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Forest fires present significant threats to both natural ecosystems and human communities, highlighting the need for advanced detection systems for early intervention and mitigation. This paper aims to develop a novel forest fire detection system by integrating Internet of Things technology, machine learning algorithms, and real-time data from weather APIs. The proposed system utilizes IoT sensors to gather environmental parameters and weather conditions, enhancing the accuracy of fire detection. A machine learning model trained on this data distinguishes between normal environmental fluctuations and signs of fire. Additionally, an image processing algorithm is employed to analyze images for the presence of smoke or flames. Integration and testing of the system demonstrate its promising results in terms of accuracy and efficiency compared to traditional methods. This paper contributes to technology-driven solutions for forest fire management, with significant implications for environmental conservation and public safety. [ABSTRACT FROM AUTHOR]

The 9th International Conference on Forest Fire Research, organised by the Forest Fire Research Center of the Association for Developmental of Industrial Aerodynamics every 4 years since 1990, was held in November 2022 in Coimbra, Portugal. The conference was held in conjunction with the 17th International Wildland Fire Safety Summit, sponsored by the International Association of Wildland Fire. The number and quality of the submissions for this joint event was very high, and the authors were encouraged to submit a full paper to a special issue of the International Journal of Wildland Fire (IJWF). Given the large number of submissions, the Journal decided to publish the special issue in four parts. Part 1 was published in January 2023, with eight papers, Part 2 in March 2023, with 10 papers and Part 3 in June 2023 with 15 papers. This fourth part presents 10 original papers, in three areas: Decision Support Systems and Tools (3), Risk Assessment & Reduction (3) and the Wildland Urban Interface (4). All the papers in this special issue are published Open Access. Part 4 of the special issue that resulted from the 9th International Conference on Forest Fire Research includes 10 papers on various aspects of wildland fire research. Parts 1, 2 and 3 were published in January, March and June 2023 respectively. All papers in the special issue are published Open Access. [ABSTRACT FROM AUTHOR]

The Special Issue "Impact of climate warming and disturbances on forest ecosystems" underscores the critical importance of understanding how forests respond to these environmental challenges and the legacy of past management practices. Forest ecosystems are facing significant challenges due to ongoing climate change, characterized by rising temperatures and increased frequency of extreme events. The rapid pace of climate change is altering disturbance patterns and the adaptability of forests, which have a direct impact on ecosystem services that contribute to human well-being. This Special Issue features 11 research papers from nine countries. Some key outputs from these research papers include evidence on how climate change is already impacting forest ecosystems. For instance, the climatic envelope of many forest species has shifted due to global warming, making species more vulnerable, especially in lower elevations and at the edges of their distribution. Urgent adaptive measures in forest management are necessary to address this challenge. Climate change also affects vegetation phenology, tree growth, stand productivity, reproduction rates, and stand regeneration. Remote sensing data and ecological modeling techniques play a crucial role in monitoring and understanding these changes, especially in remote regions where field measurements are limited. The rising frequency and intensity of extreme events like droughts, windstorms, and forest fires require enhanced prediction and automatic monitoring. Leveraging machine learning tools and remote sensing data is imperative. This Special Issue provides insights into the intricate relationships among forests, climate change, and human interventions. We provide further research recommendations for the quantification and automated monitoring of forest fires and the management of forests to better withstand storms and increase their resilience to climate change. [ABSTRACT FROM AUTHOR]

Forest fires present a substantial environmental challenge, constituting a dual menace to human life and ecological well-being. The imperative for forest fire prevention and management underscores the indispensability of robust detection, prediction, and behavior analysis systems. This scholarly paper offers a thorough exploration of diverse methodologies and techniques employed in the realms of forest fire detection, prediction, and behavior analysis. Encompassing both ground-based and aerial surveillance systems, as well as remote sensing technologies, machine learning-based approaches, and social media-driven systems, the survey provides a comprehensive overview of the multifaceted landscape of forest fire monitoring. In addition to delineating the current state of the art, the paper critically examines the challenges and limitations inherent in existing systems. Furthermore, it imparts valuable insights into prospective avenues for research and development within this domain. In essence, this paper underscores the significance of amalgamating data from various sources and employing diverse analytical methods to enhance our comprehension of forest fire behavior. The overarching goal is to foster the formulation of efficacious strategies for mitigating the impact of this environmental menace. [ABSTRACT FROM AUTHOR]

Forest fire is a major disturbance in mountainous ecosystems across the world. It impacts the composition, functioning, and structure of the forest ecosystem. Demographic pressure and the related climatic factors are thought to exacerbate the danger of forest fires, although there is a lack of statistical data to support this, particularly in South Asian countries with limited historical records. The study looks at how forest fires interact with their causative variables, such as population, topography, vegetation, and climate regimes, in the forests of Northern India. In order to do so, the paper presents a 15-year trend analysis of forest burn scars. This investigation confirmed the utility of principal component analysis using multisource spatial data and correlation matrices. The findings reveal that the occurrence and intensity of forest fires in mountainous locations are influenced by anthropogenic activities, forests near roads and settlements are extremely vulnerable. Based on the analysis, we emphasize the need for restructuring the existing policies like National Action Plan for forest fires and forest fire prevention and management scheme into more practical science-based policies. These findings may be utilized in planning effective fire control processes in time, and the technique provided in this study may be used to discover potential fire-risk zones in other areas. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) and quadrotors are being used in an increasing number of applications. The detection and management of forest fires is continually improved by the incorporation of new economical technologies in order to prevent ecological degradation and disasters. Using an inner-outer loop design, this paper discusses an attitude and altitude controller for a quadrotor. As a highly nonlinear system, quadrotor dynamics can be simplified by assuming several assumptions. Quadrotor autopilot is developed using nonlinear feedback linearization technique, LQR, SMC, PD, and PID controllers. Often, these approaches are used to improve control and to reject disturbances. PDPID controllers are also deployed in the tracking and surveillance of smoke or fire by intelligent algorithms. In this paper, the efficiency using a combined PDPID controllers with adjustable parameters have been studied. The performance was assessed by simulation using matlab Simulink. The computational study conducted to assess the proposed approach showed that the PD-PID combination presented in this paper yields promising outcomes. [ABSTRACT FROM AUTHOR]

Forest fires are one of the world's deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding an attention mechanism to the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection performance of the model and the robustness of the model. In addition, a random mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model processing load and to improve the detection speed. Experimental results on self-built forest-fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This paper presents a method for mapping the spatial distribution of canopy fuel loads (CFLs) in alignment with their natural variability and three-dimensional spatial distribution. The approach leverages an object-based machine learning framework with UAV multispectral data and photogrammetric point clouds. The proposed method was developed in the mixed forest of the natural protected area of "Sierra de Quila", Jalisco, Mexico. Structural variables derived from photogrammetric point clouds, along with spectral information, were used in an object-based Random Forest model to accurately estimate CFLs, yielding R2 = 0.75, RMSE = 1.78 Mg, and an average B i a s r e l = 18.62%. Canopy volume was the most significant explanatory variable, achieving a mean decrease in impurity values greater than 80%, while the combination of texture and vegetation indices presented importance values close to 20%. Our modelling approach enables the accurate estimation of CFLs, accounting for the ecological context that governs their dynamics and spatial variability. The high precision achieved, at a relatively low cost, encourages constant updating of forest fuels maps to enable researchers and forest managers to streamline decision making on fuel and forest fire management. [ABSTRACT FROM AUTHOR]

Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development, regulatory measures, and their implementation elevating the environment. Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe. Therefore, the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent. Therefore, this paper focuses on the design of automated forest fire detection using a fusion-based deep learning (AFFD-FDL) model for environmental monitoring. The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction. The combination of the handcrafted features using histogram of gradients (HOG) with deep features using SqueezeNet and Inception v3 models. Besides, an optimal extreme learning machine (ELM) based classifier is used to identify the existence of fire or not. In order to properly tune the parameters of the ELM model, the oppositional glowworm swarm optimization (OGSO) algorithm is employed and thereby improves the forest fire detection performance. A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects. The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to highlight the interest of focusing public policies for wildfire management on behavioral changes supported by sustainable development projects dedicated to the Algerian forest heritage. Thus, the Aurès region in the eastern part of Algeria will be used as an example to illustrate the proposed new strategy. Design/methodology/approach: The proposed method, guided by projects' management in sustainable development, consists of developing a responsible citizenship strategy focused on behavioral changes of citizens. Therefore, the title of the proposed method: The Construct of Responsible Citizenship. Findings: In order to cope with forest fires, the proposed approach highlights the interest of promoting responsible citizenship. Likewise, it outlines a tool for sustaining behavioral changes based on the principle of continuous improvement and field follow-up using a multicriteria approach known as "goal programming." Practical implications: The proposed new forest fire protection plan addresses the shortcomings in Algeria's current forest fire management policy, which appears unable to deal with the increasing severity of forests' fire risk that the country has been experiencing in recent decades. Originality/value: The aim is to highlight the interest of investment in forest fires prevention within the framework of the sustainable development of Algerian forest heritage, specifically, to work toward citizen subcontracting of the Algerian forest heritage. [ABSTRACT FROM AUTHOR]

The surface fine dead fuel moisture content (FFMC) is an important factor in predicting forest fire risk and is influenced by various meteorological factors. Many prediction methods rely on temperature and humidity as factors, resulting in poor model prediction accuracy under rainfall conditions. At the same time, there is an increasing number of methods based on machine learning, but there is still a lack of comparison with traditional models. Therefore, this paper selected the broad-leaved forest tree species Quercus mongolica and the coniferous forest species Pinus sylvestris var. mongolica in Northeast China. Taking surface dead fine fuel as the research object, we used indoor simulated rainfall experiments to explore the impact of rainfall on the surface dead fuel moisture content. The prediction model for surface dead fuel moisture content was modified by the direct estimation method. Finally, using field data, the direct estimation method and convolution neural network (CNN) model were used in the comparison. The rainfall simulation results showed that the indoor fuel moisture content had a logarithmic increasing trend. Rainfall and previous fuel moisture content had a significant impact on the fuel moisture content prediction model, and both the relational model and nonlinear model performed well in predicting fuel moisture content under indoor rainfall conditions. Under field conditions, humidity, temperature and rainfall played a significant role in fuel moisture content. Compared with the unmodified direct estimation method, the modified direct estimation method significantly improved the prediction accuracy and the goodness of fit (R2) increased from 0.85–0.94 to 0.94–0.96. Mean absolute error (MAE) decreased from 9.18–18.33% to 6.86–10.74%, and mean relative error (MRE) decreased from 3.97–17.18% to 3.53–14.48%. The modified direct estimation method has higher prediction accuracy compared with the convolutional neural network model; the R2 value was above 0.90, MAE was below 8.11%, and MRE was below 8.87%. The modified direct estimation method had the best prediction effect among them. This study has a certain reference value for the prediction model of surface fuel moisture content in post-rainfall fire risk assessment and is also of great significance for forest fire management in Northeast China. [ABSTRACT FROM AUTHOR]

Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes an optimal parameter logistic regression (OPLR) model, conducts forest fire risk zoning research under the optimal spatial analysis scale and model parameters, and establishes a forest fire risk prediction model. The results showed that the spatial unit of the optimal spatial analysis scale in the study area was 5 km and that the prediction accuracy of the OPLR was about 81%. The climate was the main driving factor of forest fires, while temperature had the greatest influence on the probability of forest fires. According to the forest fire prediction model, mapping the fire risk zoning, in which the medium- and high-risk area was 6021.13 km2, accounted for 9.99% of the study area. The results contribute to a better understanding of forest fire management based on the local environmental characteristics of the Liangshan Yi Autonomous Prefecture and provide a reference for related forest fire prevention and control management. [ABSTRACT FROM AUTHOR]

The fire season of 2023 was record-breaking in Canada given the number, severity, and intensity of wildfires. Factors contributing to the number of fires are likely to get worse in the future because of the interaction and complexity of many factors such as climate change and the legacy of decades of successful wildfire suppression. Further complicating wildfire management is the ever-continuing expansion of the wildland-urban interface and subsequent increases in values at risk. Fire management solutions including prevention and suppression will require novel approaches given rising costs and logistical complications. This paper examines the payment concept for ecosystem services whereby ranchers are paid to graze cattle in targeted high-priority areas in the wildland urban interface. Grazing can be ecologically appropriate and has long been used in fire-prone ecosystems in Europe. With some key considerations, implementing PES (Payments for Ecosystem Services) could reduce fire risk, support agricultural producers, and enhance societal protection. [ABSTRACT FROM AUTHOR]

A new physical and mathematical formulation for the extinguishing of a forest fire with an inhibitor solution distributed using an explosive charge is proposed in this paper. It is assumed that the water and inhibitor are in the liquid-dispersion state and move together with the medium. The inhibitor allows the disruption of exothermic reactions, while being partially consumed. The results of numerical simulations have shown that the presence of an inhibitor makes it possible to reduce the required mass of the extinguishing composition for the successful suppression of a forest fire. It is shown that these results are in good agreement with known experimental data on the efficiency of replacing pure water with an inhibitor solution. [ABSTRACT FROM AUTHOR]

Forest fires are becoming a more significant problem in Central Europe, but their danger is not as high as that in Southern Europe. The exception, however, is forest fires occurring in disturbed areas (windthrow and bark beetle outbreak areas), which are comparable in severity and danger to the most serious forest fires. In this study, we describe the current situation in Central European countries in terms of fire protection for disturbed areas in managed forests and forest stands left to spontaneously develop (secondary succession). If a country has regulations and strategies in this area, they are often only published in the local language. In this review, we combine information from all Central European countries and summarize it in a unified international language, provide an opportunity for local authorities to express their own experiences, and integrate data from worldwide scientific research. Thus, this paper may be considered a universal guide for managing fire protection and preparedness in disturbed areas and can serve as a reference for the establishment of strict legislative rules at the state level. These laws must be obligatory for all stakeholders in individual countries. The motivation for this study was two large forest fires in an area left to spontaneously develop in the Bohemian Switzerland National Park in the Czech Republic and Harz Mountains in Germany in the summer of 2022. These incidents revealed that fire prevention legislation was inadequate or nonexistent in these areas. The strategy of the European Union is to increase the size of protected areas and spontaneous development areas. Therefore, we consider it necessary to provide governments with relevant information on this topic to create conditions for better management of these destructive events. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Form@re is the property of Firenze University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, forests play an important role in stabilizing the ecological balance, being one of the most important components of the biosphere. Due to the vital activity of forests, the gas composition of the atmosphere is normalized. Mass forest fires have the opposite effect. They cause irreparable damage to flora and fauna, contribute to the melting of Arctic ice, an increase in the Earth's temperature, and destabilization of the carbon balance. The purpose of this study is to develop an algorithm for calculating the moisture content and time of forest fire maturation of forest combustible materials. To achieve this goal, the main factors determining a forest fire hazard have been studied, as well as a review of existing methods for assessing forest fire danger and scientific papers on forest pyrology. As a result of the analysis of the research aimed at studying the rate of drying of forest combustible materials (FCM), depending on the physical properties and environmental parameters, a dependency of changes in moisture content over time was obtained. With its help, knowing the initial moisture content of FCM, it is possible to calculate the periods of fire maturation for each component of the forest plantation. Cooperative use of the resulting algorithm with a digital twin of a forest stand makes it possible to identify the most fire-hazard forest areas and estimate the period of their fire-prone maturation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing YOLOv5‐based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high‐quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small‐scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph‐embedded YOLOv5 forest fire detection framework, which can improve the performance of small‐scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi‐head self‐attention (MSA) module before each YOLO head. The experimental results on FLAME and our self‐built fire dataset show that our proposed model improves the accuracy of small‐scale forest fire detection. The proposed model achieves high performance in real‐time performance by fully utilizing the advantages of the YOLOv5 framework. [ABSTRACT FROM AUTHOR]

The magnitude of the recent forest fires, the time required to extinguish them, and the damage they caused have attracted the attention of all humanity. If the current trend continues, it will cause great irreversible losses. There is a great need for scientific studies to prevent or reduce the damages of these fires. In this context, this paper proposes algorithms and mathematical models for generating the routes of unmanned aerial vehicles to detect forest fires that may occur especially in regions far from residential areas. A novel heuristic dispatching rule and a simulation-based optimization algorithm are proposed. The striking features of the proposed algorithms are that the routes are created with a focus on minimizing the fire probabilities. The uncertainties and dynamics of real-life are also considered. Various scenarios have experimented on a realistic case. Experimental results and findings are promising. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have driven the recent advances in fire detection. However, existing methods require large-scale labeled samples to train data-hungry networks, which are difficult to collect and even more laborious to label. This paper applies unsupervised domain adaptation (UDA) to transfer knowledge from a labeled public fire dataset to another unlabeled one in practical application scenarios for the first time. Then, a transfer learning benchmark dataset called Fire-DA is built from public datasets for fire recognition. Next, the Deep Subdomain Adaptation Network (DSAN) and the Dynamic Adversarial Adaptation Network (DAAN) are experimented on Fire-DA to provide a benchmark result for future transfer learning research in fire recognition. Finally, two transfer tasks are built from Fire-DA to two public forest fire datasets, the aerial forest fire dataset FLAME and the large-scale fire dataset FD-dataset containing forest fire scenarios. Compared with traditional handcrafted feature-based methods and supervised CNNs, DSAN reaches 82.5% performance of the optimal supervised CNN on the testing set of FLAME. In addition, DSAN achieves 95.8% and 83.5% recognition accuracy on the testing set and challenging testing set of FD-dataset, which outperform the optimal supervised CNN by 0.5% and 2.6%, respectively. The experimental results demonstrate that DSAN achieves an impressive performance on FLAME and a new state of the art on FD-dataset without accessing their labels during training, a fundamental step toward unsupervised forest fire recognition for industrial applications. [ABSTRACT FROM AUTHOR]

Fire was a serious natural disturbance to forest ecosystems. In this study, a national-scale forest fire risk model (RF_NTL) was proposed in mainland China by incorporating a new human activity factor (NTL) from nighttime light data. For objective verification of model's performance, the logistic regression model (LR) and random forest model (RF) were also built in this paper. Results showed that the RF_NTL model fitted with an AUC value up to 0.95, which was superior to the conventional LR model and RF model. The RF_NTL model also had good generalization ability and stability on annual and monthly scales, helping to accurately forecast forest fire risk. Moreover, the spatial distribution of forest fire risk based on RF_NTL model was detailed and reasonable on a national scale. Forest fire high-risk areas in China were mainly concentrated in the northeast and southwest regions. This study could provide references for future relevant research. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Indonesia is a tropical country that experiences forest fires every year. Forest fires occur due to a prolonged summer season. A major effect of forest fires that commonly occurred in Indonesia is the respiratory disorder and the visual impairment caused by the thick smoke that can experienced by the people who lives around the forest. The effects of forest fire fumes when the forest fire spread across the bigger land or area are also experienced by neighboring countries such as Malaysia, Singapore and Brunei Darussalam. Forest is a habitat for many animals, one of which is a bird. Birds are able to communicate with their colonies through sound. The bird sound during communication to the group can be in the form of calls, marriage invitations, and warnings of danger or threat of forest fires. This paper presents bird sounds classification study using one of Deep Learning (DL) algorithms i.e. Convolutional Neural Network (CNN) method. The CNN method is used to classify bird sounds in the two conditions: (1) under normal circumstances or conditions, and (2) under threated or panic condition. The bird sound data used in this study were collected from the local birds in Indonesia. The classification result of two bird sounds based on CNN method achieved up to 96.45%. This paper is a preliminary study for bio inspired early warning of forest fires based on the sound of birds. [ABSTRACT FROM AUTHOR]

This paper shows the relevance of the problem of fire hazard in the forests of the Volga Federal District (VFD) of European Russia. The Nesterov index and the Selyaninov hydrothermal coefficient (HTC) are considered as indicators of fire hazard. The changes in climatic conditions in the VFD during 1955–2018 are shown; a trend towards warming and an increase in aridity in the study region were revealed. The repeatability of various fire hazard classes from May to September was calculated using the Nesterov method. It is shown that in July, the most dangerous situation was in the south of the VFD, where the repeatability of class IV fire hazard reached 27%. Using the HTC index, the degree of aridity of the district in the summer period was estimated. The frequency of the most arid conditions (HTC < 0.5) increases from the north to the south of the district, from 6% (Kirov Region) to 47% (Orenburg Region). Using the TT index, the potential thunderstorm danger in the VFD was assessed. With the help of the constructed maps, the hotspots of the most probable occurrence of thunderstorms were detected. The use of Rosstat data on the number of forest fires from 1992 to 2020 made it possible to consider the spatiotemporal distribution of forest fires in 14 administrative regions of the VFD. The distribution of the number of fires by the regions is shown depending on their forest cover and season. The peak of the number of fires was revealed in 2010, when the entire territory of the study region was covered by a severe drought, as a result of which the area of forests covered by fire increased many times over. In recent years (since 2017), there has been an increase in the area of burned forest due to the active phase of climate warming. [ABSTRACT FROM AUTHOR]

In recent years, forest-fire monitoring methods represented by deep learning have been developed rapidly. The use of drone technology and optimization of existing models to improve forest-fire recognition accuracy and segmentation quality are of great significance for understanding the spatial distribution of forest fires and protecting forest resources. Due to the spreading and irregular nature of fire, it is extremely tough to detect fire accurately in a complex environment. Based on the aerial imagery dataset FLAME, this paper focuses on the analysis of methods to two deep-learning problems: (1) the video frames are classified as two classes (fire, no-fire) according to the presence or absence of fire. A novel image classification method based on channel domain attention mechanism was developed, which achieved a classification accuracy of 93.65%. (2) We propose a novel instance segmentation method (MaskSU R-CNN) for incipient forest-fire detection and segmentation based on MS R-CNN model. For the optimized model, the MaskIoU branch is reconstructed by a U-shaped network in order to reduce the segmentation error. Experimental results show that the precision of our MaskSU R-CNN reached 91.85%, recall 88.81%, F1-score 90.30%, and mean intersection over union (mIoU) 82.31%. Compared with many state-of-the-art segmentation models, our method achieves satisfactory results on forest-fire dataset. [ABSTRACT FROM AUTHOR]

In this paper, the occurrence of large forest fires in the management area of ŠPD "Srednjobosanska šume" in 2020 was analyzed with regard to forest types and management classes. The period of April-May 2020 was characterized as a period with a particularly high occurrence of forest fires, where the burnt area was 1,030.36 ha, and the material damage was 1,893,559.15 KM. The causes of the fires are unknown, as are the instigators. In most cases, the human factor was found to be the cause, and in three cases it was determined that the fire was caused intentionally. The danger of certain categories of forests (and forest land) from fire is different. The biggest financial damage was caused by the burning of areas under young cultures, i.e. due to damage to seedlings. The aim of the research was to assess the number, frequency and damage caused by fires, as well as the risk of certain categories of forests from fires. Such research is essential for assessing the degree of threat to forests and forest land from fire. [ABSTRACT FROM AUTHOR]

There is a worldwide risk of fire spread due to the mismanagement of stubble in agricultural land. In 2019, 3.6% of the forest area was damaged by fires in Turkey due to stubble burning. The burning of agriculture residues negatively affects soil properties, air quality and water resources. This finally threatens humankind sustainability. However, there is a lack of information analyzing this problem from the current policies in Turkey. Therefore, this paper reviews the legislation currently applied to understand the specific competencies to achieve sustainable fire and forest management. A holistic analysis of the legal regulations and practices to prevent stubble burning in Turkey shown here the farmer´s perception of the use of fire as an agriculture tool and the relevance of stubble burning to explain the location and recurrence of wildfires. Then, we discussed the relevance to shed light on how effective are the laws to avoid stubble burning and its impact on the environment. We claim for an update of the legislation to allow the farmers to manage the stubble and encourage the policymakers to develop new strategies to compost the stubble and achieve sustainable management within the Sustainable Development Goals of the United Nations that will contribute to achieving the Land Degradation Neutrality Challenge. [ABSTRACT FROM AUTHOR]

Forest landscapes across western North America (wNA) have experienced extensive changes over the last two centuries, while climatic warming has become a global reality over the last four decades. Resulting interactions between historical increases in forested area and density and recent rapid warming, increasing insect mortality, and wildfire burned areas, are now leading to substantial abrupt landscape alterations. These outcomes are forcing forest planners and managers to identify strategies that can modify future outcomes that are ecologically and/or socially undesirable. Past forest management, including widespread harvest of fire‐ and climate‐tolerant large old trees and old forests, fire exclusion (both Indigenous and lightning ignitions), and highly effective wildfire suppression have contributed to the current state of wNA forests. These practices were successful at meeting short‐term demands, but they match poorly to modern realities. Hagmann et al. review a century of observations and multi‐scale, multi‐proxy, research evidence that details widespread changes in forested landscapes and wildfire regimes since the influx of European colonists. Over the preceding 10 millennia, large areas of wNA were already settled and proactively managed with intentional burning by Indigenous tribes. Prichard et al. then review the research on management practices historically applied by Indigenous tribes and currently applied by some managers to intentionally manage forests for resilient conditions. They address 10 questions surrounding the application and relevance of these management practices. Here, we highlight the main findings of both papers and offer recommendations for management. We discuss progress paralysis that often occurs with strict adherence to the precautionary principle; offer insights for dealing with the common problem of irreducible uncertainty and suggestions for reframing management and policy direction; and identify key knowledge gaps and research needs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for estimating the burned area of forest fires using the TabNet transformer-based architecture. Forest fires pose a significant threat to ecosystems, and accurate estimation of the affected area is essential for effective disaster management and resource allocation. We conducted a comprehensive analysis of various Machine Learning (ML) and Deep Learning (DL) methods, including Random Forest, Neural Networks, Neural Architecture Search (NAS), TabNet with Transformers, and Self-Supervised Learning with Autoencoders, to identify the most accurate and efficient model for area estimation. Our experiments employed a publicly available dataset, UCI Forest Fires, containing a combination of meteorological, geospatial, and categorical data. We implemented a thorough preprocessing pipeline that included handling categorical variables, standardization, and feature engineering. The results demonstrate that TabNet outperforms other methods, achieving state-of-the-art accuracy and generalization in predicting the target variable with a Mean Squared Error (MSE) of 2319 in training and 7781 in testing. [ABSTRACT FROM AUTHOR]

Forest areas or green infrastructure have become a fundamental economic and social factor which has made it possible to generate new sources of employment and to maximize the use of basic resources. Nevertheless, good conservation of this type of infrastructure is a challenge. This is due to the problems deriving from, on the one hand, the increase of its physical scope and, on the other, poor management or no management at all. The Spanish region of Galicia is a historic place of natural wealth, especially concerning forest resources, wherein 2/3 of its territory is forest area from where more than half of the Spanish wood supply comes.This paper seeks to create a mapping of major forest fire disturbances on the Galician territory over extended periods of time. To achieve this, an automated multitemporal detection process based on vegetation indices and unsupervised learning is developed. The objective is to obtain data heterogeneity in terms of vegetation state, land use, and image properties, allowing a better understanding of forest land disturbances and improving their management. [ABSTRACT FROM AUTHOR]

Biomass estimates for shrub‐dominated ecosystems in southern California have been generated at national and statewide extents. However, existing data tend to underestimate biomass in shrub vegetation types are limited to one point in time, or estimate aboveground live biomass only. In this study, we extended our previously developed estimates of aboveground live biomass (AGLBM) based on the empirical relationship of plot‐based field biomass measurements to Landsat normalized difference vegetation index (NDVI) and multiple environmental factors to include other vegetative pools of biomass. AGLBM estimates were made by extracting plot values from elevation, solar radiation, aspect, slope, soil type, landform, climatic water deficit, evapotranspiration, and precipitation rasters and then using a random forest model to estimate per‐pixel AGLBM across our southern California study area. By substituting year‐specific Landsat NDVI and precipitation data, we created a stack of annual AGLBM raster layers for each year from 2001 to 2021. Using these AGLBM data as a foundation, we developed decision rules to estimate belowground, standing dead, and litter biomass pools. These rules were based on relationships between AGLBM and the biomass of the other vegetative pools derived primarily from peer‐reviewed literature and an existing spatial data set. For shrub vegetation types (our primary focus), rules were based on literature estimates by the postfire regeneration strategy of each species (obligate seeder, facultative seeder, obligate resprouter). Similarly, for nonshrub vegetation types (grasslands, woodlands) we used literature and existing spatial data sets specific to each vegetation type to define rules to estimate the other pools from AGLBM. Using a Python language script that accessed Environmental Systems Research Institute raster geographic information system utilities, we applied decision rules to create raster layers for each of the non‐AGLBM pools for the years 2001–2021. The resulting spatial data archive contains a zipped file for each year; each of these files contains four 32‐bit tiff files for each of the four biomass pools (AGLBM, standing dead, litter, and belowground). The biomass units are grams per square meter (g/m2). We estimated the uncertainty of our biomass data by conducting a Monte Carlo analysis of the inputs used to generate the data. Our Monte Carlo technique used randomly generated values for each of the literature‐based and spatial inputs based on their expected distribution. We conducted 200 Monte Carlo iterations, which produced percentage uncertainty values for each of the biomass pools. Results showed, using 2010 as an example, mean biomass for the study area and percentage uncertainty for each of the pools as follows: AGLBM (905.4 g/m2, 14.4%); standing dead (644.9 g/m2, 1.3%); litter (731.2 g/m2, 1.2%); and belowground (776.2 g/m2, 17.2%). Because our methods are consistently applied across each year, the data produced can be used to inform changes in biomass pools due to disturbance and subsequent recovery. As such, these data provide an important contribution to supporting the management of shrub‐dominated ecosystems for monitoring trends in carbon storage and assessing the impacts of wildfire and management activities, such as fuel management and restoration. There are no copyright restrictions on the data set; please cite this paper and the data package when using these data. [ABSTRACT FROM AUTHOR]

Wildfires represent a major concern for the forests in the Mediterranean basin regions since global climate change enhances the frequency and the severity of this natural ecological disturbance agent. Wildfire models (fire spread or fire behavior models) can be of valuable help for fire risk assessment as well as for planning mitigation actions which mainly concern forest management. These models need generalized vegetation (fuel) data sets at the scales which are significant for fire/forest management. However, despite of the efforts to develop methods for generating global fuel data sets, there is a lack of fine resolution regional scale fuel data sets for the Mediterranean basin. The aim of this work is to build a fuel data set based on existing data sources for fire/forest management, risk assessment and decision-support purposes. Thus, this paper presents a database (building methods and data) of wildland fuels for Mediterranean basin vegetation stands types and in particular for those in Corsica, which gathers together most common input parameters needed by wildfire's models at several vegetation scales (i.e., stand, elements, particles). Moreover, an example is provided where simulations are run with a specific wildfire model by using the fuel input data extracted from the database. Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National (French) Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The harmonization of European NFIs generates a reliable data source available to extend and generalize the methodology presented herein to other regions or countries. • Vegetation/Fuel database for fire risk and forest management. • Stand-level attributes meaningful at landscape scale. • Data compiled in layers to account for the structure and arrangement of vegetation/fuels. [ABSTRACT FROM AUTHOR]

Introduction: The behavior of forest fire is a complex phenomenon, and accurate simulation of forest fire is conducive to emergency response management after ignition. In order to further understand the characteristics of forest fire spread and the applicability of WRF-Fire in China, which is a coupled fire-atmospheric wildfire model, this study simulated a high-intensity forest fire event that occurred on October 17, 2022 in Xintian County, southern Hunan Province. Methods: Based on the fire-atmosphere coupled WRF-Fire model, we used high-resolution geographic information, meteorological observation and fuel classification data to analyze the forest fire behavior. At the same time, the simulation results are compared with the fire burned area observed by satellite remote sensing forest fire monitoring data. Results: The study found that, the simulated wind speed, direction and temperature trends are similar to the observation results, but the simulated wind speed is overestimated, the dominant wind direction is N, and the temperature is slightly underestimated. The simulated wind field is close to the actual wind field, and the simulation results can show the spatial and temporal variation characteristics of the local wind field under complex terrain while obtaining the high-resolution wind field. The simulated fire burned area is generally overestimated, spreading to the north and southwest compared with the observed fires, but it can also capture the overall shape and spread trend of the fire well. Discussion: The results show that the model can accurately reproduce the real spread of fire, and it is more helpful to forest fire management. [ABSTRACT FROM AUTHOR]

In the Mediterranean basin, coniferous reforestation mainly comprises forest stands highly susceptible to fires. When silvicultural treatments have not been performed for decades after plantation, these stands often exhibit high vertical and horizontal tree density, along with a significant occurrence of lying and standing deadwood, thereby increasing the fuel load. On average, these pine forests are characterized by high values of above-ground biomass, ranging from 175 to 254 Mg ha−1 for the younger and the older ones, respectively. The theoretical heat energy produced per surface unit, in the case of the total combustion of the above-ground biomass, is also high, varying from 300 to 450 MJ ha−1 depending on the stage of stand development. In this study, we demonstrated the importance of silvicultural interventions in reducing the pyrological potential in pine reforested stands located in southern Italy, also giving attention to the water savings needed during extinction phases. In detail, we applied a preliminary mathematical reaction-diffusion model aimed at predicting the development of forest fires. The model was applied using data obtained through the estimation of the pyrological potential in terms of heat energy produced per surface unit (1 hectare) and the variation in the critical surface intensity. We verified that, when silvicultural interventions are applied, they induce a reduction of heat energy ranging between 17 and 21%, while the extinguishing water saved ranges between 600 and 1000 Mg ha−1. Moreover, when the silvicultural interventions are implemented, the probability of the transition from surface fire to crown fire can be reduced by up to 31%. The most effective results on fire risk mitigation are mainly obtained when thinning aimed at reducing canopy and tree density is carried out in the younger phases of the reforested pine stands. [ABSTRACT FROM AUTHOR]

Wildfires generate substantial smoke containing fine particulate matter (PM2.5) that adversely impacts health. This study develops machine learning models integrating pre-wildfire factors like weather and fuel conditions with post-wildfire health impacts to provide a holistic understanding of smoke exposure risks. Various data-driven models including Support Vector Regression, Multi-layer Perceptron, and three tree-based ensemble algorithms (Random Forest, Extreme Gradient Boosting (XGBoost), and Natural Gradient Boosting (NGBoost)) are evaluated in this study. Ensemble models effectively predict PM2.5 levels based on temperature, humidity, wind, and fuel moisture, revealing the significant roles of radiation, temperature, and moisture. Further modelling links smoke exposure to deaths from chronic obstructive pulmonary disease (COPD) and lung cancer using age, sex, and pollution type as inputs. Ambient pollution is the primary driver of COPD mortality, while age has a greater influence on lung cancer deaths. This research advances atmospheric and health impact understanding, aiding forest fire prevention and management. [ABSTRACT FROM AUTHOR]

The Nanwenghe Nature Reserve is located in the Daxing'anling forest region, which is abundant meadow resource which is prone to fire in fire season every year. In this paper, the change in the soil chemical properties of burned stands was emphasized. The results showed that: (1) Soil organic matter (SOM) concentration in burned stands showed an extremely significant difference from that in the control stands (p < 0.01). Alkali-hydrolysable N, available K and total N in the burned stands were significantly lower than those in the control stands (p < 0.05). (2) Total P, total K, pH and available P in the burned stands were not significantly different from the concentrations in the control stands (p > 0.05). (3) Available K, total N, total P and total K concentration showed no significant difference from June to September. The pH values increased with a variance of 8%, and pH values in July, August and September were significantly different from those in June. SOM concentrations obtained in July, August and September increased significantly, compared to the concentrations in June (p < 0.05). Alkali-hydrolysable N showed a decreasing trend for all conditions. [ABSTRACT FROM AUTHOR]

In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5 anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach's sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables' impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has become an important means of forest fire monitoring because it has the advantages of wide coverage, few ground constraints and high dynamics. When utilizing satellites for forest fire hotspot monitoring, two types of ground hotspots, agricultural and other fire hotspots can be ruled out through ground object features. False forest fire hotspots within forested areas must be excluded for a more accurate distinction between forest fires and non-forest fires. This study utilizes spatio-temporal data along with time-series classification to excavate false forest fire hotspots exhibiting temporal characteristics within forested areas and construct a dataset of such false forest fire hotspots, thereby achieving a more realistic forest fire dataset. Taking Hunan Province as the research object, this study takes the satellite ground hotspots in the forests of Hunan Province as the suspected forest fire hotspot dataset and excludes the satellite ground hotspots in the forests such as fixed heat sources, periodic heat sources and recurring heat sources which are excavated. The validity of these methods and results was then analyzed. False forest fire hotspots, from satellite ground hotspots extracted from 2019 to 2023 Himawari-8/9 satellite images, closely resemble the official release of actual forest fires data and the accuracy rate in the actual forest fire monitoring is 95.12%. This validates that the method employed in this study can improve the accuracy of satellite-based forest fire monitoring. [ABSTRACT FROM AUTHOR]

Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities. [ABSTRACT FROM AUTHOR]

The unprecedented wildfires of 2017 in Portugal, particularly affecting the Centre Region, resulted in more than 100 deaths and numerous other dramatic socioeconomic and environmental impacts. Due to the unparalleled magnitude of these events, the extent of the burned area, and the catastrophic consequences, forest and fire management were placed at the center stage of public and political concerns, receiving wide mass media attention and amplification likely to shape public opinion and influence policy decisions. Through a quasi-qualitative content analysis approach, this article focuses on the media narratives conveyed by the news issued over two years (N = 1056) by eight newspapers of different periodicities and geographical scopes regarding the wildfires of October 2017 in the Matas do Litoral—coastal public-managed forest areas located in the Centre Region of Portugal. The analysis focuses on three main dimensions: the agents and actors mentioned and their power-related positions, the geographical coverage of the newspapers, and the relevance ascribed to emotions and feelings in the media narratives about the wildfires. The results demonstrate the relevance attributed in media narratives to the views of institutional agents and the negligence of local people's voices. When particularly highlighting the material impacts of the wildfires, media narratives tended to focus less on people's emotions and other non-tangible issues. The variation in these narratives according to the geographical scope of the newspapers and the publication time of the news strongly points out the need to address the heterogeneous character of the media coverage of wildfires. [ABSTRACT FROM AUTHOR]

The article focuses on James Spencer Whipple's role as New York State's, U.S., "Indian Expert" and his influence on Progressive Era conservation policies. Topics include his push to bring Indigenous peoples under state control while promoting state reforestation efforts; the impact of his policies on Indigenous sovereignty and natural resource management; and his political alliances that benefited non-Indigenous landholders in Seneca territory, Native American nation territory.

This study developed a comprehensive framework using Google Earth Engine to efficiently generate a forest fire inventory dataset, which enhanced data accessibility without specialized knowledge or access to private datasets. The framework is applicable globally, and the datasets generated are freely accessible and shareable. By implementing the framework in Peninsular Malaysia, significant forest fire factors were successfully extracted, including the Keetch–Byram Drought Index (KBDI), soil moisture, temperature, windspeed, land surface temperature (LST), Palmer Drought Severity Index (PDSI), Normalized Vegetation Index (NDVI), landcover, and precipitation, among others. Additionally, this study also adopted large language models, specifically GPT-4 with the Noteable plugin, for preliminary data analysis to assess the dataset's validity. Although the plugin effectively performed basic statistical analyses and visualizations, it demonstrated limitations, such as selectively dropping or choosing only relevant columns for tests and automatically modifying scales. These behaviors underscore the need for users to perform additional checks on the codes generated to ensure that they accurately reflect the intended analyses. The initial findings indicate that factors such as KBDI, LST, climate water deficit, and precipitation significantly impact forest fire occurrences in Peninsular Malaysia. Future research should explore extending the framework's application to various regions and further refine it to accommodate a broader range of factors. Embracing and rigorously validating large language model technologies, alongside developing new tools and plugins, are essential for advancing the field of data analysis. [ABSTRACT FROM AUTHOR]

The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements. [ABSTRACT FROM AUTHOR]

In Colombia's Orinoco, wildfires have a profound impact on ecosystem dynamics, particularly affecting savannas and forest–savanna transitions. Human activities have disrupted the natural fire regime, leading to increased wildfire frequency due to changes in land use, deforestation, and climate change. Despite extensive research on fire monitoring and prediction, the quantification of fuel accumulation, a critical factor in fire incidence, remains inadequately explored. This study addresses this gap by quantifying dead organic material (detritus) accumulation and identifying influencing factors. Using Brown transects across forests with varying fire intensities, we assessed fuel loads and characterized variables related to detritus accumulation over time. Employing factor analysis, principal components analysis, and a generalized linear mixed model, we determined the effects of various factors. Our findings reveal significant variations in biomass accumulation patterns influenced by factors such as thickness, wet and dry mass, density, gravity, porosity, and moisture content. Additionally, a decrease in fuel load over time was attributed to increased precipitation from three La Niña events. These insights enable more accurate fire predictions and inform targeted forest management strategies for fire prevention and mitigation, thereby enhancing our understanding of fire ecology in the Orinoco basin and guiding effective conservation practices. [ABSTRACT FROM AUTHOR]

Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past 2 decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a 30 m resolution global forest burn severity (GFBS) dataset of the degree of biomass consumed by fires from 2003 to 2016. To develop this dataset, we used the Global Fire Atlas product to determine when and where forest fires occurred during that period and then we overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. We compared the GFBS dataset against the Canada Landsat Burned Severity (CanLaBS) product, showing better agreement than the existing Moderate Resolution Imaging Spectrometer (MODIS)-based global burn severity dataset (MOdis burn SEVerity, MOSEV) in representing the distribution of forest burn severity over Canada. Using the in situ burn severity category data available for the 2013 wildfires in southeastern Australia, we demonstrated that GFBS could provide burn severity estimation with clearer differentiation between the high-severity and moderate-/low-severity classes, while such differentiation among the in situ burn severity classes is not captured in the MOSEV product. Using the CONUS-wide composite burn index (CBI) as a ground truth, we showed that dNBR from GFBS was more strongly correlated with CBI (r=0.63) than dNBR from MOSEV (r=0.28). RdNBR from GFBS also exhibited better agreement with CBI (r=0.56) than RdNBR from MOSEV (r=0.20). On a global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS are similar to those of MOSEV, MOSEV tends to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. The GFBS dataset provides a more precise and reliable assessment of burn severity than existing available datasets. These enhancements are crucial for understanding the ecological impacts of forest fires and for informing management and recovery efforts in affected regions worldwide. The GFBS dataset is freely accessible at 10.5281/zenodo.10037629 (He et al., 2023). [ABSTRACT FROM AUTHOR]