Search Results (112)

Forest fires are recurrent in Spain and affect tree species in different ways. Fire incidence in the main Spanish forest species, both native and alien, is estimated in this study based on actual fire occurrences. Indices of presence, burned area, fire extent, frequency, and recurrence were calculated for each species, and with them, fire incidence indices were obtained. Significant fire incidence was detected in Pinus canariensis, P. pinaster, Eucalyptus globulus, Quercus robur, Betula spp., Castanea sativa, Pinus radiata, and Quercus pyrenaica. Most of the species with the highest fire incidence are not located in the areas with the highest climatic hazard. There is limited correlation between flammability and fire extension, and this is not significant when considering fire incidence. The relationship between fire incidence and conifers is valid in absolute terms, but only partially in relative terms. Similarly, there is no general relationship between relative fire incidence and species with a natural or reforested origin. Some native hardwood species have unexpectedly high incidence, probably due to collateral damage caused by fires in nearby pine and eucalyptus stands. The fire incidence index of forest species is useful for forest management and for protecting species that are suffering severely from fire effects. 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

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

Global forest wildfires are increasing in both frequency and intensity, resulting in significant ecological degradation and posing substantial threats to human health. This study focused on the Dongjiang River Basin in southern China and investigated the seasonal and spatial distribution patterns of forest wildfires in the research region from 2003 to 2023 using geographic information system technology. This study employed the random forest (RF) model, a machine learning algorithm, to predict the danger level of wildfire across different seasons and quantitatively interpret the seasonal wildfire driving mechanisms using the SHapley Additive exPlanations (SHAP) values. The results indicated that forest wildfires in the Dongjiang Basin were predominantly concentrated in the eastern region of the Dongjiang Basin, with significant seasonal variation in the spatial distribution. The frequency of fire events exhibited distinct seasonal patterns, with higher incidence in spring and winter and relatively lower frequency in summer and autumn. The random forest model demonstrated high predictive accuracy for the wildfire danger in all the seasons. Furthermore, the analysis of the driving factors showed that, despite some seasonal variability, the underlying mechanisms of wildfire occurrence could be effectively quantified using the SHAP values. Notably, the Normalized Difference Vegetation Index and anthropogenic disturbances consistently emerged as the dominant driving forces behind forest wildfires across all the seasons. Full article

Fire is important in boreal forest ecosystems, but comprehensive recovery analysis is lacking for soil nutrients and plant traits in China boreal forests, where the strict “extinguish at sight” fire prevention policy has been implemented. Based on over 50 years of forest fire recordings in the Daxing’anling Mts, 48 pairs of burnt and unburnt controls (1066 plots) were selected for 0–20 cm soil sampling and plant surveys. We recorded 18 plant parameters of the abundance of each tree, shrub, grass, and plant size (height, diameter, and coverage), 7 geo-topographic data parameters, and 2 fire traits (recovery year and burnt area). We measured eight soil properties (soil organic carbon, SOC; total nitrogen, TN; total phosphorus, TP; alkali-hydrolyzed P, AP; organic P, Po; inorganic P, Pi; total glomalin-related soil protein, T-GRSP; easily-extracted GRSP, EE-GRSP). Paired T-tests revealed that the most significant impact of the fire was a 25%–48% reduction in tree sizes, followed by decline in the plant diversity of arbors and shrubs but increasing plant diversity in herbs. GRSP showed an >18% increase and P_o decreased by 17% (p < 0.05). Redundancy ordination showed that the post-fire recovery years and burnt area were the most potent explainer for the variations (p < 0.05), strongly interacting with latitudes and longitudes. Plant richness and tree size were directly affected by fire traits, while the burnt area and recovery times indirectly increased the GRSP via plant richness. A fire/control ratio chronosequence found that forest community traits (tree size and diversity) and soil nutrients could be recovered to the control level after ca. 30 years. This was relatively shorter than in reports on other boreal forests. The possible reasons are the low forest quality from overharvesting in history and the low fire severity from China’s fire prevention policy. This policy reduced the human mistake-related fire incidence to <10% in the 2010s in the studied region. Chinese forest fire incidences were 3% that of the USA. The burnt area/fire averaged 5 hm² (while the USA averaged 46 hm², Russia averaged 380 hm², and Canada averaged 527 hm²). Overharvesting resulted in the forest height declining at a rate of >10 cm/year. Our finding supports forest management and the evaluation of forest succession after wildfires from a holistic view of plant–soil interactions. Full article

At present, remote sensing serves as a key approach to track ecological recovery after fires. However, systematic and quantitative research on the research progress of post-fire remote sensing remains insufficient. This study presents the first global bibliometric analysis of post-fire remote sensing research (1994–2024), analyzing 1155 Web of Science publications and using CiteSpace to reveal critical trends and gaps. The key findings include the following: As multi-sensor remote sensing and big data technologies evolve, the research focus is increasingly pivoting toward interdisciplinary, multi-scale, and intelligent methodologies. Since 2020, AI-driven technologies such as machine learning have become research hotspots and continue to grow. In the future, more extensive time-series monitoring, holistic evaluations under compound disturbances, and enhanced fire management strategies will be required to addressing the global climate change challenge and sustainability. The USA, Canada, China, and multiple European nations work jointly on fire ecology research and technology development, but Africa, as a high wildfire-incidence area, currently lacks appropriate local research. Remote sensing of the environment and remote sensing and forests maintain a pivotal role in scholarly impact and information exchange. This work redefines post-fire remote sensing as a nexus of ecological urgency and social justice, demanding inclusive innovation to address climate-driven post-fire recovery regimes. Full article

The Cerrado is Brazil’s second largest biome, covering continuous areas in several states. Covering approximately 23% of Brazil’s territory, the Cerrado biome connects with all the main biomes in South America, thus forming a major biological corridor. This biome is one of those that has suffered the most from the incidence of wildfires, leading to a progressive depletion of the region’s natural resources. The aim of this study was to evaluate the use of an Unmanned Aerial Vehicle (UAV) embedded with an RGB sensor to obtain high-resolution digital products that can be used to identify areas of the Brazilian Cerrado affected by wildfires. The study was carried out in a savannah biome area selecting a vegetation corridor with native vegetation free from anthropogenic influence. The following UAV surveys were carried out before and after a burning event. Once the orthomosaics of the area were available, the GLI, VARI, ExG and NGRDI vegetation indices were used to analyze the vegetation. The data indicate that the B band and the GLI and ExG indices are more suitable for environmental impact analysis in Cerrado areas affected by fires, providing a solid basis for environmental monitoring and management in scenarios of fire disturbance. Full article

Climate change, accompanied by anomalously high temperatures and a decrease in precipitation during the warm season, can have serious consequences for the ecosystems and sustainability of the Ukrainian Polissia. In particular, there are increased risks of forest and peat fires, as well as an overall deterioration of the region’s ecological condition. Between 1990 and 2021, the Ukrainian Polissia region recorded an average temperature increase of 0.60 °C per decade, along with a 3–5% decrease in annual precipitation. An analysis of the spatial distribution of wildfire incident density based on satellite data (FIRMS) in the regions of the Ukrainian Polissia from 2001 to 2023 highlighted several periods of sharp increases in fires: 2002, 2007–2009, 2014–2015, and 2019–2020. The Spring Fire Season and the Late Summer–Autumn Fire Season coincide with periods of reduced precipitation, which in some years reached 40–60% below the climatic norm. Although the climatic conditions of spring 2022 were not as warm and dry as those in 2020, significant parts of Kyiv Polissia and Chernihiv Polissia suffered from large-scale wildfires due to ongoing military actions. The spatial distribution of fire frequency in 2020 and 2022 highlights different contributing factors: in 2020, weather anomalies were the primary cause, while in 2022, armed hostilities played a key role. Military conflicts not only increase the risk of fires but also complicate firefighting efforts, making the region even more vulnerable to large-scale forest fires, and thereby threatening its sustainability. These findings underscore the urgent need for integrated fire management strategies that take into account climate change, land-use policies, and geopolitical factors to mitigate the escalating wildfire threat in the region and ensure long-term sustainability. Full article

Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently automating OE tasks. Foundational OE textbooks are used as the basis for dataset creation and for feeding the LLMs. The methodology involved segmenting texts into manageable chapters, generating question–answer pairs, and translating visual elements into description logic to curate fine-tuned datasets in JSONL format. This research aims to enhance the models’ abilities to generate domain-specific ontologies, with hypotheses asserting that fine-tuned LLMs would outperform base models, and that domain-specific datasets would significantly improve their performance. Comparative experiments revealed that GPT-4 demonstrated superior accuracy and adherence to ontology syntax, albeit with higher computational costs. Conversely, Mistral 7B excelled in speed and cost efficiency but struggled with domain-specific tasks, often generating outputs that lacked syntactical precision and relevance. The presented results highlight the necessity of integrating domain-specific datasets to improve contextual understanding and practical utility in specialized applications, such as Search and Rescue (SAR) missions in wildfire incidents. Both models, despite their limitations, exhibited potential in understanding OE principles. However, their performance underscored the importance of aligning training data with domain-specific knowledge to emulate human expertise effectively. This study, based on and extending our previous work on the topic, concludes that fine-tuned LLMs with targeted datasets enhance their utility in OE, offering insights into improving future models for domain-specific applications. The findings advocate further exploration of hybrid solutions to balance accuracy and efficiency. Full article

By developing a conceptual framework that integrates the use of fire in agricultural activities, the occurrence of wildfires, and the perception of wildfire risk, this article examines the interplay among these three elements within both wet and dry Puna grasslands. The analysis focuses on two peasant and agropastoral communities, Vilcabamba and Apachaco, both located in the Cusco region—an area with the highest incidence of wildfires in Peru. This study highlights the sociocultural significance and persistence of agricultural burnings within Puna agropastoral communities and the necessity of considering changes in agricultural activity, mutual aid systems, and communal institutions—particularly regarding land ownership—to understand the factors contributing to wildfire occurrence. Furthermore, it reveals the widespread recognition of wildfire risk among community members, who are acutely aware of both the likelihood and potential severity of wildfire events, while governmental policies aimed at addressing this hazard predominantly focus on raising awareness and enforcing bans on agricultural burning, with limited consideration of these complex sociocultural dynamics. Full article

Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels. Full article

Detecting wildfires and smoke is essential for safeguarding forest ecosystems and offers critical information for the early evaluation and prevention of such incidents. The advancement of unmanned aerial vehicle (UAV) remote sensing has further enhanced the detection of wildfires and smoke, which enables rapid and accurate identification. This paper presents an integrated one-stage object detection framework designed for the simultaneous identification of wildfires and smoke in UAV imagery. By leveraging mixed data augmentation techniques, the framework enriches the dataset with small targets to enhance its detection performance for small wildfires and smoke targets. A novel backbone enhancement strategy, integrating region convolution and feature refinement modules, is developed to facilitate the ability to localize smoke features with high transparency within complex backgrounds. By integrating the shape aware loss function, the proposed framework enables the effective capture of irregularly shaped smoke and fire targets with complex edges, facilitating the accurate identification and localization of wildfires and smoke. Experiments conducted on a UAV remote sensing dataset demonstrate that the proposed framework achieves a promising detection performance in terms of both accuracy and speed. The proposed framework attains a mean Average Precision (mAP) of 79.28%, an F1 score of 76.14%, and a processing speed of 8.98 frames per second (FPS). These results reflect increases of 4.27%, 1.96%, and 0.16 FPS compared to the YOLOv10 model. Ablation studies further validate that the incorporation of mixed data augmentation, feature refinement models, and shape aware loss results in substantial improvements over the YOLOv10 model. The findings highlight the framework’s capability to rapidly and effectively identify wildfires and smoke using UAV imagery, thereby providing a valuable foundation for proactive forest fire prevention measures. Full article

Since the late 20th century, wildfires have increasingly disrupted ecosystems, particularly in Mediterranean-climate countries like Italy. This trend is linked to agroforestry and the abandonment of grazing activities, which is worsened by climate change. This study examines the knowledge within Italian society, using data from a survey conducted as part of the European FIRE-RES project, with 152 answers, mainly from northern Italy. The research explores traditional practices, identifies knowledge gaps, and proposes a program to enhance community resilience and to mitigate the impact of wildfires through preparedness and adaptation measures. Results show that, despite a strong cultural connection to the use of fire in Italy, there is a significant gap in public knowledge and readiness to address wildfire incidents among the population. This shows the need to develop educational programs to improve community awareness and adaptive measures to mitigate the impact of wildfires. Full article

Since the early 21st century, wildlands have witnessed an effusion of wildfires, with climate and social changes resulting in unanticipated wildfire activity and impact. For forest fires to be prevented and suppressed effectively, forest firefighting forces have adopted a specific administrative system for organizing and managing the fighting force. Under the administrative system, a debate on desired “leadership and management qualities” arises, and hence, this study sought to identify the leadership and management traits that should distinguish individuals in the forest fire incident command system (FFICS) applied by the Department of Forests (Cyprus). The research subject was addressed using mixed method research, employing quantitative and qualitative data. Both datasets were used to distinguish the purposes of the applied triangulation, enabling the examination of differentiation between the trends/positions recorded in terms of the object of study. These findings point to ideal forms of transformational leadership and neoclassical management. The outcomes suggest that at the individual level, the leaders of each of the operating structures should develop leadership qualities related to emotional intelligence, empathy, judgment, critical thinking, and especially self-awareness of strengths and weaknesses. At the stage of pre-suppression, a democratic leadership style (or guiding style) is supported, while during the operational progress stage of the FFICS, a “hybrid” leadership style is suggested, borrowing elements from the democratic and authoritarian (or managerial) leadership styles. The administrative skills of FFICS leaders should include the moral and psychological rewards of subordinates, job satisfaction and recognition, and two-way communication. The current study illustrates the need for divergent leadership and management traits and styles among the different hierarchical structures of the FFICS. Full article

Numerous epidemiologic studies have used remote sensing to quantify the contribution of greenness, air pollution, and wildfire smoke to asthma and other respiration outcomes. This is the first review paper to evaluate the influence of remote sensing exposures on specific outcome severity and risk factors in different ecological settings. Literature searches utilizing PubMed and Google Scholar identified 61 unique studies published between 2009 and 2023, with 198 specific outcomes. Respiration-specific outcomes were lower in greenness and higher in air pollution and wildfire ecological settings. Aerosol optical depth (AOD)-PM_2.5 readings and specific outcomes were higher in economically developing than in economically developed countries. Prospective studies found prenatal and infant exposure to higher ambient AOD-PM_2.5 concentration level readings contributed to higher childhood asthma incidence. Lung function was higher in greenness and lower in the other two ecological settings. Age, environment, gender, other, and total risk factors showed significant differences between health outcomes and ecological settings. Published studies utilized physiologic mechanisms of immune, inflammation, and oxidative stress to describe obtained results. Individual and total physiologic mechanisms differed between ecological settings. Study results were used to develop a descriptive physiologic asthma model and propose updated population-based asthma intervention program guidelines. Full article