Search

AbstractForest fires significantly impact ecosystems, particularly microorganisms in forest soil. Elevated temperatures that exceeded the tolerance of most microorganisms, resulting in their inability to survive in such circumstances. The specific consequences of fire on microorganisms in the soil are not thoroughly comprehended; however, these populations play a crucial role in ecosystem processes and are susceptible to alterations induced by forest fires. By comparing microbial communities exposed to burning for 2, 4, and 9 years, the study aims to identify fire-resistant microorganisms and promote soil health by comparing them to those not burned. The Thermococcus, Paludibaculum, Candidatus Koribacter, Verrucomicrobium, Occallatibacter, Edaphobacter, Granulicella, Leptolyngbya, Chryseolinea, Gemmatimonas, Flavobacterium, Usitatibacter, Conexibacter, Bradyrhizobium are the most abundant phyla across samples in post-fire. Observation of the population dynamics of different bacterial species during a burning period revealed significant differences in microbial communities between burned and unburned soils. Through metagenome investigation, it was found that microbial diversity decreased in burned surface soils. Pathway analysis was conducted to explore the functional roles of bacterial species in plant and soil growth, highlighting their significance in various metabolic processes and interactions with host species. The analysis identified several class key metabolic pathways, including the biosynthesis of secondary metabolites, carbohydrate and energy metabolism, amino acid metabolism, and others, linking 19,792 metabolic pathways. These findings improve our understanding of forest fires impact on soil microorganisms and ecosystems, aiding the development of sustainable soil management in agriculture. Future studies should use multiple datasets with different geographical areas to fully understand the effects of forest fires on soil microbes. [ABSTRACT FROM AUTHOR]

Forest fires represent a major risk to both ecosystems and human health that rising frequency of it exacerbates global warming. This study introduces an innovative methodology for detecting forest fires and smoke using an enhanced capsule neural network (CNN) together with an adapted golden search optimizer (AGSO). By using advanced deep learning and optimization strategies, the method effectively identifies complex patterns linked to wildfires. Testing this model on wildfire smoke imagery and the BowFire dataset reveals that the proposed methodology outperformed traditional feature selection and classification methods. The integration of the modified CNN and AGSO facilitated rapid response and mitigation efforts, enhancing the accuracy and dependability of forest fire identification. This research highlights the importance of advanced computational techniques in reducing risks, ensuring safety, and progressing automatic forest fire detection systems. The combination of capsule neural networks with the golden search optimizer illustrates the potential of merging cutting-edge technologies to tackle intricate environmental issues efficiently. [ABSTRACT FROM AUTHOR]

In order to deeply understand the spatio-temporal distribution patterns of forest fires and reduce the adverse effects of forest fires on the ecological environment and human activities, the key parameters and dynamics of multi-source satellite for fire point identification was established using data from 8 domestic and foreign meteorological satellites and based on the classic context method. The satellite monitoring buffer zone radius verification method was used to verify the authenticity of forest hot spots retrieved from multi-source satellites, and the real forest hot spot data during the fire prevention period in 2021—2022 were used to analyze the spatio-temporal characteristics of forest fires. The results show as follows. The accuracy of satellite fire spot monitoring is 84. 42%, and the fire point classification accuracy is 89. 90% . The established inversion method is reasonable and reliable. The spatial distribution of forest fires in Hunan is “more in the southwest and less in the northeast”. At the same time the high-incidence areas are mainly distributed in southern Hunan, and the second-highest-incidence area is western Hunan. In summary, the risk of forest fires during the autumn prevention period is much greater than that during the spring prevention period. During the extreme high temperature and drought in 2022, forest fires were mainly distributed in the southern Hunan region and the Hengshao Basin. From the perspective of process distribution, the distribution of forest fires can be divided into four stages. The number of forest fires in the first three stages showed a significant increase trend, and in the third stage, the number of forest fires increased significantly. Fire risk is the most serious. In the fourth stage, due to the dual impact of precipitation and the province,s fire ban, the risk of forest hot spots was significantly reduced. [ABSTRACT FROM AUTHOR]

Forest fires cause extensive environmental damage, making early detection crucial for protecting both nature and communities. Advanced computer vision techniques can be used to detect smoke and fire. However, accurate detection of smoke and fire in forests is challenging due to different factors such as different smoke shapes, changing light, and similarity of smoke with other smoke-like elements such as clouds. This study explores recent YOLO (You Only Look Once) deep-learning object detection models YOLOv9, YOLOv10, and YOLOv11 for detecting smoke and fire in forest environments. The evaluation focuses on key performance metrics, including precision, recall, F1-score, and mean average precision (mAP), and utilizes two benchmark datasets featuring diverse instances of fire and smoke across different environments. The findings highlight the effectiveness of the small version models of YOLO (YOLOv9t, YOLOv10n, and YOLOv11n) in fire and smoke detection tasks. Among these, YOLOv11n demonstrated the highest performance, achieving a precision of 0.845, a recall of 0.801, a mAP@50 of 0.859, and a mAP@50-95 of 0.558. YOLOv11 versions (YOLOv11n and YOLOv11x) were evaluated and compared against several studies that employed the same datasets. The results show that YOLOv11x delivers promising performance compared to other YOLO variants and models. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of forest fires and critical points in activities related to extinguishing and transport of extinguishing agent to the fire site. With the increasing incidence of forest fires, there are also serious implications for the environment, ecosystems and communities. The relevance of this topic is indisputable, as forest fires are becoming more frequent and intense, with a consequent need for systematic analysis. In this paper, critical sites are identified and assessed, and a description of the equipment used to extinguish a particular fire is provided, with a description of the firefighting strategy in a difficult-to-access site in forest firefighting. This paper shows the effective solution in extinguishing forest fires and then in the design of measures to minimize this risk. We have also assessed the risk activities in this paper. The intent of this article is to show how to effectively extinguish a forest fire. The knowledge gained and recommendations made are aimed at improving firefighter preparedness, techniques and tactics to extinguish forest fires. [ABSTRACT FROM AUTHOR]

Forest fires diminish forests' ecological services, including carbon sequestration, water retention, air cooling, and recreation, while polluting the environment and endangering habitats. Despite considerable economic advancements, firefighting strategies remain less than optimal. This paper introduces the Bi-layer Predictive Ensemble (BIPE), an innovative machine learning model designed to enhance the accuracy and generalization of forest fire susceptibility mapping. BIPE integrates model-centric and data-driven strategies, employing automated methods such as 10-fold cross-validation and meta-learning to improve stability and generalization. During its 10-fold cross-validation, BIPE demonstrated excellent performance, with the Area Under the Curve (AUC) values ranging from 0.990 to 0.996 and accuracy levels consistently high, around 97%, underscoring its robust class separation ability and strong generalization across different datasets. Our results confirm that BIPE outperforms traditional high-performance models like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Convolutional Neural Network (CNN), showcasing its practical effectiveness and reliability on the data of nonlinear, high-dimensional, and complex interactions. Additionally, our forest fire susceptibility maps offer valuable complementary information for German forest fire management authorities, enhancing their ability to assess and manage fire risks more effectively. [ABSTRACT FROM AUTHOR]

Germination is an essential phenomenon in the life cycle of plants, and a variety of external and internal factors influence it. Fire and the produced smoke have been vital environmental stimulants for the germination of seeds in many plant species, like Leucospermum cordifolium and Serruria florida. These plants do not germinate at all if fire and smoke are not present. This phenomenon of germination in plant species has existed in the ecosystem since ancient times. Various studies to study the response of seeds to smoke and its extracts have been undertaken for stimulation of germination by burning various plant materials and bubbling the smoke produced through water. The application of plant-derived smoke and smoke water is well known for promoting germination, breaking dormancy, and checking abiotic stress. This significantly indicates that plant-derived smoke contains some bioactive metabolites responsible for the physiological metabolism of seed germination and is involved in enhancing seed vigor. The present review deals with the ancient use of smoke and smoke extracts for seed priming, the cost-efficient method of its preparation, the mode of action of karrikins relating to its perception by plants, and its significant effects on various crops, including its ability to check biotic and abiotic stresses. [ABSTRACT FROM AUTHOR]

Wildfires pose a significant natural hazard, particularly in Mediterranean regions where they cause lasting and often irreversible damage. Despite ongoing prevention efforts, large-scale fires continue to devastate vast areas of forest and agricultural land, a situation exacerbated by climate change and human activities. Proactive approaches are essential to mitigate this phenomenon, requiring reliable and robust susceptibility models. This study aims to evaluate the reliability of wildfire susceptibility models by incorporating a temporal perspective into the assessment of machine learning models. Unlike traditional approaches that focus solely on spatial validation, this research integrates temporal analysis to assess the accuracy of future predictions. Focusing on the province of Jijel, Algeria, the study employs six well established machine learning algorithms—K-Nearest Neighbors (KNN), Histogram-Based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Logistic Regression (LR)—to assess the wildfire exposure. The findings reveal that temporal sampling in wildfire susceptibility models decreases performance by 2–20% compared to spatial sampling, indicating the significant influence of temporal aspect on model reliability and overestimation due to data partitioning based solely on spatial sampling. Additionally, the study highlights a trade-off between performance and reliability: while HGB emerges as the best-performing model, its reliability is relatively low. In contrast, LR, despite being the least performant model, demonstrates the highest reliability and consistency across both spatial and temporal evaluations. [ABSTRACT FROM AUTHOR]

Forest fires pose a critical problem for natural environments and human settlements, necessitating effective risk management approaches. This study focuses on forest fire risk (FFR) mapping in the Antalya Forest, southern Turkey, by analyzing different criteria. Extensive literature research identifies nearly twenty criteria, which we re-evaluate based on expert opinions and study area characteristics, leading to the selection of four main criteria and fourteen sub-criteria. We process the data using Geographic Information System (GIS) software and calculate weights using the Analytical Hierarchy Process (AHP) and Ordered Weighted Average (OWA) techniques. The main criteria are topographic, meteorological, land use, and forest structure. In the AHP sub-criteria, precipitation, tree species, distance to settlement areas, and elevation receive high values. We classify the resultant FFR maps into five risk classes using both the Jenks Natural Breaks method and equal interval classification. We evaluate the accuracy of our maps using Receiver Operating Characteristic (ROC) analysis and Area Under Curve (AUC) values, based on historical forest fire data. The equal interval classification shows decreased alignment with historical fire data, especially for the AHP method, which performs significantly worse. Both OWA and AHP methods show better performance with Jenks classification compared to equal interval classification, indicating that Jenks Natural Breaks more effectively captures natural groupings in the data, making it a more suitable choice for risk mapping. Applying AHP and OWA in FFR mapping benefits regional forest management and highlights the universal applicability of these methodologies for broader environmental hazard assessments under changing climates. [ABSTRACT FROM AUTHOR]

Forest fires are one of the most dangerous disasters that threaten the natural environment, life, and diversity worldwide. The frequency of these fires and the size of the impact area have been increasing in recent years. Remote sensing methods are frequently used to detect areas affected by forest fires, to map the burned areas, to follow the course of fires, and to reveal verious statistical data. In this study, forest fires that occurred on 16.07.2023 and 22.08.2023 in Çanakkale province were analyzed using Landsat-8/9 and Sentinel-2 satellite images and various remote sensing indices. By using the images before and after the fires, the burned areas were determined and the performance of different indices were compared. The areas affected by fires were revealed using dNBR (Differenced Normalized Burn Ratio), RBR (Relative Burn Ratio), and dNDVI (Differenced Normalized Difference Vegetation Index) indices. The fire-affected areas were calculated as 3,244.41 hectares (ha) and 4,292.37 ha for the July and August fires with Landsat-8/9 images, respectively; and 3,312.08 ha and 4,445.03 ha with Sentinel-2 images, respectively. In addition, the accuracy analysis of the areas calculated using different indices was performed. By comparing the results of the analysis and accuracy assessment, the performances of Landsat-8/9 and Sentinel-2 images were determined. According to the results obtained, the Overall Accuracy values of the areas affected by fires were between 0.76 – 0.89, Kappa statistical values were between 0.52 – 0.78, and the highest value in the calculation of the burned areas was the dNBR index for both Landsat-8/9 and Sentinel-2 images. [ABSTRACT FROM AUTHOR]

Wildfires have recently destroyed large areas of forest in Central Europe. After a fire, the post-fire vegetation succession rebuilds previously lost biomass and carbon. Nevertheless, ground vegetation and tree regeneration, as well as belowground biomass, are largely neglected in allometric biomass models. Moreover, most models are calibrated for undisturbed forests, which differ from burned forests in terms of site conditions and species composition. We adapted a model using plant cover and shoot length of herb, graminoid and bryophyte species (PhytoCalc), and a single tree model using shoot length and root collar diameter of juvenile trees to estimate aboveground biomass (AGB) of post-fire species and growth groups, and extended them to estimate belowground biomass. We sampled the most dominant species from the herb and moss layer and the tree regeneration of burned and unburned Scots pine (Pinus sylvestris L.) forests in NE Germany. We successfully calibrated post-fire vegetation models and showed that models for undisturbed forests from the literature mostly underestimate post-fire AGB. Using models from the literature for clear-cuts often improved the estimation of post-fire AGB, but still failed to estimate AGB accurately for some species. Inaccurate estimation of post-fire biomass with these other models can be explained by high irradiance and increased nutrient availability on burned sites, which affect tissue and wood density. We showed that own species- and growth group-specific allometric models calibrated to post-fire vegetation are required to correctly estimate the total (above- and belowground) biomass of post-fire vegetation needed to calculate carbon storage. [ABSTRACT FROM AUTHOR]

Until recently, forest fires were considered a rare phenomenon in the temperate forests of Central Europe because of the moderate summer temperatures and the humid climate. However, many of those forests (monocultures of Picea abies , Norway Spruce) were affected by bark beetle infestations in the past years and recent fires such as that in the Bohemian-Saxon Switzerland in 2022 raised widespread debates about the effects of forest mortality on fuel accumulation and hence fire occurrence and severity. Here, we aim to investigate the association of fire radiative power (FRP) and burn severity with fuel types during the fire in Bohemian-Saxon Switzerland in 2022. We enhanced a European fuel type classification with a class for dead and dying spruce and mapped pre-fire fuel types using data on habitats, tree species, bark beetle disturbances and tree coverage. FRP was derived from observations of the Visible and Infrared Radiometer Suite (VIIRS) sensors. Burn severity was quantified by the differenced normalized burn ratio (dNBR) through pre- and postfire Landsat 8, 9, and Sentinel-2 images and was assessed in the field using the Composite Burn Index (CBI). We found the highest FRP at sites with dead spruce forests. Burn severity was moderate with high variability across all fuel types but highest severities occurred in dead spruce stands. The dNBR correlated positively with char height and the presence of torched trees (R  = 0.48, P < .05), especially in dead spruce stands, likely due to the large amount of dry fine woody debris and the presence of initial natural regeneration. Our results demonstrate that surface fuel accumulation from past bark beetle disturbances resulted in more intense fires (higher FRP, char height and torching) and higher burn severity. The results highlight the need for improved cross-border fire risk management in Central European temperate forests that were previously not considered as fire-prone. [ABSTRACT FROM AUTHOR]

Global change and disturbance ecology, including the risks and benefits of wildfires for humans, sustainability of ecosystems and biodiversity, is a current research topic in applied science. Fires and their impacts are often considered in the context of climate change, carbon dioxide emissions and air pollution. Despite a significant decline in wildfires at the global scale in recent decades (cf. Global Wildfire Information System (GWIS)), it is a widespread conviction that the burned area is increasing due to global warming. In an attempt to identify how this discrepancy has arisen, we analysed IPCC reports from 2018–2023 via text mining including word frequency analyses and compared considerations about wildfires and fire weather with findings from ecology and public information on the internet. Both a negativity bias and repetition bias were identified. Numerous examples of disasters and models indicating a global increase of wildfires are composed of alarming messages. Examples of decreasing wildfires and the global decline are much less frequently communicated. Important facts are ignored, especially in summaries for policymakers. Measured against fire-ecological conditions and benefits for the nature, alarming trends and risks due to climate change are exaggerated. We therefore call for a comprising and differentiated reflection of ecological conditions and processes in the future. [ABSTRACT FROM AUTHOR]

The study focuses on identifying fireburning and burnt areas in a large-scale forest fire that occurred in Xintian County, China, in October 2022. To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study on the recognition and mapping accuracy of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), based on Sentinel-1B and 2A imagery. Initially, three sets of pre-fire, during-fire, and post-fire remote sensing data were preprocessed. Various feature parameters from Sentinel-1B and 2A imagery were combined to identify firerelated land cover types. The experimental results revealed that: (i) During the pre-fire period, the SVM method demonstrated superior accuracy compared to the other two methods. The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. (ii) In the during-fire period, RF method exhibited higher accuracy compared to the other two methods with peak fire identification accuracy reached by combining spectral and Normalized Burn Ratio (NBR) index features at an OA of 95.43%. (iii) In the post-fire period, SVM demonstrated superior accuracy compared to other methods. The highest accuracy of 94.97% was achieved when combining spectral and radar features from Sentinel-1B imagery, highlighting the effectiveness of using spectral and radar backward scattering coefficients as feature parameters to enhance forest fire recognition accuracy for burnt areas. These findings suggest that appropriate machine learning algorithms should be employed under different conditions to obtain more precise identification of forest fire areas. This study provides technical support and empirical evidence for extracting and mapping forest fire areas while assessing damage caused by fires. [ABSTRACT FROM AUTHOR]

The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burnt areas. This study presents a methodology using UAV-LiDAR data to quantify malleability and elasticity in four burnt areas (1970, 1995, 2008 and 2015) through the statistical analysis of different metrics related to height structure and diversity (Height mean, 99th percentile and Coefficient of Variation), coverage, relative shape and distribution strata (Canopy Cover, Canopy Relief Ratio and Strata Percent Coverage), and canopy complexity (Profile Area and Profile Area Change). In general terms, malleability decreases over time in forest ecosystems that have been affected by wildfires, whereas elasticity is higher than what has been determined in previous studies. However, a particular specificity has been detected from the 1995 fire, so we can assume that there are other situational factors that may be affecting ecosystem resilience. LiDAR metrics and uni-temporal sampling between burnt sectors and control aids are used to understand community resilience and to identify the different recovery stages in P. halepensis forests. [ABSTRACT FROM AUTHOR]

In addressing the issue of path planning for UAVs ( unmanned aerial vehicles) in plateau forest fire extinguishment, a three-dimensional geographic spatial model is constructed using a grid-based approach based on DEM ( digital elevation model) data. The impacts of altitude, ambient wind, and fire temperature on UAV performance were analyzed. A UAV path planning model was established under multiple constraints, with the objective of finding the shortest path while minimizing energy consumption. An improved A∗ algorithm, combining weighted Euclidean and Chebyshev distances, was developed for solving the model. Results indicate that, compared to the traditional A∗ algorithm, the improved version reduces solution time by 36. 73%, providing more optimal paths. Altitude has a minor impact on total path length and duration but significantly affects UAV energy consumption. In high- altitude environments (3 500 m) compared to plain environments (630 m), UAV energy consumption increases by 48. 00% . Wind speed and direction primarily influence energy consumption and total duration. At 3 500 m altitude and 8 m / s wind speed, flying with the wind reduces energy consumption by 64. 74% and shortens duration by 41. 01% compared to flying against the wind. A rise in atmospheric temperature near the fire to 40 ℃ has minimal effect on UAV path planning and energy consumption. These findings provide a theoretical basis for applying UAVs in path planning for plateau forest fire extinguishment. [ABSTRACT FROM AUTHOR]

Forest fire is a natural disaster that threatens a large part of the world's forests. Considering the destructive effects of forest fires, the preparation of forest fire probability maps can be a very valuable step towards reducing such effects. This study proposes two novel wrapper feature selection-based ensemble models that combine the strengths of Support vector machine (SVM) and Artificial neural networks (ANN) with bagging (bootstrap aggregating) and Genetic Algorithm (GA) for forest fire susceptibility mapping in the Jerash and Ajloun provinces of Jordan. By integrating multiple learning algorithms through ensemble methods, we aim to increase predictive accuracy and enhance the robustness of our findings. GA was employed for feature selection utilizing data from 207 forest fire locations and fourteen predictor variables. 70% of the forest fire locations (145 locations) were used in the training phase, and the remaining 60% (62 locations) were employed to validate the models. The accuracy of the models was measured by using the area Under the Receiver Operating Characteristic (AUROC). The AUROC for single SVM, single ANN, GBSVM, and GBANN models was 69.3%, 66.9%, 70.9%, and 70.4% in the validation phase, respectively. The results showed that wrapper and bagging-based ensemble models did much better than single models. This shows that combining techniques can improve modeling performance for mapping the risk of forest fires. [ABSTRACT FROM AUTHOR]

This study introduces a methodology that integrates SHAP (SHapley Additive exPlanations) analysis with Random Forest (RF) classification to enhance the prediction accuracy of fire severity across diverse forested regions in Türkiye. Leveraging a comprehensive forest fire database spanning from 2018 to 2022 and utilizing the Google Earth Engine (GEE) platform, 436 fire events ranging from 9 to 53,764.1 hectares were automatically detected and mapped using the difference Normalized Burn Ratio (dNBR). Subsequently, a robust fire severity model was developed by incorporating 19 variables, including biophysical, topographic, climatic, and vegetation-related factors. The RF classification achieved noteworthy performance metrics, with an overall accuracy of 0.75, a Kappa value of 0.61, and a macro-average AUC value of 0.88. Furthermore, the integration of SHAP analysis provided insightful contributions to the RF classification model, elucidating the impacts of individual input features. Notably, variables such as NDMI (Normalized Difference Moisture Index), LAI (Leaf Area Index), and LWVI (Land Water Vegetation Index) emerged as significant influencers, followed by WSPD (Wind Speed) and LST (Land Surface Temperature). Additionally, an analysis of fire severity distribution across fuel types (FMT) revealed intricate patterns, underscoring the complex relationships between vegetation composition and fire behavior. The findings of this study have implications for forest management and wildfire risk assessment, offering valuable insights for decision-making processes. Furthermore, the integration of SHAP analysis with RF classification enhances the interpretability and transparency of machine learning-based fire severity prediction models, contributing to the advancement of fire management strategies. [ABSTRACT FROM AUTHOR]

Forest fires pose a great threat to the global ecological environment as well as human life and the safety of property. Formulating effective policies for preventing forest fires is not only a scientific concern but also an urgent need for government management. Canada and China both have extensive forest areas but have different fire management strategies. Canada primarily focuses on fire suppression while China pays more attention on fire prevention. This difference led to significant discrepancies in the forest fire regimes between the two countries, providing an opportunity to explore the impact of fire management policies on forest fire. By analyzing the fire occurrences in Canada and China since 1990, combining the fire prevention funds and fire management strategies, this paper discussed the influence of different policies on fire occurrence. Previously, Canada's forest suppression strategy has been widely recognized internationally, but recent widespread fires indicate that its fire management policy may still require further improvement to cope with future global warming. Although China's fire prevention strategy can effectively control current forest fires, the lack of fundamental theories on forest fires and the backwardness of fire prevention technology and equipment may increase the likelihood of major forest fires in the future. As global warming continues to intensify in the future, the length of the forest fire season and the intensity of fires will increase, making it urgent to develop more effective forest fire prevention and suppression policies to achieve sustainable development. [ABSTRACT FROM AUTHOR]

Previous evaluation models for cultural relic buildings in relation to fire risk fail to consider the necessity for effective firefighting and rescue capabilities in complex forest environments. This paper incorporates variables, including those pertaining to forest fires and climatic conditions, into the assessment index system. The hierarchical analysis method and the local punishment-incentive variable weighting method are employed to introduce a compensation coefficient score. A model for the evaluation of firefighting and rescue capability in a continuous area of cultural relic buildings in conjunction with the surrounding forest environment has been developed. The firefighting and rescue capability of the Yuelu Mountain scenic spot was evaluated at 73.91 (level III) using the fixed weight method and 69.52 (level IV) using the variable weight method. The variable weight method proved to be a more accurate approach for evaluating the status and importance of dynamic targets, thus enabling a more precise evaluation of the comprehensive evaluation area. The evaluation results inform the formulation of targeted improvement measures for enhancing the firefighting and rescue capabilities of cultural relic buildings. [ABSTRACT FROM AUTHOR]

The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies. [ABSTRACT FROM AUTHOR]

Forests are vital for life on Earth but are threatened by forest fires, which have significant impacts on climate change both locally and globally. This study examines a forest fire that lasted from 15 to 26 February 2019 in Karnataka, India, using the Weather Research and Forecasting model with Chemistry (WRF-Chem) model to analyze the effects and atmospheric spread of fire-emitted aerosols. Model simulations are analyzed to understand the horizontal and vertical transport and radiative effects of the fire. The results show high aerosol levels and smoke particles reaching up to 3.5 km altitude and above. The fire raised near-surface air temperatures by ~1–1.5 °C. The net atmospheric forcing due to the fire over the affected area ranged from approximately 10 to 14 W/m2, resulting in heating rates between about 0.002 and 0.005 K/day in the impacted region. [ABSTRACT FROM AUTHOR]

The article describes a methodology for assessing social damage during fires in mountain forest belts of the Russian Federation, associated with an increase in the overall mortality of the population as a result of long-term and intense smoke in urbanized areas. The relevance of the topic and the demand for the results are associated with the growing number of forest fires, including in mountainous areas, with changing consequences, both for the ecology of regions and human economic activity, and for the life and health of the population, changing consistent long-term smoke pollution of urbanized areas. Recently, many countries have been paying more and more attention to the pollution of the atmosphere of populated areas, namely, air quality is a determining factor for the health and life expectancy of the population. The methodology presented in the article allows us to estimate the concentration of fine particles in space at a given distance from a forest fire and to estimate the possible social damage associated with the formation of general mortality as a result of smoke pollution. An example of testing this methodology is given using the example of long-term smoke in Moscow in 2010 [ABSTRACT FROM AUTHOR]

The forest fires constitute a major danger for the forests in the Western Algerian region. They are caused by a combination of several factors, particularly climatic and anthropogenic, which are often amplified by the composition of vegetation that is considered highly flammable during the dry season. The priority for action to deal with this phenomenon is to strengthen monitoring resources and apply preventive silvicultural measures to avoid the outbreak of fires, without forgetting efforts to educate and raise public awareness. A systematic examination of data retrieved from the General Forests Directorate of Forest Fires stretching between 2003 and 2017 reveals the spatiotemporal, spatial and temporal evolution of fires in the Western Algerian region. The statistical approach applied in this study allowed us to identify the periods in which our forests are mostly vulnerable to fires, allowing the programing of an action plan for an effective forest fire management. The fire danger map produced by calculating the Fire Danger Index can be a decision-support tool for forest managers to locate areas at high risk in order to take preventive measures to limit the loss of natural resources, properties, and even human lives. [ABSTRACT FROM AUTHOR]

The very hot summers of recent years have led to an increase in the number of large forest fires in Europe. We investigated four large fire sites in Brandenburg and Saxony (Germany) up to 4 years after the fires with a focus on studying the post-fire fungal communities. In this context, we documented two species of Agaricomycetes associated with mosses, which are common but particularly emerge on burnt areas, i.e., Arrhenia bryophthora sp. nov. and Bryopistillaria clavarioides sp. nov. The former is an agaric with omphalinoid habit that causes the dieback of the common moss Ceratodon purpureus; the latter is a clavarioid fungus associated with either Ceratodon purpureus or another common moss, Funaria hygrometrica. Both fungal species appear to be restricted to recently burnt areas and have otherwise not been observed on or in close vicinity of these mosses. Herein, we describe these fungi macro- and micromorphologically as well as on a molecular basis and discuss their taxonomic position and potential lifestyles. [ABSTRACT FROM AUTHOR]

Forest fires represent a paramount natural disaster of global concern. Zhejiang Province has the highest forest coverage rate in China, and forest fires are one of the main natural disasters impacting forest management in the region. In this study, we comprehensively analyzed the spatiotemporal distribution of forest fires based on the MODIS data from 2013 to 2023. The results showed that the annual incidence of forest fires in Zhejiang Province has shown an overall downward trend from 2013 to 2023, with forest fires occurring more frequently in winter and spring. By utilizing eight contributing factors of forest fire occurrence as variables, three models were constructed: Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The RF and XGBoost models demonstrated high predictive ability, achieving accuracy rates of 0.85 and 0.92, f1-score of 0.84 and 0.92, and AUC values of 0.892 and 0.919, respectively. Further analysis using the RF and XGBoost models revealed that elevation and precipitation had the most significant effects on the occurrence of forest fires. Additionally, the predictions of forest fire risk generated by the RF and XGBoost models indicated that the incidence rate is high in the southern part of Zhejiang Province, particularly in the Wenzhou and Lishui areas, as well as in the southwest of the Hangzhou area and the north of the Quzhou area. In the future, the forest fire risk in this area can be predicted using site factors with the RF and XGBoost models, providing a scientific reference for forest management in Zhejiang Province and aiding in the prevention and mitigation of the impacts of forest fires. [ABSTRACT FROM AUTHOR]