Component Analysis of soil nutrients did not reveal clear treatment-related patterns, which was probably due to microenvironmental variability and the short post-fire interval. Overall, our results highlight the dominant role of land-use legacy in structuring these forests, with fire acting as a secondary but influential driver, revealing significant changes in species composition as well as in species richness. These findings have direct relevance for conservation and restoration strategies as well as for maintenance in these stands of Castanea sativa. They should also encourage managers of these protected areas, where land abandonment and fire are increasingly shaping forest dynamics. Full article

Accurate and rapid delineation of wildfire-affected areas is essential in the era of climate-driven increases in fire frequency. This study compares and analyzes four techniques for identifying wildfire-affected areas using Sentinel-2 satellite imagery: (1) calibrated differenced Normalized Burn Ratio (dNBR); (2) differenced NDVI (dNDVI) with empirically defined thresholds (0.04–0.18); (3) supervised SVM classifiers applying linear, polynomial, and RBF kernels; and (4) unsupervised ISODATA clustering. In particular, this study proposes an SVM-based classification method that goes beyond conventional index- and threshold-based approaches by directly using the SWIR, NIR, and RED band values of Sentinel-2 as input variables. It also examines the potential of the ISODATA method, which can rapidly classify affected areas without a training process and further assess burn severity through a two-step clustering procedure. The experimental results showed that SVM was able to effectively identify affected areas using only post-fire imagery, and that ISODATA enabled fast classification and severity analysis without training data. This study performed a wildfire damage analysis through a comparison of various techniques and presents a data-driven framework that can be utilized in future wildfire response and policy-oriented recovery support. Full article

Wildfires are increasing in extent and severity under anthropogenic climate change, with potential adverse impacts on native pollinators like wild bees. In 2019/2020, wildfires burned swathes of the Australian bushland. Whilst herbaceous angiosperms may flower in the post-fire environment, providing sustenance to native bees, pre-made holes created by wood-boring beetles that obligate cavity-nesting “renter” bees may take a longer time to recover. This may prevent native bees from colonising new areas or reduce the populations that have survived. To date, trap-nests, also known as bee hotels, have never been used as a tool to assist in providing nesting resources in post-fire environments. The project “Bee hotels to boost bees after bushfires” supported the recovery of native bee populations by installing artificial nesting substrates (bee hotels) in areas of high biodiversity value that were impacted by the 2019/2020 bushfires. This was achieved through monitoring of 1000 bee hotels (500 bamboo and 500 wooden) and visual surveys at five burnt sites and three control sites (nearby burnt sites without bee hotels) by a native bee ecologist from September–March 2021/2022. The bee hotel uptake was low initially, but by March, all hotels were occupied. Over 800 nests were created by bees in the bee hotels installed for this project and significantly more bees were observed in sites with bee hotels compared to control sites. Across sites, there was a significant negative association between honeybee density and nest occupancy, suggesting honeybees may be exerting competitive pressure on native bees in post-fire habitats. In conclusion, bee hotels, if designed correctly, can aid in boosting cavity-nesting bee populations following fires. Full article

Wildfires play a critical role in boreal forest ecosystems, yet their increasing frequency poses significant challenges for carbon emissions, ecosystem stability, and fire management. Accurate burned area detection is essential for assessing post-fire landscape recovery and fire-induced carbon fluxes. This study develops, compares, and optimizes machine learning (ML)-based models for burned area classification in the eastern Canadian boreal forest from 2000 to 2023 using MODIS-derived features extracted from Google Earth Engine (GEE), and the feature extraction includes maximum, minimum, mean, and median values per feature to enhance spectral representation and reduce noise. The dataset was randomly split into training (70%), validation (15%), and testing (15%) sets for model development and assessment. Combined labels were used due to class imbalance, and the model performance was assessed using kappa and the F1-score. Among the ML techniques tested, deep learning (DL) with a Multi-Layer Perceptron (MLP) outperformed Support Vector Machines (SVMs) and Random Forest (RF) by demonstrating superior classification accuracy in detecting burned area. It achieved an F1-score of 0.89 for burned pixels, confirming its potential for improving the long-term wildfire monitoring and management in boreal forests. Despite the computational demands of processing large-scale remote sensing data at 250 m resolution, the MLP modeling approach that we used provides an efficient, effective, and scalable solution for long-term burned area detection. These findings underscore the importance of tuning both network architecture and regularization parameters to improve the classification of burned pixels, enhancing the model robustness and generalizability. Full article

Wildfires have had measurable impacts on pollen dispersal in some areas; both facilitation and potential barriers to pollen movement have been reported. These dispersal dynamics in turn affect population genetics and reestablishment of seed-producing plants, at times significantly impacting the successional trajectory of the area in question. However, research on post-fire pollen distribution and occurrence is lacking for the boreal and sub-boreal forests of western Canada, and many communities that have been heavily impacted by wildfire remain concerned about the future forest landscape of these areas. We analyzed post-fire pollen samples from unburned and severely burned sub-boreal spruce stands in north-central British Columbia four years after a major wildfire. We used pollen traps to measure the occurrence and abundance of pollen types from four important plant families: Asteraceae, Ericaceae, Onagraceae, and Pinaceae families, to address specific concerns of the First Nation communities with territories overlapping the Shovel Lake wildfire burned area. Pinaceae pollen was found across all traps and was observed as the most dominant pollen type at all study sites, while pollen belonging to other families was found less frequently. No significant differences in pollen occurrence or abundance were found between burn severities, despite differences in the plant communities; however, plant and pollen abundance were found to be positively correlated to one another. These results may indicate that, as previously noted in other conifer-dominated forests, openings of the forest landscape by wildfire may facilitate rather than hinder pollen movements. Understory species should be studied in more detail as the effect of wildfire on pollen transport may vary between taxa and pollination syndromes. Full article