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Over recent decades, the Arctic has begun facing a range of climate-related challenges, from rising temperatures to melting ice caps and permafrost thaw, with significant implications for ecosystems and human well-being. Addressing the health impacts of these issues requires a comprehensive approach, integrating scientific research, community engagement, and policy interventions. This study conducts a literature review to assess the effects of climate change on human health in northern latitudes and to compile adaptation strategies from the Arctic countries. A literature search was performed between January and April 2024 for papers published after 2000, using the electronic databases Web of Science, Pubmed, Science Direct, Scopus, Google Scholar, and eLibrary.RU, with specific questions formulated to direct the search: (i) What are the climate changes? (ii) How does climate change affect human health? (iii) What adaptation measures and policies are required? The key phrases "climate change", "human health", "adaptation practices", and "Arctic" were employed for searching. Ultimately, 56 relevant studies were identified, reviewing health risks such as infectious diseases, mental health issues, and diseases connected with extreme weather events; wildfires and their associated pollution; permafrost degradation; pure water; and food quality. The paper also examines mitigation and adaptation strategies at all levels of governance, emphasizing the need for international cooperation and policy action to combat negative health outcomes, investments in healthcare infrastructure, emergency preparedness, and public health education. Incorporating diverse perspectives, including Indigenous knowledge, Community-Based Adaptation, EcoHealth and One Health approaches, is crucial for effectively addressing the health risks associated with climate change. In conclusion, the paper proposes adaptation strategies to mitigate the health impacts of climate change in the Arctic. [ABSTRACT FROM AUTHOR]

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and the debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires' geoenvironmental impacts does not fit within the scope of this paper. In the end, and in no exhaustive way, some of the areas requiring future research are highlighted. [ABSTRACT FROM AUTHOR]

This concept paper explores possible collective identities for a future wildland fire workforce. Taking inspiration from the work of futurists who foresee an end to the dominant fire exclusion/suppression paradigm, and assuming that an emerging fire restoration/resilience paradigm shift replaces it, this paper engages in speculative explorations of the process and product of this paradigm shift with respect to the future collective identities of a workforce conducting ecological fire management. Social constructionist assumptions from symbolic interactionist sociological theory, Gramscian political theory's concept of hegemony, and new social movement theory's concept of collective identity all provide the intellectual foundations for the discussion. This concept paper argues that in order to actualize a paradigm shift, more than advances in scientific research or reforms of government policies will be required—the wildland fire community will need to become (or join) a social movement engaged in collective actions. An imaginary social movement, the "Greenfire revolution," is invented to help illustrate how the selected theories and concepts might apply in the social construction of ecological fire management and the collective identities of its future workforce. [ABSTRACT FROM AUTHOR]

Forests provide a wide range of ecosystem services which are important for achieving sustainable development. Anthropogenic climate change has led to the increased frequency and severity of forest fires, which imply losses of valuable ecosystem services. This paper provides a methodological framework based on Multiple Criteria Decision Aid methods for determining relative regional vulnerabilities associated with forest fires. Different notions of ex-post vulnerability are defined based on the forest area burned and the frequency of forest fires, and their relation to the regions' area and certain socioeconomic characteristics. The climatic drivers of forest fire occurrence are explored by linking forest fires with summer and spring temperatures and precipitation, using econometric count data analysis. The methodology is applied to Greece and its administrative regions for the period 2000–2022. Ex-post vulnerability of regions to forest fires based on physical and socioeconomic characteristics is calculated, and expected changes in the frequency of fires of specific size classes conditional on the evolution of mean seasonal regional temperature and precipitation according to IPCC scenarios are predicted. Relative vulnerability estimates and the impact of specific climatic drivers on forest fires will be useful in designing policies for preserving forests as natural capital and promoting sustainability. [ABSTRACT FROM AUTHOR]

The call for papers for this Special Issue Iidentifies contemporary humanity as experiencing a global "biogeochemical ... political, economic, technological, ethical, and therefore, biocultural" crisis and asks scholars to consider how "religion may function as an adaptive or maladaptive presence" in response. Unasked is the adaptive capacity of scholarship as a crisis response. When buildings fall in earthquakes, or cities burn in wildfires, or second stories flood, few people just keep on doing what they were doing, "with a change of focus". This is "studying Rome while it burns". It's time to put out the fire if we can and survive it if we cannot. We scholar/teachers can't go on doing the same things and expecting different results. Unprecedented circumstances call for unprecedented actions in response. What would actual crisis responses on our part look like? What steps do we need to take as human beings in response to this crisis? How will that affect us as professionals? Seeking an ecology, rather than unanimity, of action and thought, and guided by Brian Walker's resilience theory and a number of Indigenous scholars, I suggest a process of reintegration, analogous to regenerative agriculture, which is at once both socio-cultural and ecological. This process, necessarily rooted in place, progressively situates us experientially in a dynamic, creative, and relational world characterized by connection, collaboration, and relation. As scholars, we will find forms of discovery, discussion, and dissemination that share these qualities. As teachers, we will model this world to our students and embody it in our classrooms and curricula. [ABSTRACT FROM AUTHOR]

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099. [ABSTRACT FROM AUTHOR]

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [ABSTRACT FROM AUTHOR]

Wildfires are a significant problem in Irkutsk Oblast. They are caused by climate change, thunderstorms, and human factors. In this study, we use the Random Forest machine learning method to map the wildfire susceptibility of Irkutsk Oblast based on data from remote sensing, meteorology, government forestry authorities, and emergency situations. The main contributions of the paper are the following: an improved domain model that describes information about weather conditions, vegetation type, and infrastructure of the region in the context of the possible risk of wildfires; a database of wildfires in Irkutsk Oblast from 2017 to 2020; the results of an analysis of factors that cause wildfires and risk assessment based on Random Forest in the form of fire hazard mapping. In this paper, we collected and visualized data on wildfires and factors influencing their occurrence: meteorological, topographic, characteristics of vegetation, and human activity (social factors). Data sets describing two classes, "fire" and "no fire", were generated. We introduced a classification according to which the probability of a wildfire in each specific cell of the territory can be determined and a wildfire risk map built. The use of the Random Forest method allowed us to achieve the following risk assessment accuracy indicators: accuracy—0.89, F1-score—0.88, and AUC—0.96. The comparison of the results with earlier ones obtained using case-based reasoning revealed that the application of the case-based approach can be considered the initial stage for deeper investigations with the use of Random Forest for more accurate forecasting. [ABSTRACT FROM AUTHOR]

Forest fires pose a catastrophic threat to Earth's ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle (UAV) based on a lightweight forest fire recognition model, Fire-Net, which has a multi-stage structure and incorporates cross-channel attention following the fifth stage. This is to enable the model's ability to perceive features at various scales, particularly small-scale fire sources in wild forest scenes. Through training and testing on a real-world dataset, various lightweight convolutional neural networks were evaluated on embedded devices. The experimental outcomes indicate that Fire-Net attained an accuracy of 98.18%, a precision of 99.14%, and a recall of 98.01%, surpassing the current leading methods. Furthermore, the model showcases an average inference time of 10 milliseconds per image and operates at 86 frames per second (FPS) on embedded devices. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue on recent advances in hydrological modeling. It provides an introduction to hydrological models and their role in water resources operations and management. The editorial discusses the historical development of hydrological modeling, from empirical equations to sophisticated numerical models, and highlights the advancements in computing technology and data assimilation techniques that have improved the predictive capabilities of these models. It also summarizes the papers featured in the special issue, which cover topics such as machine learning in assessing the performance of hydrological models, improved estimation of extremes using adaptive filtering methods, and the impact of climate change on snow seasonality and water supply. The editorial concludes by emphasizing the importance of continued application and validation of novel techniques, particularly those involving machine learning and data assimilation, in hydrological modeling. [Extracted from the article]

Wildfires have long been one of the critical environmental disasters that require a careful monitoring system. An intelligent system has the potential to both prevent/extinguish the fire and deliver urgent requirements postfire. In recent years, unmanned aerial vehicles (UAVs), with the ability to detect missions in high-risk areas, have been gaining increasing interest, particularly in forest fire monitoring. Taking a large-scale area involved in a fire into consideration, a single UAV is often insufficient to accomplish the task of covering the whole disaster zone. This poses the challenge of multi-UAVs optimum path planning with a key focus on limitations such as energy constraints and connectivity. To narrow down this issue, this paper proposes a deep reinforcement learning-based trajectory planning approach for multi-UAVs that permits UAVs to extract the required information within the disaster area on time. A target area is partitioned into several identical subareas in terms of size to enable UAVs to perform their patrol duties over the subareas. This subarea-based arrangement converts the issue of trajectory planning into allowing UAVs to frequently visit each subarea. Each subarea is initiated with a risk level by creating a fire risk map optimizing the UAV patrol route more precisely. Through a set of simulations conducted with a real trace of the dataset, the performance outcomes confirmed the superiority of the proposed idea. [ABSTRACT FROM AUTHOR]

The identification of wildfires is a very complex task due to their different shapes, textures, and colours. Traditional image processing methods need to manually design feature extraction algorithms based on prior knowledge, and because fires at different stages have different characteristics, manually designed feature extraction algorithms often have insufficient generalization capabilities. A convolutional neural network (CNN) can automatically extract the deeper features of an image, avoiding the complexity and blindness of the feature extraction phase. Therefore, a wildfire identification method based on an improved two-channel CNN is proposed in this paper. Firstly, in order to solve the problem of the insufficient dataset, the dataset is processed by using PCA_Jittering, transfer learning is used to train the model and then the accuracy of the model is improved by using segmented training. Secondly, in order to achieve the effective coverage of the model for fire scenes of different sizes, a two-channel CNN based on feature fusion is designed, in which the fully connected layers are replaced by a support vector machine (SVM). Finally, in order to reduce the delay time of the model, Lasso_SVM is designed to replace the SVM in the original model. The results show that the method has the advantages of high accuracy and low latency. The accuracy of wildfire identification is 98.47% and the average delay time of fire identification is 0.051 s/frame. The wildfire identification method designed in this paper improves the accuracy of identifying wildfires and reduces the delay time in identifying them. [ABSTRACT FROM AUTHOR]

Wildfire is a growing concern worldwide with significant impacts on human lives and the environment. This study aimed to provide an overview of the current trends and research gaps in wildfire prediction by conducting a bibliometric analysis of papers in the Web of Science and Scopus databases. CiteSpace was employed to analyze the co-occurrence of keywords, identify clusters, and detect emerging trends. The results showed that the most frequently occurring keywords were "wildfire", "prediction", and "model" and the top three clusters were related to "air quality", "history", and "validation". The analysis of emerging trends revealed a focus on vegetation, precipitation, land use, trends, and the random forest algorithm. The study contributes to a better understanding of the research trends and gaps in wildfire prediction and provides recommendations for future research, such as incorporating new data sources and using advanced techniques. [ABSTRACT FROM AUTHOR]

Bushfires are classified as catastrophic disasters capable of inflicting significant destruction. The key detrimental consequences of bushfires include the loss of human lives, trauma within communities, economic losses and environmental damage. For example, the estimated economic loss from the September 2019 to March 2020 bushfires in New South Wales (Australia) was about AUD 110 billion, including more than 3000 burned houses. There has been a notable increase in both the frequency and intensity of bushfires, as clearly demonstrated by recent bushfire events. Bushfires are an intricate phenomenon that transpires across various spatial and temporal scales. Further, the changing circumstances of landscapes, vegetation patterns, weather conditions and ecosystems account for the complexity. Therefore, continual attention is essential for the development of bushfire management strategies. In this context, this paper undertakes a comprehensive literature review of bushfire management strategies, encompassing aspects such as bushfire prediction, detection, suppression and prevention. Based on the review, a bushfire management framework is proposed that can eliminate or successfully mitigate the consequences of bushfires. Further, the paper delves into the domains of fire weather conditions, the initiation of bushfires and the adverse consequences stemming from these fires. Both terrestrial and aerial remote sensing methods have proven to be effective in predicting and detecting bushfires. Nevertheless, a simple unique solution cannot be proposed for bushfire management. Changing weather conditions, topography and the geographic mix of asset types need to be considered when deciding on bushfire management strategies and their breadth and depth of application. [ABSTRACT FROM AUTHOR]

Wildfires cause great losses to the ecological environment, economy, and people's safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones. [ABSTRACT FROM AUTHOR]

The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model's predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies. [ABSTRACT FROM AUTHOR]

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires. [ABSTRACT FROM AUTHOR]

Ecosystems are frequently disturbed by fires that have an important impact on the soil environment and the composition of soil organisms. In order to provide a baseline for the current research and identify trends on the effects of wildland fire on soil environment and biological changes, the available literature was identified from the Web of Science database, covering the period from 1998/1998/1999 (the year of the earliest publication in this field) to 2023. A bibliometric analysis was performed and the data were visually displayed for the number of publications, countries, authors, research institutions, and keywords representing research hotspots. Specifically, the effects of wildland fire on the soil environment, on soil microorganisms and on soil fauna were analyzed. The results show that the annual number of publications describing effects of wildland fire on the soil environment and on soil microorganisms are increasing over time, while those describing effects on soil fauna are fewer and their number remains constant. The largest number of papers originate from the United States, with the United States Department of Agriculture as the research institution with the largest output. The three authors with the largest number of publications are Stefan H. Doerr, Manuel Esteban Lucas-Borja and Jan Jacob Keizer. The research hotspots, as identified by keywords, are highly concentrated on wildfire, fire, organic matter, and biodiversity, amongst others. This study comprehensively analyzes the current situation of the research on the effects of wildland fire on changes in the soil environment and organisms, and provides reference for relevant scientific researchers in this trend and future research hotspots. [ABSTRACT FROM AUTHOR]

Wildfire is one of the most critical natural disasters that poses a serious threat to human lives as well as ecosystems. One issue hindering a high accuracy of computer vision-based wildfire detection is the potential for water mists and clouds to be marked as wildfire smoke due to the similar appearance in images, leading to an unacceptable high false alarm rate in real-world wildfire early warning cases. This paper proposes a novel hybrid wildfire smoke detection approach by combining the multi-layer ResNet architecture with SVM to extract the smoke image dynamic and static characteristics, respectively. The ResNet model is improved via the SE attention mechanism and fully convolutional network as SE-ResNet. A fusion decision procedure is proposed for wildfire early warning. The proposed detection method was tested on open datasets and achieved an accuracy of 98.99%. The comparisons with AlexNet, VGG-16, GoogleNet, SE-ResNet-50 and SVM further illustrate the improvements. [ABSTRACT FROM AUTHOR]

Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect prediction accuracy, thereby providing rational suggestions for emergency decision-making. In this paper, we tested how different conditions of missing data affect the prediction accuracy of existing wildfire spread models and quantified the corresponding errors. The final experimental results suggest that it is necessary to judge the potential impact of data gaps based on the geographical conditions of the study area appropriately, as there is no significant pattern of behavior yet identified. This study aims to simulate the impact of data scarcity on the accuracy of wildfire spread prediction models in real scenarios, thereby enabling researchers to better understand the priority of different environmental variables for the model and identify the acceptable degree of missing data and the indispensable data attributes. It offers new insights for developing spread prediction models applicable to real-world scenarios and rational assessment of the effectiveness of model outcomes. [ABSTRACT FROM AUTHOR]

Wildland fires negatively impact forest biodiversity and human lives. They also spread very rapidly. Early detection of smoke and fires plays a crucial role in improving the efficiency of firefighting operations. Deep learning techniques are used to detect fires and smoke. However, the different shapes, sizes, and colors of smoke and fires make their detection a challenging task. In this paper, recent YOLO-based algorithms are adopted and implemented for detecting and localizing smoke and wildfires within ground and aerial images. Notably, the YOLOv7x model achieved the best performance with an mAP (mean Average Precision) score of 80.40% and fast detection speed, outperforming the baseline models in detecting both smoke and wildfires. YOLOv8s obtained a high mAP of 98.10% in identifying and localizing only wildfire smoke. These models demonstrated their significant potential in handling challenging scenarios, including detecting small fire and smoke areas; varying fire and smoke features such as shape, size, and colors; the complexity of background, which can include diverse terrain, weather conditions, and vegetation; and addressing visual similarities among smoke, fog, and clouds and the the visual resemblances among fire, lighting, and sun glare. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models. [ABSTRACT FROM AUTHOR]

The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk. [ABSTRACT FROM AUTHOR]

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen's slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed. [ABSTRACT FROM AUTHOR]

Wildfire causes environmental, economic, and human problems or losses. This study reviewed wildfires induced by lightning strikes. This review focuses on the investigations of lightning mechanisms in the laboratory. Also, the paper aims to discuss some of the modeling studies on lightning-induced wildfires at different geographical locations using satellite-recorded lightning data and different statistical analyses. This review established that irrespective of the different models used to predict lightning wildfires, there is still a lack of understanding of the lightning-strike ignition mechanism; few experiments have been modeled to establish the dynamics of lightning-strike ignition. Therefore, further research needs to be carried out in this area to understand lightning ignition. It was ascertained from the various statistical modeling that lightning-induced wildfires are exacerbated by the abundant availability of fuel with a lower moisture content and high lightning efficiency. Moreover, because of changes in the climate and weather conditions, i.e., harsh weather and climate conditions due to anthropogenic activities, lightning-induced ignition wildfires have increased over the years, and they are expected to increase in the future if the climate and weather conditions continue to aggravate. Although various modeling studies have identified that lightning-induced wildfires have increased recently, no preventive measures have been conclusively proposed to reduce lightning-caused wildfires. Hence, this aspect of research has to be given critical attention. This review presents information that gives a profound understanding of lightning-induced wildfires, especially factors that influence lightning wildfires, and the state-of-the-art research that has been completed to understand lightning-induced wildfires. [ABSTRACT FROM AUTHOR]

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

As climate change increases the frequency and severity of wildfires across the Western U.S., there is an urgent need for improved wildfire preparedness and responses. Socially marginalized communities are particularly vulnerable to wildfire effects because they disproportionately lack access to the resources necessary to prepare for and recover from wildfire and are frequently underrepresented in the wildfire planning process. As an exemplar of how to understand and improve preparedness in such communities, this research identified communities in Ventura County facing heightened marginalization and risk of wildfire using spatial analysis. Researchers then deployed a county-wide survey and held focus groups in two communities identified in the spatial analysis. Research revealed that non-English speakers, women, people of color, and newer residents in Ventura County are less prepared for wildfire than other groups. Based on these findings, this paper recommends an expansion of traditional risk mitigation programs, strengthened community engagement efforts, and strategies that increase community resources and leadership to decouple marginalization and wildfire vulnerability. [ABSTRACT FROM AUTHOR]

This paper presents a study based on new fireproof design guidelines for dwellings against the impact of wildfires. The main objective is to present the results from the surveys of the large wildfires of 2017 in Portugal, identifying vulnerabilities in dwellings that may result in spot ignitions when exposed to wildfires. Utilizing the information gathered from these surveys, it is possible to recommend fire resistance and reaction class requirements using European indoor fire standards and adapting them to suit wildfire conditions. The study focuses on classical dwellings predominantly located in high-risk fire zones within the wildland–urban interface. These assessments have the potential to generate new fireproof construction recommendations employing traditional materials commonly found in the European construction industry. [ABSTRACT FROM AUTHOR]

There has been a notable increase in the frequency and severity of extreme events, such as hurricanes, floods, wildfires, and storms. These events, although infrequent, have a significant disruptive effect, causing prolonged outages and compromising essential services, thereby severely affecting customers' safety. As a result, there is an urgent requirement to enhance the resilience of distribution networks by quickly restoring the loads during and after a disaster. In this regard, this paper reviews the existing studies on black-start service restoration in active distribution systems and microgrids. A comprehensive review is conducted for each aspect of the restoration problem, encompassing various proposed methods and modeling techniques found in the existing literature. The aim of this review is to consolidate the knowledge and findings from previous studies, providing a valuable resource for researchers and practitioners in the field. Also, some key research directions for the future work in this field are recommended for developing more practical and reliable methods. [ABSTRACT FROM AUTHOR]

Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection. [ABSTRACT FROM AUTHOR]

In this paper, the patterns of the occurrences of fire incidents over sub-Saharan Africa are studied on the basis of satellite data. Patterns for the whole sub-Saharan Africa are contrasted with those for northern sub-Saharan Africa and southern-hemisphere Africa. This paper attempts to unravel linear trends and overriding oscillations using regression and spectral techniques. It compares fire patterns for aggregated vegetation with those for specific types, which are savannahs, grasslands, shrublands, croplands, and forests, to identify key trend drivers. The underlying cyclic trends are interpreted in light of climate change and model projections. Considering sub-Saharan Africa, northern sub-Saharan Africa, and southern-hemisphere Africa, we found declining linear trends of wildfires with overriding cyclic patterns that have a period of ∼5 years, seemingly largely driven by savannahs, grasslands, and croplands. [ABSTRACT FROM AUTHOR]

Wildfires are widely recognized as a cause of mechanical damage to rocks. Nevertheless, previous research has neglected how wildfires might impact sport climbing areas. In Spain, two large wildfires affected two climbing areas between 2020 and 2021. This paper addresses the rock mechanical effects of wildfires that could lead to safety issues, such as rock falls, climbing hold deterioration, and climbing anchor damage. In this study, the Non-Destructive Techniques (NDTs) of Ultrasonic Pulse Velocity (UPV) and Schmidt Hammer (SH) were used, and two types of measurements were carried out: randomized grid measurements and measurements along the climbing routes. Two phenomena were recognized: (a) thermal breakdown and (b) mineralogical changes. The results of using the SH show a relationship between the decrease in the rebound value and the observed mechanical damage. Field observations showed mechanical weathering, such as cracking, spalling, granular disaggregation, and thermochemical weathering with different temperature thresholds. Observed thermochemical reactions included reddening, CaCO3 calcination, rock decomposition, and quartz cracking. The set of changes involves a major rock outcrop transformation and an acceleration of fire-induced weathering processes. Both areas exhibited more effects at the bottom of the wall. Furthermore, in this paper, we explore how iconic climbing routes can be considered a form of cultural heritage and the consequences of their loss. [ABSTRACT FROM AUTHOR]

Wildfires are recurrent natural events that have been increasing in frequency and severity in recent decades. They threaten human lives and damage ecosystems and infrastructure, leading to high recovery costs. To address the issue of wildfires, several activities must be managed and coordinated in order to develop a suitable response that is both effective and affordable. This includes actions taken before (mitigation, prevention, and preparedness), during (response), and after the event (recovery). Considering the available resources and the safety of the involved personnel is a key aspect. This article is a review focused on fire suppression, which comprises actions belonging to the preparedness phase (deployment) and the response phase (dispatching) of the wildfire management scheme. It goes through the models and methodologies that, applying operations research and optimization techniques, address the management of resources to address fire suppression. This article presents a review of the studies published after the last review on the topic in 2017, but also includes some interesting papers before that date. It concludes with some classifying tables and a few conclusions about possible future lines of research. [ABSTRACT FROM AUTHOR]

Featured Application: Contributing to the development of an innovative performance-based design approach for enhancing the safety and resilience of the built environment in the wildland–urban interface (WUI). This paper presents the results obtained from a field fire test, aiming to reproduce a wildland–urban interface scenario to collect relevant information concerning the impact of wildfires on the built environment. The objective was to understand heat transfer mechanisms from forest fires to structures. During the fire test, the temperatures at the exposed face of one building component were monitored, as well as those in the vicinity of that component, using thermal imaging. The detailed characterization of the field test and building component and obtained experimental results of the fire test were then used to develop and validate a complex computational fluid dynamics model (full physics models) using the Fire Dynamics Simulator (FDS). Several numerical models were previously developed to reproduce the behaviour of individual shrubs and trees in fires considering available results in the literature. The developed Computational Fluid Dynamics (CFD) models can accurately reproduce the field test, including the fire spread and the temperature evolution on the surface of the exposed construction component. The obtained maximum temperature in the construction element was 1038 °C, whereas the maximum average temperature was approximately 638 °C. According to the results from the numerical model, the construction element was exposed to a very high heat flux (above 40 kW/m2), indicating direct contact of the flames with the construction element. The use of CFD enables the quantification of the characteristics of the fire and the exposure of structures to fire in the wildland–urban interface (WUI), allowing for the definition of a performance-based design approach for buildings in the WUI. This contributes to developing safe and resilient structures, as well as mitigating and reducing the impacts of wildfires in the built environment. [ABSTRACT FROM AUTHOR]

An important issue from the forest fire protection system perspective is forecasting fires and maintaining a high readiness of firefighting units at a low cost. In Poland, the level of fire protection is defined by the degree of forest fire risk, based on forecasts made for 1 March to 30 September, i.e., the risk period (the so-called fire season). In this paper, based on classical statistical and spatial analysis, we positively verify the research hypothesis that the fire season in Poland changes in terms of start and end dates depending on the region. We therefore propose a modification to the forecasting period, adapted to local conditions and calculated annually on the basis of the previous period. Then, using network analysis, we negatively verify the hypothesis that the size of the burned area is related to the distance from fire brigades, which proves that the reasons for such a differentiation in the number of fires should be sought elsewhere than in the system used for allocating fire units. On the basis of cluster analysis, attention is drawn to areas where large fires break out more often. [ABSTRACT FROM AUTHOR]

Interactions between clouds, aerosol, and precipitation are crucial aspects of weather and climate. The simple Koren–Feingold conceptual model is important for providing deeper insight into the complex aerosol–cloud–precipitation system. Recently, artificial neural networks (ANNs) and physics-informed neural networks (PINNs) have been used to study multiple dynamic systems. However, the Koren–Feingold model for aerosol–cloud–precipitation interactions has not yet been studied with either ANNs or PINNs. It is challenging for pure data-driven models, such as ANNs, to accurately predict and reconstruct time series in a small data regime. The pure data-driven approach results in the ANN becoming a "black box" that limits physical interpretability. We demonstrate how these challenges can be overcome by combining a simple ANN with physical laws into a PINN model (not purely data-driven, good for the small data regime, and interpretable). This paper is the first to use PINNs to learn about the original and modified Koren–Feingold models in a small data regime, including external forcings such as wildfire-induced aerosols or the diurnal cycle of clouds. By adding external forcing, we investigate the effects of environmental phenomena on the aerosol–cloud–precipitation system. In addition to predicting the system's future, we also use PINN to reconstruct the system's past: a nontrivial task because of time delay. So far, most research has focused on using PINNs to predict the future of dynamic systems. We demonstrate the PINN's ability to reconstruct the past with limited data for a dynamic system with nonlinear delayed differential equations, such as the Koren–Feingold model, which remains underexplored in the literature. The main reason that this is possible is that the model is non-diffusive. We also demonstrate for the first time that PINNs have significant advantages over traditional ANNs in predicting the future and reconstructing the past of the original and modified Koren–Feingold models containing external forcings in the small data regime. We also show that the accuracy of the PINN is not sensitive to the value of the regularization factor ( λ ), a key parameter for the PINN that controls the weight for the physics loss relative to the data loss, for a broad range (from λ = 1 × 10 3 to λ = 1 × 10 5 ). [ABSTRACT FROM AUTHOR]

In this work, a series of laboratory surface fire experiments were performed over a pine needle fuel bed to investigate the effectiveness of a firebreak and the behaviors of a surface fire across a firebreak. Seven wind velocities of 0~3.0 m/s and six firebreak widths of 10~35 cm are varied. The behaviors of a surface fire across the firebreak, the heat flux received by fuel surface and fuel temperature before and after the firebreak are analyzed and compared simultaneously. The main conclusions are as follows: the behaviors of a surface fire spreading across a firebreak under different wind velocities are classified into three categories—no ignition, ignition by flame contact and ignition by spot fires. When the wind velocity is not more than 1.0 m/s, the surface fire cannot successfully cross the firebreak; as wind velocity changes from 1.5 m/s to 2.5 m/s, the fuel after the firebreak can be ignited by flame contact for relatively narrow firebreak conditions; when the wind velocity increases to 3.0 m/s, the burning fuel can be blown away along the fuel bed, and the fuel behind the firebreak will be ignited by spot fire. A linear relationship between the threshold of firebreak width and the fireline intensity is obtained, and the linear fitting coefficient in this paper is larger than the results reported by Wilson (0.36). For no ignition conditions, the fuel temperature and the heat flux received by the fuel after firebreak are significantly lower than those before the firebreak, whereas their variations over time are similar to those before the firebreak for ignition conditions. Moreover, for no ignition conditions, the maximum fuel temperature and the heat flux after the firebreak increase with wind velocity, but decrease with firebreak width. Additionally, when the fuel temperature (253 °C) and the heat flux received by the fuel considering the radiation and convection (43 kW/m2) after firebreak exceed a threshold value, the surface fire can successfully cross the firebreak. [ABSTRACT FROM AUTHOR]

Forests and forestry-related industries and ecosystem services play a critical role in the daily life of all societies, including in cultural, ecological, social, economic, and environmental aspects. Globally, there are about 4.1 billion hectares of forestland. In the United States, there are about 304 million hectares of forestland, covering about 34% of the total land area, and the forest product industry produces over USD 200 billion worth of forestry products annually. Evidence suggests these precious resources may be negatively impacted by climate change via direct and indirect processes, including wildfires, insect/pest pressure, drought, extreme storm events, increased air temperature, solar radiation, vapor pressure deficit, and other factors and variables that can be detrimental. All these can not only cause significant changes in the health and productivity of the forests, but can also cause the extinction, migration, and/or re-distribution of different tree species. Thus, humankind has the paramount responsibility to take policy, technologic, economic, environmental, and management decisions and actions to protect this vital resource for current and future generations, plants, and animals. This paper provides an overview of some of the important characteristics of forest environmental services, climate–environment–forest interactions with respect to forest health and productivity, climate change's impacts on forest species, and the utilization of forest biomass for high-value products. [ABSTRACT FROM AUTHOR]

We are witnessing significant climatic changes and increasingly frequent extreme weather conditions affecting every part of the globe. In order to reduce and stop these unfavourable climate changes, there has been a shift to the use of renewables, and in this sense, a significant contribution of the photovoltaic (PV) power plant is planned. This paper analyses the safety, reliability, and resilience of PV systems to extreme weather conditions such as wind storms, hail, lightning, high temperatures, fire, and floods. In addition to using available information from the literature, temperature measurements were also carried out on the rooftop PV power plant in Slavonski Brod, as well as a numerical stress analysis at extreme wind speeds using Ansys software. The results of the analysis show that existing PV systems are very resilient to extreme weather conditions. Utility-scale PV systems can usually withstand wind speeds of up to 50 m/s without any problems, and only at higher speeds do local stresses occur in certain parts of the structure that are higher than permissible. Resistance to hail is also very high, and manufacturers guarantee resistance to hail up to 25 mm in size. At high air temperatures, the temperature of the panel frame can reach about 70 °C, the panel temperature up to 85 °C, and the temperature of the cable insulation over 60 °C, as measurements have shown. Such high temperatures lead to a drop in electricity production up to 30% but do not pose a fire hazard to the cables and the roof if the roof insulation is conducted correctly. Forest fires do not usually pose a direct threat to PV systems, but the smoke that spreads over a large area reduces the solar radiation reaching the PV panel. It can also cause an unfavourable "wiggle effect". Lightning strikes to a PV panel are not common, although they are possible. With built-in safeguards, no major damage should occur. Flooding is always a possibility, but with properly designed drainage systems, the damage is minimal in most cases. [ABSTRACT FROM AUTHOR]

The destructive Sir Ivan Dougherty fire burned 55,000 hectares around 250 km northwest of Sydney in New South Wales on 12 February 2017. Record hot temperatures were recorded in the area during the lead-in days and the fire conditions at the time were described as the 'worst ever seen in NSW'. The observed weather conditions were hot, dry and very windy ahead of a synoptic frontal wind change during the afternoon. 'Extreme' to 'catastrophic' fire weather was predicted, and the potential for extreme fire behaviour was identified several days in advance. The Australian coupled fire–atmosphere model ACCESS-Fire has been run to explore the characteristics of the Sir Ivan fire. Several features resulting from fire–atmosphere interaction are produced in the simulations. Simulated heat flux along the fire perimeter shows increased intensity on the northern fire flank in response to gradual backing winds ahead of the main frontal wind change. Temporal and spatial variability in fire activity, seen as pulses in fire intensity and fireline wind speed, develop in response to boundary layer rolls in the wind fields. Deep moist convection consistent with the observed pyrocumulonimbus (pyroCb) cloud is simulated over the fire at around the time of the frontal wind change, and matches guidance from the 'PyroCb Firepower Threshold' tool, which showed transient favourable conditions. After the wind change, short-lived near-surface and elevated vortices suggest organised rotating features on the northern flank of the fire. The coupled model captures processes that cannot be produced in uncoupled fire predictions and that are not captured in current operational meteorological forecast products provided to Australian fire agencies. This paper links the features from coupled simulations to available observations and suggests pathways to embed the learnings in operational practice. [ABSTRACT FROM AUTHOR]

Featured Application: We provide a case study for the Wildfire Response, Risk Mitigation and Recovery and a methodology for research-to-commercialization (R2C) for analytics of value using solutions-oriented science. Global climate change and associated environmental extremes present a pressing need to understand and predict social–environmental impacts while identifying opportunities for mitigation and adaptation. In support of informing a more resilient future, emerging data analytics technologies can leverage the growing availability of Earth observations from diverse data sources ranging from satellites to sensors to social media. Yet, there remains a need to transition from research for knowledge gain to sustained operational deployment. In this paper, we present a research-to-commercialization (R2C) model and conduct a case study using it to address the wicked wildfire problem through an industry–university partnership. We systematically evaluated 39 different user stories across eight user personas and identified information gaps in public perception and dynamic risk. We discuss utility and challenges in deploying such a model as well as the relevance of the findings from this use case. We find that research-to-commercialization is non-trivial and that academic–industry partnerships can facilitate this process provided there is a clear delineation of (i) intellectual property rights; (ii) technical deliverables that help overcome cultural differences in working styles and reward systems; and (iii) a method to both satisfy open science and protect proprietary information and strategy. The R2C model presented provides a basis for directing solutions-oriented science in support of value-added analytics that can inform a more resilient future. [ABSTRACT FROM AUTHOR]

Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study. [ABSTRACT FROM AUTHOR]

Although humans have interacted with wildfires for millennia, a science-based approach to fire management has evolved in recent decades. This paper reviews the development of fire-management research, focusing on publications that use this term in their title, abstract, or keywords identified on the Scopus platform. This resulted in the identification of 5624 documents published between 1973 and 2021. Publication rates have particularly increased since 2010. The paper details the characteristics of this body of the literature, including the main authors, institutions, and countries. Furthermore, it considers the bibliographic networks, main research foci, and the publications' study areas. First, these analyses provide researchers interested in fire management an overview of the field and its most prominent sources, authors, and publications. Second, they invite reflection on the current state of fire-management research. In particular, the considerable disparities in spatial foci and countries of authorship suggest that the challenges of today's problems in fire management are more likely to be overcome with a more balanced global research effort. [ABSTRACT FROM AUTHOR]

Wildfires have become a common incident over the past decades, and they have been threatening people's lives and assets. In the communities close to wildlands or wildland–urban interfaces (WUI), these threats become increasingly serious, and in case of wildfires, people are advised or often have to evacuate the area to save their lives. In order to have a safe and effective evacuation, data on people's behavior and decisions during wildfires, evacuation modeling, and traffic simulations are required. This paper reviews past and recent research on evacuation, human behavior in wildfires, evacuation modeling, and traffic simulation. Similar research on evacuation in other situations is also reviewed, and the applicability of the models and simulations on wildfires is discussed. Different stages for an evacuation modeling design are assessed, and the gaps and challenges in obtaining an effective evacuation model are presented. [ABSTRACT FROM AUTHOR]

Open burning is the main factor contributing to the occurrence of wildfires in Thailand, which every year result in forest fires and air pollution. Open burning has become the natural disaster that threatens wildlands and forest resources the most. Traditional firefighting systems, which are based on ground crew inspection, have several limits and dangerous risks. Aerial imagery technologies have become one of the most important tools to prevent wildfires, especially drone real-time monitoring for wildfire surveillance. This paper presents an accuracy assessment of drone real-time open burning imagery detection (Dr-TOBID) to detect smoke and burning as a framework for a deep learning-based object detection method using a combination of the YOLOv5 detector and a lightweight version of the long short-term memory (LSTM) classifier. The Dr-TOBID framework was designed using OpenCV, YOLOv5, TensorFlow, LebelImg, and Pycharm and wirelessly connected via live stream on open broadcaster software (OBS). The datasets were separated by 80% for training and 20% for testing. The resulting assessment considered the conditions of the drone's altitudes, ranges, and red-green-black (RGB) mode in daytime and nighttime. The accuracy, precision, recall, and F1-Score are shown for the evaluation metrics. The quantitative results show that the accuracy of Dr-TOBID successfully detected open burning monitoring, smoke, and burning characteristics, where the average F1-score was 80.6% for smoke detection in the daytime, 82.5% for burning detection in the daytime, 77.9% for smoke detection at nighttime, and 81.9% for burning detection at nighttime. [ABSTRACT FROM AUTHOR]

Anyone who has tried lighting a campfire on a windy day can appreciate how difficult it could be. However, despite real-life experience and despite laboratory experiments which have demonstrated that fire ignition risk dramatically decreases beyond a certain wind threshold, current fire weather indices (FWIs) do not take this effect into account and assume a monotonic relation between wind velocity and ignition risk. In this paper, we perform a global analysis which empirically quantifies the probability of ignition as a function of wind velocity. Using both traditional methods (a logistic regression and a generalized additive model) and machine learning techniques, we find that beyond a threshold of approximately 3–4 m/s, the ignition risk substantially decreases. The effect holds when accounting for additional factors such as temperature and relative humidity. We recommend updating FWIs to account for this issue. [ABSTRACT FROM AUTHOR]

In Canada, Indigenous populations are disproportionately threatened by wildfire smoke and the associated adverse health impacts. This paper presents the results of a narrative review of 51 academic and related resources which explored protective action decision making during wildfire smoke events within Indigenous communities in Canada. A search of scholarly articles and other relevant sources yielded resources which were subject to thematic analysis and described in order to present a narrative review of current knowledge and gaps in research. A small and growing literature provides insights into protective actions taken by the general population during wildfire smoke events, but very little is known about protective actions taken by Indigenous peoples in Canada during wildfire smoke events. This lack of understanding hinders the capacity of decision makers to improve emergency management and minimize community health impacts of wildfire smoke. [ABSTRACT FROM AUTHOR]

The results of long-term lidar studies of the peculiarities of the vertical structure of atmospheric aerosols over Lake Baikal are presented. The paper provides an analysis of data obtained over the period from 2010 to 2022. The studies were carried out under both the background conditions and the extreme natural conditions associated with severe wildfires in Siberia. The parameters of the lidars used in regular summer expeditions to Lake Baikal are briefly described. The data analysis shows that the vertical structure of the aerosol in the lower troposphere up to 2000 m above Baikal in summer is often a stable structure of several aerosol layers tens to hundreds of meters thick. There can be no mixing of layers because the water in the lake is very cold and the aerosol does not rise to higher layers while the air is warming up during the day. The difference is shown between the spatiotemporal structures of aerosol plumes from local wildfires within the lake area and from distant sources. The Angstrom parameter and the aerosol optical depth are calculated for different atmospheric conditions: ηβ = 1.57 ± 0.16 and τ = 0.09 for background conditions; ηβ = 1.41 ± 0.07 and τ = 0.64 for the cases of the observation of smoke aerosol from distant wildfires; and ηβ = 1.05 ± 0.08 and τ = 0.25 for the cases of the observation of smoke aerosol from nearby wildfires. [ABSTRACT FROM AUTHOR]

Accurate predictions of daily wildfire growth rates are crucial, as extreme wildfires have become increasingly frequent in recent years. The factors which determine wildfire growth rates are complex and depend on numerous meteorological factors, topography, and fuel loads. In this paper, we have built upon previous studies that have mapped daily burned areas at the individual fire level around the globe. We applied several Machine Learning (ML) algorithms including XGBoost, Random Forest, and Multilayer Perceptron to predict daily fire growth rate based on meteorological factors, topography, and fuel loads. Our best model on the entire dataset obtained a 1.15 km2 MAE. The ML model obtained a 90% accuracy when predicting whether a fire's growth rate will increase or decrease the following day, compared to 61% using a logistic regression. We discuss the central factors that determine wildfire growth rate. To the best of our knowledge, this study is the first to perform such analyses on a global dataset. [ABSTRACT FROM AUTHOR]

Forest fires have devastating impacts on ecology, the economy, and human life. Therefore, the timely detection and extinguishing of fires are crucial to minimizing the losses caused by these disasters. A novel dual-channel CNN for forest fires is proposed in this paper based on multiple feature enhancement techniques. First, the features' semantic information and richness are enhanced by repeatedly fusing deep and shallow features extracted from the basic network model and integrating the results of multiple types of pooling layers. Second, an attention mechanism, the convolutional block attention module, is used to focus on the key details of the fused features, making the network more efficient. Finally, two improved single-channel networks are merged to obtain a better-performing dual-channel network. In addition, transfer learning is used to address overfitting and reduce time costs. The experimental results show that the accuracy of the proposed model for fire recognition is 98.90%, with a better performance. The findings from this study can be applied to the early detection of forest fires, assisting forest ecosystem managers in developing timely and scientifically informed defense strategies to minimize the damage caused by fires. [ABSTRACT FROM AUTHOR]