Search Results (563)

Wildland fires are a common and destructive natural disaster in Alaska. Recent active fires in Alaska were assessed and analysed for their associated synoptic-scale climatic conditions in this study. Hotspot (HS) data from satellite observations over the past 20 years since 2004 (total number of HS = 300,988) were used to identify active fire-periods, and the occurrence of Rossby wave breaking (RWB) was examined using various weather maps. Analysis results show that there are 13 active fire-periods of which 7 active fire-periods are related to RWB. The total number of HSs during the seven RWB-related fire-periods was 164,422, indicating that about half (54.6%) of the recent fires in Alaska occurred under fire weather conditions related to RWB. During the RWB-related fire-periods, two hotspot peaks with different wind directions occurred. At the first hotspot peak, southwesterly wind blew from high-pressure systems in the Gulf of Alaska. In the second hotspot peak, the Beaufort Sea High (BSH) supplied strong easterly wind into Interior Alaska. It was suggested that changes in wind direction during active fire-period and continuously blowing winds from BSH may affect fire propagation. It is hoped that this study will stimulate further research into active fires related to RWBs in Alaska. Full article

Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article

The prevailing environmental conditions before and during the 28 Large Forest Fires (LFFs) that have occurred in the Canary Islands since 1983 are analyzed. These conditions are often associated with episodes characterized by the advection of continental tropical air masses originating from the Sahara, which frequently result in intense heatwaves. During the onset of the LFFs, the base of the subsidence thermal inversion layer—separating a lower layer of cool, moist air from an upper layer of warm, dry air—is typically located at an altitude of around 350 m above sea level, approximately 600 m below the usual average. Understanding these Saharan air advection events is crucial, as they significantly alter the vertical thermal structure of the atmosphere and create highly conducive conditions for wildfire ignition and spread in the forested mid- and high-altitude zones of the archipelago. Analysis of meteorological records from various weather stations reveals that the average maximum temperature on the first day of fire ignition is 30.3 °C, with mean temperatures of 27.4 °C during the preceding week and 28.9 °C throughout the fire activity period. Relative humidity on the ignition days averages 24.3%, remaining at around 30% during the active phase of the fires. No significant correlation has been found between dry or wet years and the occurrence of LFFs, which have been recorded across years with widely varying precipitation levels. 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

The time fluctuations of forest fires occurring in Basilicata, a region situated in Southern Italy, between 2004 and 2023 were investigated using various analytical approaches. Analysis revealed a clustering of fire occurrences over time, as indicated by a significantly high coefficient of variation. This suggests that the fire sequence does not follow a Poisson distribution and instead exhibits a clustered structure, largely driven by the heightened frequency of events during the summer seasons. The analysis of monthly forest fire occurrences and total burned area indicates a significant correlation between the two. This correlation is reinforced by shared patterns, notably an annual cycle that appears to be influenced by meteorological factors, aligning with the yearly fluctuations in the region’s weather conditions typical of a Mediterranean climate. Furthermore, the relationship between the Standardized Precipitation Evapotranspiration Index (SPEI) and forest fires revealed that the accumulation period of the SPEI corresponds to the cycle length of the fires: longer cycles in fire occurrences align with higher accumulation periods in SPEI data. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. 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

Coal-fired power plants exacerbate hazy weather under low winter temperatures, while sulphate ions (SO₄²⁻) in condensable particulate matter (CPM) emitted from ultra-low emission coal-fired power plants accelerate sulphate formation. The transformation of gaseous precursors (SO₂, NOx, NH₃) is the main pathway for sulphate formation by homogeneous or non-homogeneous reactions. For the sustainability of the world, in this paper, the effects of condensation temperature, H₂O, NO_X and NH₃ on the SO₄²⁻ generation characteristics under low-temperature rapid condensation conditions are investigated. With lower temperatures, especially from 0 °C cooling to −20 °C, the concentration of SO₄²⁻ was as high as 26.79 mg/m³. With a greater proportion of H₂SO₄ in the aerosol state, and a faster rate of sulphate formation, H₂O vapour condensation can provide a reaction site for sulphuric acid aerosol generation. SO₄²⁻ in CPM is mainly derived from the non-homogeneous reaction of SO₂. SO₃ is an important component of CPM and provides a reaction site for the formation of SO₄²⁻. SO₂ and SO₃, in combination with Stefan flow, jointly play a synergistic role in the generation of SO₄²⁻. The content of SO₄²⁻ was as high as 36.18 mg/m³. While NO_X sometimes inhibits the formation of SO₄²⁻, NH₃ has a key role in the nucleation process of CPM. NH₃, SO₂ and NO_X have been found to rapidly form sulphate with particle sizes up to 5 µm at sub-zero temperatures and promote the formation of sulphuric acid aerosols. 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

Forest fires in the Hindu Kush Himalaya (HKH) region are increasing in frequency and severity, driven by climate variability, prolonged dry periods, and human activity. Nepal, a critical part of the HKH, recorded over 22,700 forest fire events in the past decade, with fire incidence nearly doubling in 2023. Despite this growing threat, operational early warning systems remain limited. This study presents Nepal’s first high-resolution early fire risk outlook system, developed by adopting the Canadian Fire Weather Index (FWI) using meteorological forecasts from the High-Impact Weather Assessment Toolkit (HIWAT). The system generates daily and two-day forecasts using a fully automated Python-based workflow and publishes results as Web Map Services (WMS). Model validation against MODIS, VIIRS, and ground fire records for 2023 showed that over 80% of fires occurred in zones classified as Moderate to Very High risk. Spatiotemporal analysis confirmed fire seasonality, with peaks in mid-April and over 65% of fires occurring in forested areas. The system’s integration of satellite data and high-resolution forecasts improves the spatial and temporal accuracy of fire danger predictions. This research presents a novel, scalable, and operational framework tailored for data-scarce and topographically complex regions. Its transferability holds substantial potential for strengthening anticipatory fire management and climate adaptation strategies across the HKH and beyond. Full article

Climate change has led to the global intensification of wildfire activity, and South Korea has also experienced a marked increase in fire danger. To understand these developments, it is essential to examine both temporal trends and spatial patterns in fire-conducive climate conditions. In this study, we investigated the temporal and spatial characteristics of wildfire danger across South Korea between 2004 and 2023 using the Fire Weather Index (FWI). The analysis of long-term climate trends revealed region-specific patterns of increasing temperatures, decreasing precipitation, and declining wind speeds, which collectively contribute to an increased risk of wildfires. The FWI exhibited strong seasonal variations, with significant increases observed in spring, particularly in May, over the most recent decade. Statistical analyses confirmed a strong correlation between high FWI percentiles and wildfire occurrence, particularly noting an increased frequency of large-scale fires (>100 ha) in the highest FWI bins. The spatial analysis further highlighted that certain provinces, including Gangwon State, Gyeongsangbuk-do, Chuncheongbuk-do, and Gyeonggi-do, experienced disproportionately high increases in FWI values. These findings suggest that the FWI can serve as a robust framework for wildfire danger assessment in South Korea, particularly when supported by region-specific calibration and long-term validation under varying climatic conditions. Full article

Extensive wildfires and logging have affected the Russian boreal forests in recent decades. Scots pine (Pinus sylvestris L.) forests are widespread in Russia and are one of the most disturbed tree species in Siberia. However, the effects of disturbance on soil CO₂ efflux in the vast Siberian forests are still poorly understood. We used the LI 8100A infrared gas analyzer to study changes in soil CO₂ efflux into the atmosphere in mature Scots pine forests in the Siberian central taiga five–six years following fires and logging. Measurements of soil CO₂ efflux rates were performed on sites where automatic weather stations have been continuously operational since 2022, which gives us temporal patterns of meteorological fluctuations across forests with different disturbance histories. We found significant differences in soil efflux rates depending on the site and disturbance characteristics. In the undisturbed dry lichen-dominated forest, CO₂ efflux was 4.8 ± 2.1 µmol m⁻² s⁻¹, while in the wet moss-dominated forest it was 2.3 ± 1.3 µmol m⁻² s⁻¹, with soil efflux in Sphagnum sp. being twofold of that in feather moss. Both fire and logging significantly reduced CO₂ efflux, with a smaller reduction in soil CO₂ efflux observed in the moss-dominated plots (5%–40%) compared to the lichen-dominated plots (36%–55%). The soil efflux rate increased exponentially with increasing topsoil temperatures in lichen-dominated Scots pine sites, with disturbed plots showing less dependence compared to undisturbed forest. In the wet moss-dominated Scots pine forest, we found no significant dependence of soil efflux on temperature for all disturbance types. We also found a positive moderate relationship between soil efflux and forest floor depth in both lichen- and moss-dominated Scots pine forests across all the plots studied. Our findings advance the understanding of the effects of fire and logging on the carbon cycle and highlight the importance of accounting for disturbance factors in Earth system models due to changing climate and anthropogenic patterns. Full article

Climate change causes extreme weather events to occur frequently, such as drought, which may exacerbate forest fire regimes; as such, forest fire regimes may be closely related to drought characteristics. The spatial non-stationarity of factors affecting forest fires has not been fully clarified and needs further exploration. This study intends to address how drought characteristics affect forest fire regimes in China and whether spatial non-stationarity can improve the model prediction based on methods such as the run theory and GWR. Our results show that geographically weighted regression models perform better (AICc, AUC, R², RMSE, and MAE) than global regression models in modeling forest fire regimes. Although GWR improves accuracy, small sample sizes (vegetation zones, climatic zones) may affect its accuracy. Drought characteristics significantly affect (p < 0.05) the forest fire regimes, and the correlation is spatially non-static. At the grid scale, a positive correlation between the forest fire occurrence probability and drought characteristics is mostly distributed in the southwest and northwest regions. Our study is conducive to an in-depth understanding of the relationship between forest fire regimes and drought, aiming to provide a scientific basis for the development of forest fire management measures to mitigate drought stress according to local conditions. Full article

In fields such as military reconnaissance, forest fire prevention, and autonomous driving at night, there is an urgent need for high-precision three-dimensional reconstruction in low-light or night environments. The acquisition of remote sensing data by RGB cameras relies on external light, resulting in a significant decline in image quality and making it difficult to meet the task requirements. The method based on lidar has poor imaging effects in rainy and foggy weather, close-range scenes, and scenarios requiring thermal imaging data. In contrast, infrared cameras can effectively overcome this challenge because their imaging mechanisms are different from those of RGB cameras and lidar. However, the research on three-dimensional scene reconstruction of infrared images is relatively immature, especially in the field of infrared binocular stereo matching. There are two main challenges given this situation: first, there is a lack of a dataset specifically for infrared binocular stereo matching; second, the lack of texture information in infrared images causes a limit in the extension of the RGB method to the infrared reconstruction problem. To solve these problems, this study begins with the construction of an infrared binocular stereo matching dataset and then proposes an innovative perspective projection positional encoding-based transformer method to complete the infrared binocular stereo matching task. In this paper, a stereo matching network combined with transformer and cost volume is constructed. The existing work in the positional encoding of the transformer usually uses a parallel projection model to simplify the calculation. Our method is based on the actual perspective projection model so that each pixel is associated with a different projection ray. It effectively solves the problem of feature extraction and matching caused by insufficient texture information in infrared images and significantly improves matching accuracy. We conducted experiments based on the infrared binocular stereo matching dataset proposed in this paper. Experiments demonstrated the effectiveness of the proposed method. Full article

The Valencia region exemplifies the intricate interplay of climate, urbanization, and human interventions in managing hydrological systems amidst increasing environmental challenges. This study explores the escalating risks posed by flood events, emphasizing how anthropogenic factors—such as urban expansion, sediment exploitation, and inadequate land use—amplify the vulnerabilities to extreme weather patterns driven by abnormal Greenhouse Gas (GHG) concentration. Nature-based solutions (NBS) like floodplain restoration and dam removal are analyzed for their benefits in enhancing ecosystem resilience and biodiversity but are critiqued for unintended consequences, including accelerated river flow and sedimentation issues. This study further examines the impacts of forest fires, exacerbated by land abandonment and insufficient management practices, on soil erosion and runoff. A critical evaluation of global policies like the Sustainable Development Goals (SDGs) reveals the tension between aspirational targets and practical, locally-driven implementations. By advocating historical insights, ecological restoration practices, and community engagement, the findings highlight the importance of adaptive strategies to harmonize global frameworks with local realities through modeling and scaling simulations, offering a replicable model for sustainable flood mitigation and resilience building in Mediterranean contexts and beyond. Full article