Search Results (165)

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

The long-term post-fire recovery phase is a critical stage for forest ecosystems to progress toward regeneration and mature succession. During this process, soil bacteria exhibit greater environmental adaptability, rapidly driving nutrient cycling and facilitating vegetation restoration. This study investigated the community structure and diversity of soil bacteria during long-term recovery after forest fires in the cold temperate zone, focusing on soils from the 2000 fires in Daxing’anling. Soil samples were classified into Low (L), Moderate (M), and High (H) fire damage intensity, with bacterial community composition and diversity analyzed using Illumina sequencing technology. After long-term fire recovery, the contents of soil organic carbon, black carbon, total nitrogen, alkaline nitrogen, available phosphorus, and available potassium were significantly higher elevated (p < 0.05), and water content was significantly lower, compared with that in the control check (CK) group. Soil urease, fluorescein diacetate, soil acid phosphatase, and soil dehydrogenase activities were significantly higher, and soil sucrase activity was significantly lower in H. There was a significant difference in the Alpha diversity index among the groups. Compared with CK, the Shannon index was significantly increased (p < 0.05) in L, while both Chao1 and Shannon indices were significantly decreased (p < 0.05) in M and significantly higher in H than CK. The results of the PCoA showed that there was a significant difference in the Beta diversity of the bacterial community among the groups (R² = 0.60 p = 0.001). The dominant bacteria groups were Proteobacteria and Acidobacteriota, while Actinobacteria became the new dominant group during the long-term post-fire recovery. AP, WC, DOC, MBC, S-DHA, and S-SC were significantly and positively correlated with soil bacterial diversity (p < 0.05). The results of the co-occurrence network analysis showed that all groups were dominated by symbiotic relationships, with M having the highest network complexity and strongest competitive effects. This study found that the physicochemical properties of soils recovered over a long period of time after fire returned to or exceeded the unfired forest condition. The Actinobacteria phylum became a new dominant bacterial group, with stronger network complexity and competition, in the process of forest recovery after moderate fire. Full article

With rising global temperatures attributed to climate change, an increase in fire occurrences worldwide is anticipated. Therefore, a detailed examination of changing fire patterns is essential to improve our understanding of effective control strategies. This study analyzes the long-term trends of fire activity in Southern India (8–20° N, 73–85° E), utilizing MODIS active fire count data from January 2003 to December 2023. The climatological monthly mean results show that Southern India experiences heightened fire activity from December to May, reaching a peak in March. Yearly variations indicate that the highest fire counts occurred in 2021, followed by 2023, 2012, and 2018. The three most significant fire years in recent history reflect an upward trend in fire activity over the past decade, confirming insights from annual trend analysis. The correlation between inter-annual fire anomalies and different meteorological factors reveals a notable negative relationship with precipitation and soil moisture and a positive relationship with surface air temperature (SAT). Soil moisture demonstrates a stronger correlation (−0.45) than precipitation and SAT. In summary, long-term trends show a noteworthy annual increase of 3%. Additionally, monthly trends reveal interesting rising patterns in October, November, December, and January with higher significance levels. Our research supports regional climate action initiatives and policies addressing fire incidents in Southern India in light of the ongoing warming crisis. Full article

Grassland fires are one of the main disasters in the temperate grasslands of the Mongolian Plateau, posing a serious threat to the lives and property of residents. The occurrence of grassland fires is affected by a variety of factors, including the biomass and humidity of fuels, the air temperature and humidity, the precipitation and evaporation, snow cover, wind, the elevation and topographic relief, and human activities. In this paper, MCD12Q1, MCD64A1, ERA5, and ETOPO 2022 remote sensing data products and other products were used to obtain the relevant data of these factors to predict the occurrence of grassland fires. In order to achieve a better prediction, this paper proposes a generalized geographically weighted boosted regression (GGWBR) method that combines spatial heterogeneity and complex nonlinear relationships, and further attempts the generalized spatiotemporally weighted boosting regression (GSTWBR) method that reflects spatiotemporal heterogeneity. The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. The results showed that the accuracy of GGWBR was 0.8320, which was higher than generalized boosted regression models’ (GBM) 0.7690. Its sensitivity was 0.7754, which is higher than random forests’ (RF) 0.5662 and GBM’s 0.6927. The accuracy of GSTWBR was 0.8854, which was higher than that of RF, GBM and GGWBR. Its sensitivity was 0.7459, which is higher than that of RF and GBM. This study provides a new technical approach and theoretical support for the disaster prevention and mitigation of grassland fires in the Mongolian Plateau. Full article

In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, forest fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing ecological and economic losses, improving forest fire management efficiency, and ensuring personnel safety and property security. To enhance comprehensive understanding of wildfire prediction research, this paper systematically reviews studies since 2015, focusing on two key aspects: datasets with related tools and prediction algorithms. We categorized the literature into three categories: statistical analysis and physical models, traditional machine learning methods, and deep learning approaches. Additionally, this review summarizes the data types and open-source datasets used in the selected literature. The paper further outlines current challenges and future directions, including exploring wildfire risk data management and multimodal deep learning, investigating self-supervised learning models, improving model interpretability and developing explainable models, integrating physics-informed models with machine learning, and constructing digital twin technology for real-time wildfire simulation and fire scenario analysis. This study aims to provide valuable support for forest natural resource management and enhanced environmental protection through the application of remote sensing technologies and artificial intelligence algorithms. Full article

The Orinoquía region of Colombia includes diverse ecosystems such as riparian forests and seasonal savannas, which play vital roles as biodiversity reservoirs. However, increased fire activity, driven by both natural and anthropogenic pressures, poses mounting threats to these ecosystems. Despite their importance, the effects of fire on faunal communities, especially in transitional habitats, are not well understood. Understanding biodiversity responses to fire across different recovery stages is essential for conservation planning. This study aimed to assess the effects of fire occurrence and recovery time on dung beetle communities as an indicator of ecosystem resilience. We analyzed taxonomic responses—including species richness, abundance, and Hill diversity indices (D₀, D₁, D₂)—as well as functional traits such as guild richness, biomass, and food relocation behavior, across riparian forest–savanna ecotones under varying fire histories. Our results indicate that recent fires (≤1 year) and high fire frequencies (4–5 events) negatively affect species diversity and abundance. Dominance by a few disturbance-tolerant species, such as Digitonthophagus gazella, was observed in burned savannas, while forest habitats supported both rare and dominant taxa. Despite taxonomic declines, functional redundancy was maintained, largely due to the prevalence of small-bodied species. However, we observed a general resilience effect in which core species contributed to postfire community reassembly. Functional redundancy was maintained, with small dung beetles dominating the biomass and guild composition. The conservation status of transitional habitats, particularly the forest–savanna ecotone, played a critical role in postfire dung beetle community restructuring. The presence of resilient assemblages highlights the importance of dung beetles in sustaining key ecosystem functions following fire events. These findings underscore the potential of dung beetles as bioindicators for postfire monitoring and emphasize the need for improved fire management strategies in sensitive ecosystems. Full article

Fire is one of the most serious threatening conditions that endanger the safety of human life and building property. Religious buildings, where activities such as ritual incense burning and parishioner worship are conducted year-round, suffer from high fire risks and incomplete coverage of fire protection facilities, which have led to the frequent occurrence of fire accidents in ancient religious buildings around the globe. This study focuses on fire reconstruction and flame-retardant research for double-roofed ancient Buddhist buildings, addressing a gap in fire protection research for ancient religious buildings, particularly those with unique double-roofed structures. A systematic fire simulation method integrating building information modeling (BIM) and computational fluid dynamics (CFD) is proposed. This approach not only accurately models the complex structures of ancient buildings but also simulates fire and smoke spread paths, providing a scientific basis for fire warnings and firefighting strategies. Firstly, the double-roofed ancient Buddhist building is modeled according to its size through building information modeling (BIM). Secondly, the building modeling is revised, and the fire hazard is modeled based on computational fluid dynamics (CFD). Thirdly, the smoke and temperature sensors for fire warning and sprinkler systems for flame retardant are set. Finally, the fire and smoke spread paths are simulated for determining the location for installing the warning sensor and providing valuable fire rescues strategy. Based on simulations, a fire warning system using smoke and temperature sensors, along with a sprinkler-based flame retardant system, is designed. This integrated design significantly enhances the fire prevention and control capabilities of ancient buildings, reducing the occurrence of fire accidents. By simulating fire and smoke spread paths, the optimal locations for sensor installation are determined, and valuable fire rescue strategies are provided. This simulation-based analytical method greatly improves the precision and effectiveness of fire prevention and control. Experiments validate the flame-retardant and fire warning capabilities of the proposed method, demonstrating its practical application value in protecting ancient buildings from fire. The method offers new insights and technical support for fire protection in religious ancient buildings. Full article

Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a⁻¹ (p < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China. Full article

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios. 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

Globally, forests are constantly threatened by a plethora of disturbances of natural and anthropogenic origin, such as climate change, forest fires, urbanization, and pollution. Besides the most common stressors, during the last few years, Portuguese forests have been impacted by severe decline phenomena caused by invasive pathogens, many of which belong to the genus Phytophthora. The genus Phytophthora includes a large number of species that are invading forest ecosystems worldwide, chiefly as a consequence of global trade and human activities. This paper reports the results of a survey of Phytophthora diversity in natural and semi-natural forest ecosystems in Portugal along an elevation gradient. Isolations performed from 138 symptomatic plant tissues and rhizosphere samples collected from 26 plant species yielded a total of 19 Phytophthora species belonging to 6 phylogenetic clades, including P. cinnamomi (36 isolates), P. multivora (20), P. plurivora (9), P. cactorum (8), P. lacustris (8), P. pseudocryptogea (8), P. amnicola (6), P. hedraiandra (6), P. pseudosyringae (5), P. thermophila (5), P. bilorbang (4), P. inundata (4), P. asparagi (3), P. citricola (3), P. gonapodyides (3), P. rosacearum (3), P. chlamydospora (2), P. pachypleura (2), and P. syringae (1). Overall, the data obtained highlight the widespread occurrence of P. cinnamomi in natural ecosystems from sea level to mountain habitats. The results of the pathogenicity tests carried out on 2-year-old chestnut plants confirmed the key role of P. cinnamomi in the recrudescence of chestnut ink disease and the additional risk posed by P. pachypleura, P. plurivora, and P. multivora to Portuguese chestnut forests. Finally, three species, P. citricola, P. hedraiandra, and P. pachypleura, are reported for the first time in the natural ecosystems of Portugal. Full article

The Alattu–Päkylä gold occurrence is located in the Northern Lake Ladoga area, in the Raaha-Ladoga suprasubduction zone, at the Karelian Craton (AR)—Svecofennian foldbelt (PR₁) boundary. Its gold ore mineral associations are of two types of mineralization: (1) copper–molybdenum–porphyry with arsenopyrite and gold (intrusion-related) and (2) gold–arsenopyrite–sulfide in shear zones. Optical and scanning electron microscopy, X-ray fluorescence spectrometry, inductively coupled plasma mass spectrometry (ICP-MS), instrumental neutron activation analysis (INAA) and fire analysis with AAS finishing were used to study them. Type 1 was provoked by shallow-depth tonalite intrusion (~1.89 Ga) and type 2 by two stages of Svecofennian metamorphism (1.89–1.86 and 1.83–1.79 Ga) with the possible influence of the impactogenesis of the Janisjärvi astrobleme (age ~1 Ga). Intrusive and host rocks were subjected to shearing accompanied by the formation of ore-bearing metasomatic rocks of the propylite-beresite series (depending on substrate) and quartz–sericite, quartz and sericite–tourmaline veins and streaks. Ore mineralization is present as several consecutive mineral associations: pyritic–molybdenite with arsenopyrite and gold; gold–arsenopyrite; quartz–arsenopyrite with antimony sulfosalts of lead; gold–polysulfide with tetrahedrite –argentotetrahedrite series minerals and gold–antimony with Pb–Sb–S system minerals and native antimony. Arsenopyrite contains invisible (up to 234 ppm) and visible gold. Metamorphosed domains in arsenopyrite and rims with visible gold around it are usually enriched in As, indicating higher (up to >500 °C) temperatures of formations than original arsenopyrite with invisible gold (<500 °C). A paragenetic sequence associated with the deposition of invisible and visible gold established at the Alattu–Päkylä ore occurrence: pyrrhotite + unaltered arsenopyrite (with invisible gold) → altered arsenopyrite (As-enriched) + pyrite ± pyrrhotite + visible gold. Gold, associated with gudmundite, sphalerite and native antimony, seems to be due to cainotypic rhyodacitic porphyry cutting tonalite intrusion or with a retrograde stage in post-Svecofennian metamorphism. The isotopic composition of Pb and ²³⁸U/²⁰⁴Pb (9.4–9.75) for the feldspar of the tonalite intrusion and the pyrite of gold mineralization, εNd (−4 up to −5) for tonalites and ẟ³⁴S values of −2.10–+4.99 for arsenopyrite, indicate the formation of gold occurrence provoked by Svecofennian magmatic and tectono-thermal processes with the involvement of matter from a mantle-lower crustal reservoir into magma formation and mineralization. Full article

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation. Full article

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans. Full article

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region. Full article