Search Results (800)

In early May 2025, extreme wildfires swept across Manitoba, Canada, fueled by unseasonably warm temperatures, prolonged drought, and stressed vegetation. We explore how multi-source satellite indicators—such as anomalies in snow cover, precipitation, temperature, vegetation indices, and soil moisture in April–May—jointly signal landscape preconditioning for fire, highlighting the potential of these compound anomalies to inform fire risk awareness in boreal regions. Results indicate that rainfall deficits and diminished snowpack significantly reduced soil moisture, which subsequently decreased vegetative greenness and created a flammable environment prior to ignition. This concept captures how multiple moderate anomalies, when occurring simultaneously, can converge to create high-impact fire conditions that would not be flagged by individual thresholds alone. These findings underscore the importance of integrating climate and biosphere anomalies into wildfire risk monitoring to enhance preparedness in boreal regions under accelerating climate change. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

This study aimed to investigate the effect of wildfire episodes on air quality in terms of particulate matter (PM) and carbonaceous compound concentration in ambient air, and to assess deviations from typical annual patterns. The sampling was performed at a rural background site near the Adriatic coast in Croatia through 2024. To better understand contributions caused by fire events, the levels of organic carbon (OC), elemental carbon (EC), black carbon (BC), pyrolytic carbon (PyrC), optical carbon (OptC), water-soluble organic carbon (WSOC), levoglucosan (LG), mannosan (MNS), and galactosan (GA) were determined in PM₁₀ and PM_2.5 fractions (particles smaller than 10 µm and 2.5 µm, respectively). The annual mean concentrations of PM₁₀ and PM_2.5 were 14 µg/m³ and 8 µg/m³, respectively. During the fire episode, the PM_2.5 mass contribution to the total PM₁₀ mass exceeded 65%. Total carbon (TC) and OC increased by a factor of 7, EC and BC by 12, PyrC by 8, and WSOC by 12. The concentration of LG reached 1.219 μg/m³ in the PM₁₀ fractions and 0.954 μg/m³ in the PM_2.5 fractions, representing a 200-fold increase during the fire episode. Meteorological data were integrated to assess atmospheric conditions during the fire episode, and the specific ratios between fire-related compounds were analyzed. Full article

Increasing wildfires are causing global concerns about ecosystem functioning and services. Although some wildfires are caused by natural ignitions, it is also important to understand how human ignitions and human-related factors can contribute to wildfires. While dynamic global vegetation models (DGVMs) have incorporated fire-related modules to simulate wildfires and their impacts, few models have fully considered various human-related factors causing human ignitions. Using global examples, this study aims to identify key factors associated with human impacts on wildfires and provides suggestions for enhancing model simulations. The main categories explored in this paper are human behavior and activities, socioeconomic background, policy, laws, regulations, and cultural and traditional activities, all of which can influence wildfires. Employing an integrated and interdisciplinary assessment approach, this study evaluates existing DGVMs and provides suggestions for their improvement. Full article

The escalating impacts and occurrence of wildfires threaten the public, economies, and global ecosystems. Physiologically declining or dead trees are a great portion of the fires because these trees are prone to higher ignition and have lower moisture content. To prevent wildfires, hazardous vegetation needs to be removed, and the vegetation should be identified early on. This work proposes a real-time fire risk tree detection framework using UAV images, which is based on lightweight object detection. The model uses the MobileNetV3-Small spine, which is optimized for edge deployment, combined with an SSD head. This configuration results in a highly optimized and fast UAV-based inference pipeline. The dataset used in this study comprises over 3000 annotated RGB UAV images of trees in healthy, partially dead, and fully dead conditions, collected from mixed real-world forest scenes and public drone imagery repositories. Thorough evaluation shows that the proposed model outperforms conventional SSD and recent YOLOs on Precision (94.1%), Recall (93.7%), mAP (90.7%), F1 (91.0%) while being light-weight (8.7 MB) and fast (62.5 FPS on Jetson Xavier NX). These findings strongly support the model’s effectiveness for large-scale continuous forest monitoring to detect health degradations and mitigate wildfire risks proactively. The framework UAV-based environmental monitoring systems differentiates itself by incorporating a balance between detection accuracy, speed, and resource efficiency as fundamental principles. Full article

In the United States, ecosystems regularly experience wildfires and as fire seasons lengthen, fires are becoming a more important disturbance. While all types of disturbance have impacts on the carbon cycle, fires result in immediate emissions into the atmosphere. To assist managers in assessing wildland fire impacts, particularly on federally managed land, we developed estimates of area burned and related emissions for a 21-year period. These estimates are based on wildland fires defined by the interagency Monitoring Trends in Burn Severity database; emissions are simulated through the Wildland Fire Emissions Inventory System; and the classification of public land is performed according to the US Geological Survey’s Protected Areas Database of the United States. Wildland fires on federal land contributed 62 percent of all annual CO₂ emissions from wildfires in the United States between 2001 and 2021. During this period, emissions from the forest fire subset of wildland fires ranged from 328 Tg CO₂ in 2004 to 37 Tg CO₂ in 2001. While forest fires averaged 38 percent of burned area, they represent the majority—59 to 89 percent of annual emissions—relative to fires in all ecosystems, including non-forest. Wildland fire emissions on land belonging to the federal government accounted for 44 to 77 percent of total annual fire emissions for the entire United States. Land managed by three federal agencies—the Forest Service, the Bureau of Land Management, and the Fish and Wildlife Service—accounted for 93 percent of fire emissions from federal land over the course of the study period, but year-to-year contributions varied. Full article

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

This study describes a quite special and interesting atmospheric event characterized by the simultaneous presence of fresh and aged smoke layers. These peculiar conditions occurred on 16 July 2024 at the CNR-IMAA atmospheric observatory (CIAO) in Potenza (Italy), and represent an ideal case for the evaluation of the impact of aging and transport mechanisms on both the optical and microphysical properties of biomass burning aerosol. The fresh smoke was originated by a local wildfire about 2 km from the measurement site and observed about one hour after its ignition. The other smoke layer was due to a wide wildfire occurring in Canada that, according to backward trajectory analysis, traveled for about 5–6 days before reaching the observatory. Synergetic use of lidar, ceilometer, radar, and microwave radiometer measurements revealed that particles from the local wildfire, located at about 3 km a.s.l., acted as condensation nuclei for cloud formation as a result of high humidity concentrations at this altitude range. Optical characterization of the fresh smoke layer based on Raman lidar measurements provided lidar ratio (LR) values of 46 ± 4 sr and 34 ± 3 sr, at 355 and 532 nm, respectively. The particle linear depolarization ratio (PLDR) at 532 nm was 0.067 ± 0.002, while backscatter-related Ångström exponent (AEβ) values were 1.21 ± 0.03, 1.23 ± 0.03, and 1.22 ± 0.04 in the spectral ranges of 355–532 nm, 355–1064 nm and 532–1064 nm, respectively. Microphysical inversion caused by these intensive optical parameters indicates a low contribution of black carbon (BC) and, despite their small size, particles remained outside the ultrafine range. Moreover, a combined use of CIAO remote sensing and in situ instrumentation shows that the particle properties are affected by humidity variations, thus suggesting a marked particle hygroscopic behavior. In contrast, the smoke plume from the Canadian wildfire traveled at altitudes between 6 and 8 km a.s.l., remaining unaffected by local humidity. Absorption in this case was higher, and, as observed in other aged wildfires, the LR at 532 nm was larger than that at 355 nm. Specifically, the LR at 355 nm was 55 ± 2 sr, while at 532 nm it was 82 ± 3 sr. The AEβ values were 1.77 ± 0.13 and 1.41 ± 0.07 at 355–532 nm and 532–1064 nm, respectively and the PLDR at 532 nm was 0.040 ± 0.003. Microphysical analysis suggests the presence of larger, yet much more absorbent particles. This analysis indicates that both optical and microphysical properties of smoke can vary significantly depending on its origin, persistence, and transport in the atmosphere. These factors that must be carefully incorporated into future climate models, especially considering the frequent occurrences of fire events worldwide. 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

Arundo donax L. has been introduced in markets worldwide due to its economic value. However, it is listed in the world’s 100 worst alien invasive species because it easily escapes from cultivation, and forms dense monospecific stands in riparian areas, agricultural areas, and grassland areas along roadsides, including in protected areas. This species grows rapidly and produces large amounts of biomass due to its high photosynthetic ability. It spreads asexually through ramets, in addition to stem and rhizome fragments. Wildfires, flooding, and human activity promote its distribution and domination. It can adapt to various habitats and tolerate various adverse environmental conditions, such as cold temperatures, drought, flooding, and high salinity. A. donax exhibits defense mechanisms against biotic stressors, including herbivores and pathogens. It produces indole alkaloids, such as bufotenidine and gramine, as well as other alkaloids that are toxic to herbivorous mammals, insects, parasitic nematodes, and pathogenic fungi and oomycetes. A. donax accumulates high concentrations of phytoliths, which also protect against pathogen infection and herbivory. Only a few herbivores and pathogens have been reported to significantly damage A. donax growth and populations. Additionally, A. donax exhibits allelopathic activity against competing plant species, though the allelochemicals involved have yet to be identified. These characteristics may contribute to its infestation, survival, and population expansion in new habitats as an invasive plant species. Dense monospecific stands of A. donax alter ecosystem structures and functions. These stands impact abiotic processes in ecosystems by reducing water availability, and increasing the risk of erosion, flooding, and intense fires. The stands also negatively affect biotic processes by reducing plant diversity and richness, as well as the fitness of habitats for invertebrates and vertebrates. Eradicating A. donax from a habitat requires an ongoing, long-term integrated management approach based on an understanding of its invasive mechanisms. Human activity has also contributed to the spread of A. donax populations. There is an urgent need to address its invasive traits. This is the first review focusing on the invasive mechanisms of this plant in terms of adaptation to abiotic and biotic stressors, particularly physiological adaptation. Full article

Understanding and mapping forest fire vulnerability is essential for informed landscape management and disaster risk reduction, especially in the context of increasing anthropogenic and climatic pressures. This study aims to model and spatially predict forest fire vulnerability across Romania using two machine learning algorithms: MaxEnt and XGBoost. We integrated forest fire occurrence data from 2006 to 2024 with a suite of climatic, topographic, ecological, and anthropogenic predictors at a 250 m spatial resolution. MaxEnt, based on presence-only data, achieved moderate predictive performance (AUC = 0.758), while XGBoost, trained on presence–absence data, delivered higher classification accuracy (AUC = 0.988). Both models revealed that the impact of environmental variables on forest fire occurrence is complex and heterogeneous, with the most influential predictors being the Fire Weather Index, forest fuel type, elevation, and distance to human proximity features. The resulting vulnerability and uncertainty maps revealed hotspots in Sub-Carpathian and lowland regions, especially in Mehedinți, Gorj, Dolj, and Olt counties. These patterns reflect historical fire data and highlight the role of transitional agro-forested landscapes. This study delivers a replicable, data-driven approach to wildfire risk modelling, supporting proactive management and emphasising the importance of integrating vulnerability assessments into planning and climate adaptation strategies. Full article

Wildfires critically affect ecosystems, carbon cycles, and public health. COVID-19 restrictions provided a unique opportunity to study human activity’s role in wildfire regimes. This study presents a comprehensive evaluation of pandemic-induced wildfire regime changes across global fire-prone regions. Using MODIS data (2010–2022), we analyzed fire patterns during the pandemic (2020–2022) against pre-pandemic baselines. Key findings include: (a) A 22% global decline in wildfire hotspots during 2020–2022 compared to 2015–2019, with the most pronounced reduction occurring in 2022; (b) Contrasting regional trends: reduced fire activity in tropical zones versus intensified burning in boreal regions; (c) Stark national disparities, exemplified by Russia’s net increase of 59,990 hotspots versus Australia’s decrease of 60,380 in 2020; (d) Seasonal shifts characterized by December declines linked to mobility restrictions, while northern summer fires persisted due to climate-driven factors. Notably, although climatic factors predominantly govern fire regimes in northern latitudes, anthropogenic ignition sources such as agricultural burning and accidental fires substantially contribute to both fire incidence and associated emissions. The pandemic period demonstrated that while human activity restrictions reduced ignition sources in tropical regions, fire activity in boreal ecosystems during these years exhibited persistent correlations with climatic variables, reinforcing climate’s pivotal—though not exclusive—role in shaping fire regimes. This underscores the need for integrated wildfire management strategies that address both human and climatic factors through regionally tailored approaches. Future research should explore long-term shifts and adaptive management frameworks. 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

Vapour pressure deficit (VPD) is frequently used to assess the impact of climate change on wildfires, vegetation, and other phenomena dependent on atmospheric moisture. A common aim of projection studies is to sample the full range of changes projected by climate models. Although characterization of model spread in projected temperature and rainfall is common, similar analyses are lacking for VPD. Here, we analyze the range of change in projected VPD from a 15-member CMIP6 model ensemble using the SSP-370 scenario. Projected changes are calculated for 2015–2100 relative to the historical period 1850–2014, and the resulting changes are analyzed on a seasonal and regional basis, the latter using continents based on IPCC reference regions. We find substantial regional differences including higher increases in VPD in areas towards the equatorial regions, indicating increased vulnerability to climate change in these areas. Seasonal assessments reveal that regions in the Northern Hemisphere experience peak VPD changes in summer (JJA), correlating with higher temperatures and lower relative humidity, while Southern Hemisphere areas like South America see notable increases in all seasons. We find that the mean projected change in seasonal VPD ranges from 0.02–0.23 kPa in Europe, 0.04–0.19 kPa in Asia, 0.02–0.16 kPa in North America, 0.15–0.33 kPa in South America, 0.10–0.18 kPa in Oceania, and 0.21–0.31 kPa in Africa. Our analysis suggests that for most regions, no two models span the range of projected change in VPD for all seasons. The overall projected change space for VPD identified here can be used to interpret existing studies and support model selection for future climate change impact assessments that seek to span this range. Full article

The Baikal region, including areas with poor environmental conditions, has significant clean background zones. In the summer of 2023, we analyzed the physical and chemical parameters of aerosol particles with different size fractions at Irkutsk and Listvyanka monitoring stations. Reduced wildfires and minimal impact from fuel and energy industries allowed us to observe regional and transboundary pollution transport. A large data array indicated that, during the shift of cyclones from Mongolia to the south of the Baikal region, the concentrations of Na⁺, Ca²⁺, Mg²⁺, K⁺, and Cl⁻ ions increased at the Irkutsk station, dominated by NH₄⁺ and SO₄²⁻. The growth of the ionic concentrations at the Listvyanka station was observed in aerosol particles during the northwesterly transport. When air masses arrived from the southerly direction, the atmosphere was the cleanest. The analysis of 27 elements in aerosols revealed that Al, Fe, Mn, Cu, and Zn made the greatest contribution to air pollution at the Irkutsk station, while Fe, Al, Cu, Cr, Mn, and Ni made the greatest contribution to air pollution at the Listvyanka station. The dynamics of the investigated elements were mainly due to natural processes in the air under various synoptic situations and weather conditions in the region, although anthropogenic factors also affected the formation of aerosol composition wth certain directions of air mass transport. Full article