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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]

In 2019, the Chinese government put forward the concept of social co-governance in the field of emergency management. This paper discusses whether the adjustment in the governance concept promotes public participation in emergency management practices and what the mechanisms are to achieve successful public participation in a wildfire emergency. Taking the Beibei wildfire in Chongqing in 2022 as a study case, this paper establishes the public participation mechanisms for wildfire rescue and management with a qualitative case study method. The result shows that adjustments to emergency management concepts and the pressure of wildfire governance make local governments and commanders seek help from the public; key persons and informal networks play an important role in public participation in a wildfire emergency; and constructing social memory in social networks, whether government-led or public-organized, is an important way to enhance public participation in wildfire governance. Our findings, grounded in the reality of the growing complexity of wildfires, provide useful lessons for future wildfire governance in other provinces in China, as well as valuable references for other countries or regions. [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]

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

Recently, the United States has experienced, on average, costs of USD 20 billion due to natural and climate disasters, such as hurricanes and wildfires. In this study, we focus on wildfires, which have occurred more frequently in the past few years. This paper examines how various factors, such as the PM10 levels, elevation, precipitation, SOX, population, and temperature, can influence the intensity of wildfires differently across counties in California. More specifically, we use Bayesian analysis to classify all counties of California into two groups: those with more wildfires and those with fewer wildfires. The Bayesian model incorporates prior knowledge and uncertainty for a more robust understanding of how these environmental factors impact wildfires differently among county groups. The findings show a similar effect of the SOX, population, and temperature, while the PM10, elevation, and precipitation have different implications for wildfires across various groups. [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]

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]

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]

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]

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 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]

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]

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]

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]

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 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]

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]

Increasing wildfire activity has led to complex ecosystem consequences, with direct effects on the subsystems that affect the presence and movement of water. Although studies have investigated the cascading effects of wildfires on the water balance, our understanding of broad-scale groundwater modifications post fire remains unclear. This review aims to elucidate fire-induced shifts in the water balance, their causal factors, and their potential effects on groundwater recharge. By scrutinizing prior research examples that modeled post-fire recharge scenarios, the review highlights persistent knowledge gaps. The challenge of quantifying and integrating fire-induced alterations in precipitation, wind, and land temperature patterns into recharge projection models is specifically addressed. Despite these gaps, post-fire values of hydrologically meaningful parameters such as leaf area index (LAI), curve number (CN), and near-surface saturated hydraulic conductivity (KST) have been identified. Simulating post-fire recharge via the extrapolation of these values requires the consideration of site-specific conditions, vegetation recovery, and ash removal. It frequently results in a reduced interception and increased surface runoff, while evapotranspiration remains dependent on site-specific factors and often dictates groundwater recharge estimates. Although post-fire recharge simulations are inherently complex and imprecise, their growing application can guide land-use alterations and support policy implementation that considers fire-induced water availability changes. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of Spain. Furthermore, data collected from a handheld mobile laser scanner system (HMLS) in 43 out of the 73 plots were used to assess the extent of improvement in fuel identification resulting from the fusion of UAV and HMLS data. UAV three-dimensional point clouds (average density: 452 points/m2) allowed the generation of LiDAR metrics and indices related to vegetation structure. Additionally, voxels of 5 cm3 derived from HMLS three-dimensional point clouds (average density: 63,148 points/m2) facilitated the calculation of fuel volume at each Prometheus fuel type height stratum (0.60, 2, and 4 m). Two different models based on three machine learning techniques (Random Forest, Linear Support Vector Machine, and Radial Support Vector Machine) were employed to classify the fuel types: one including only UAV variables and the other incorporating HMLS volume data. The most relevant UAV variables introduced into the classification models, according to Dunn's test, were the 99th and 10th percentile of the vegetation heights, the standard deviation of the heights, the total returns above 4 m, and the LiDAR Height Diversity Index (LHDI). The best classification using only UAV data was achieved with Random Forest (overall accuracy = 81.28%), with confusion mainly found between similar shrub and tree fuel types. The integration of fuel volume from HMLS data yielded a substantial improvement, especially in Random Forest (overall accuracy = 95.05%). The mapping of the UAV model correctly estimated the fuel types in the total area of 55 plots and at least part of the area of 59 plots. These results confirm that UAV-LiDAR systems are valid and operational tools for forest fuel classification and mapping and show how fusion with HMLS data refines the identification of fuel types, contributing to more effective management of forest ecosystems. [ABSTRACT FROM AUTHOR]

In the United States, rangelands comprise 30% of the total land cover and serve as a valuable resource for livestock, wildlife, water, and recreation. Rangelands vary in climate and are often subject to disturbances like drought and wildfire. Historic wildfire trends have indicated an increase in wildfire size and frequency, raising societal and ecological concerns about the management of these lands, both pre- and post-wildfire. While there has been investigation into the effects of grazing prior to a wildfire on fire severity and plant mortality, there is limited research related to grazing post-wildfire even though current management paradigms suggest deferring grazing rangeland for two years after a wildfire to avoid additional stress on native plant species. Based on the diversity found across rangeland ecotypes and history with wildfire, the two-year deferment recommendation may need to be reconsidered for some ecosystems. Species found in perennial bunchgrass rangelands like Pseudoroegneria spicata (bluebunch wheatgrass) and Festuca idahoensis (Idaho fescue) may be less susceptible to post-fire grazing than initially thought, necessitating the need for research into different rangeland ecosystems. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat to ecosystems and communities. This study introduces innovative enhancements to the YOLOv8n object detection algorithm, significantly improving its efficiency and accuracy for real-time forest fire monitoring. By employing Depthwise Separable Convolution and Ghost Convolution, the model's computational complexity is significantly reduced, making it suitable for deployment on resource-constrained edge devices. Additionally, Dynamic UpSampling and Coordinate Attention mechanisms enhance the model's ability to capture multi-scale features and focus on relevant regions, improving detection accuracy for small-scale fires. The Distance-Intersection over Union loss function further optimizes the model's training process, leading to more accurate bounding box predictions. Experimental results on a comprehensive dataset demonstrate that our proposed model achieves a 41% reduction in parameters and a 54% reduction in GFLOPs, while maintaining a high mean Average Precision (mAP) of 99.0% at an Intersection over Union (IoU) threshold of 0.5. The proposed model offers a promising solution for real-time forest fire monitoring, enabling a timely detection of, and response to, wildfires. [ABSTRACT FROM AUTHOR]

We conducted a multi-scaled Ecoregional Conservation Assessment for the Southern Rockies (~14.5 M ha) and its trailing edge, the Santa Fe Subregion (~2.2 M ha), from Wyoming to New Mexico, USA. We included a representation analysis of Existing Vegetation Types (EVTs), mature and old-growth forests (MOG), and four focal species—Canada lynx (Lynx canadensis), North American wolverine (Gulo gulo luscus), Mexican spotted owl (Strix occidentalis lucida), and northern goshawk (Accipiter gentilis)—in relation to 30 × 30 and 50 × 50 conservation targets. To integrate conservation targets with wildfire risk reduction to the built environment and climate change planning, we overlaid the location of wildfires and forest treatments in relation to the Wildland–Urban Interface (WUI) and included downscaled climate projections for a lower (RCP4.5) and higher (RCP8.5) emission scenario. Protected areas were highly skewed toward upper-elevation EVTs (most were >50% protected), underrepresented forest types (<30% protected), especially MOG (<22% protected) and riparian areas (~14% protected), and poorly represented habitats (<30%) for at least three of the focal species, especially in the subregion where nearly all the targets underperformed compared to the ecoregion. Most (>73%) forest-thinning treatments over the past decade were >1 km from delineated WUI areas, well beyond the distance at which vegetation management can effectively reduce structure ignition risk (<50 m from structures). Extreme heat, drought, snowpack reductions, altered timing of peak stream flows, increasing wildfires, and potential shifts in the climate, favoring woodlands over conifer forests, may impact forest-dependent species, while declining snowpack may impact wolverines that den at upper elevations. Strategically targeting the built environment for fuel treatments would improve wildfire risk reduction and may allow for expansion of protected areas held up in controversy. Stepped-up protection for roadless areas, adoption of wilderness proposals, and greater protection for MOG and riparian forests are critical for meeting representation targets. [ABSTRACT FROM AUTHOR]

Over the last few years, new land use planning instruments to reduce the negative consequences of recent land use/cover changes (farmland abandonment, wildfires) have been proposed in Galicia (northwest Spain). Understanding the complex relationship between biophysical constraints, socioeconomic drivers and land use/cover changes is paramount for their successful implementation. In this work, we present an analysis of recent (2005–2017) land use/cover changes in the region, along with a classification of municipalities in homogeneous groups with different patterns of land use and land use change. We then characterize those groups regarding the demographic and employment structure, the economic performance, the characteristics of the primary sector, the land ownership structure and the relative importance of recent wildfire events and the biophysical suitability for the main productions of the primary sector in the region. The results allowed us to identify four different groups of municipalities which are clearly separated by specific patterns of land use (an area where most of the population lives, an area devoted to forest production, another for farming production and a final one dominated by semi-natural covers). These four areas followed a gradient of decreasing levels of population density and economic activity. While land use patterns in different areas could be explained largely by biophysical suitability, the fragmentation of land ownership emerged as a relevant factor, which can explain the greater presence of farmland abandonment—and, therefore, higher wildfire risk—in certain areas. These results offer relevant guidelines for the successful implementation of the new land use planning instruments in the region. [ABSTRACT FROM AUTHOR]

The world has seen an increase in the frequency and severity of elevated outdoor pollution events exacerbated by the rise in distant polluting events (i.e., wildfires). We examined the intersection between indoor and outdoor air quality in an urban area using research-grade sensors to explore PM2.5 infiltration across a variety of pollution events by testing two separate indoor environments within the same building. We confirmed prior work suggesting that indoor environments in buildings are most protective during wintertime inversion events and less so during fireworks and wildfire events. The building indoor environment protectiveness varies notably during different pollution episodes, especially those that have traveled longer distances (e.g., wildfires), and we found evidence of varied infiltration rates across PM2.5 types. Inversion events have the lowest infiltration rates (13–22%), followed by fireworks (53–58%), and wildfires have the highest infiltration rates (62–70%), with distant wildfire events persisting longer and, therefore, infiltrating for greater durations than local-wildfire-related particle matter. The differences in PM infiltration rates were likely due to the combined effects of several factors, including varying particle size, concentration, and chemistry. Subsequently, the local wildfires had different temporal air quality impacts than distant wildfire pollution in this case. Based on these findings, indoor air quality appears more conducive to protective action and policies than outdoor air quality because the built environment may serve to shield individuals from outdoor air. [ABSTRACT FROM AUTHOR]

Boreal and Arctic regions have warmed up to four times quicker than the rest of the planet since the 1970s. As a result, boreal and tundra ecosystems are experiencing more frequent and higher intensity extreme weather events and disturbances, such as wildfires. Yet limitations in ground and satellite data across the Arctic and boreal regions have challenged efforts to track these disturbances at regional scales. In order to effectively monitor the progression and extent of wildfires in the Arctic-boreal zone, it is essential to determine whether burned area (BA) products are accurate representations of BA. Here, we use 12 different datasets together with MODIS active fire data to determine the total yearly BA and seasonal patterns of fires in Arctic-boreal North America and Russia for the years 2001–2020. We found relatively little variability between the datasets in North America, both in terms of total BA and seasonality, with an average BA of 2.55 ± 1.24 (standard deviation) Mha/year for our analysis period, the majority (ca. 41%) of which occurs in July. In contrast, in Russia, there are large disparities between the products—GFED5 produces over four times more BA than GFED4s in southern Siberia. These disparities occur due to the different methodologies used; dNBR (differenced Normalized Burn Ratio) of short-term composites from Landsat images used alongside hotspot data was the most consistently successful in representing BA. We stress caution using GABAM in these regions, especially for the years 2001–2013, as Landsat-7 ETM+ scan lines are mistaken as burnt patches, increasing errors of commission. On the other hand, we highlight using regional products where possible, such as ABoVE-FED or ABBA in North America, and the Talucci et al. fire perimeter product in Russia, due to their detection of smaller fires which are often missed by global products. [ABSTRACT FROM AUTHOR]

Detecting wildfires presents significant challenges due to the presence of various potential targets in fire imagery, such as smoke, vehicles, and people. To address these challenges, we propose a novel multi-label classification model based on BiFormer's feature extraction method, which constructs sparse region-indexing relations and performs feature extraction only in key regions, thereby facilitating more effective capture of flame characteristics. Additionally, we introduce a feature screening method based on a domain-adversarial neural network (DANN) to minimize misclassification by accurately determining feature domains. Furthermore, a feature discrimination method utilizing a Graph Convolutional Network (GCN) is proposed, enabling the model to capture label correlations more effectively and improve performance by constructing a label correlation matrix. This model enhances cross-domain generalization capability and improves recognition performance in fire scenarios. In the experimental phase, we developed a comprehensive dataset by integrating multiple fire-related public datasets, and conducted detailed comparison and ablation experiments. Results from the tenfold cross-validation demonstrate that the proposed model significantly improves recognition of multi-labeled images in fire scenarios. Compared with the baseline model, the mAP increased by 4.426%, CP by 4.14% and CF1 by 7.04%. [ABSTRACT FROM AUTHOR]

Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases. [ABSTRACT FROM AUTHOR]

Climate change and wildfire effects have continued to receive great attention in recent times due to the impact they render on the environment and most especially to the field of agriculture. The purpose of this study was to assess the synergistic impacts of climate change and wildfires on agricultural sustainability. This study adopted a cross-sectional survey design based on the quantitative research approach. Data were collected from 340 environmental experts using an online questionnaire. The results showed that extreme weather events such as heavy rains or extreme droughts negatively influence agricultural sustainability in Europe. The results showed that disruptions in ecosystems caused by climate change have a significant positive impact on agricultural sustainability in Europe. Furthermore, forest regeneration after wildfires showed statistically significant positive influence on agricultural sustainability in Europe. The economic impact of fire on crops, cattle, and farms can be estimated. This information can be used to develop and plan agricultural regions near fire-prone areas; choose the best, most cost-effective, and longest-lasting cultivar; and limit fire risk. It is also clear that increased wildfire smoke negatively affects agricultural sustainability. [ABSTRACT FROM AUTHOR]

Climate change intensifies the frequency and severity of extreme weather events, profoundly altering demographic landscapes globally and within the United States. This study investigates their impact on migration patterns, using propensity score matching and LASSO techniques within a larger regression modeling framework. Here, we analyze historical population trends in relation to climate risk and exposure metrics for various hazards. Our findings reveal nuanced patterns of climate-induced population change, including "risky growth" areas where economic opportunities mitigate climate risks, sustaining growth in the face of observed exposure; "tipping point" areas where the amenities are slowly giving way to the disamenity of escalating hazards; and "Climate abandonment" areas experiencing exacerbated out-migration from climate risks, compounded by other out-migration market factors. Even within a single county, these patterns vary significantly, underscoring the importance of localized analyses. Projecting population impacts due to climate risk to 2055, flood risks are projected to impact the largest percentage of areas (82.6%), followed by heatwaves (47.4%), drought (46.6%), wildfires (32.7%), wildfire smoke (21.7%), and tropical cyclone winds (11.1%). The results underscore the importance of understanding hyperlocal patterns of risk and change in order to better forecast future patterns. [ABSTRACT FROM AUTHOR]

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL's UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire "burn" detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as "burned" compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance. [ABSTRACT FROM AUTHOR]

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance. [ABSTRACT FROM AUTHOR]

Flood variability associated with urbanization, ecological change, and climatic change is of increasing economic and social concern in and around Flagstaff, Arizona, where flood hydrology is influenced by a biannual precipitation regime and the relatively unique geologic setting at the edge of the San Francisco Volcanic Field on the southern edge of the Colorado Plateau. There has been limited long-term gauging of the ephemeral channels draining the developed lands and dry coniferous forests of the region, resulting in a spaciotemporal gap in observation-based assessments of large-scale flooding patterns. We present new data from over 10 years of flood monitoring using a crest stage gauge network, combined with other channel monitoring records from multiple agency sources, to assess inter-decadal patterns of flood change in the area, with a specific emphasis on examining how various controls and disturbances have altered the character and seasonality of peak annual flows. Methods of analysis included the following: using Fisher's Exact Test to compare the seasonality of flooding between historic data spanning the 1970s and contemporary data obtained since 2010; summarizing GIS-based spatial data and meteorological timeseries to characterize study catchment conditions and changes between flood study periods; and relating spatiotemporal patterns of flood seasonality and occurrences of notably large floods with catchment characteristics and environmental changes. Our results show systematic patterns and changes in Flagstaff-area flood regimes that relate to geologic and topographic controls of the varied catchment systems, and in response to records of climate variations and local catchment disturbances, including urbanization and, especially, high-severity wildfire. For most catchments there has been a shift from predominantly late winter to spring snowmelt floods, or mixed seasonal flood regimes, towards monsoon-dominated flooding, patterns which may relate to observed local warming and precipitation changes. Post-wildfire flooding has produced extreme flood discharges which have likely exceeded historical estimates of flood magnitude over decade-long monitoring periods by one to two orders of magnitude. We advocate for continued monitoring and the expansion of local stream gauge networks to enable seasonal, magnitude-frequency trend analyses, improved climate and environmental change attribution, and to better inform the many planned and ongoing flood mitigation projects being undertaken in the increasingly developed Flagstaff region. [ABSTRACT FROM AUTHOR]

Prescriptions for fuel management are universally applied across the forest types in British Columbia, Canada, to reduce the fire behaviour potential in the wildland–urban interface. Fuel thinning treatments have been advocated as a means of minimizing the likelihood of crown fire development in conifer forests. We hypothesized that these types of prescriptions are inappropriate for the coastal rainforests of the Whistler region of the province. Our study examined the impact of fuel thinning treatments in four stands located in the Whistler community forest. We measured several in-stand microclimatic variables beginning with snow melt in the spring up to the height of fire danger in late summer, at paired thinned and unthinned stand locations. We found that the thinning led to warmer, drier, and windier fire environments. The difference in mean soil moisture, ambient air temperature, and relative humidity between thinned and unthinned stands was significant in the spring with approximate p-values of 0.000217, 9.40 × 10−5, and 4.33 × 10−8, respectively, though there were no discernible differences in the late summer. The difference in mean solar radiation, average wind speed, and average cross wind between thinned and unthinned locations are significant in the spring and late summer (with approximate p-values for spring of 9.54 × 10−7, 0.02101, 1.92 × 10−9, and for late summer of 2.45 × 10−7, 4.08 × 10−6, and 2.45 × 10−5, respectively). [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the pyrolysis of selected California foliage and estimate the energy content of the released volatiles to show the significance of the pyrolysis of foliage and its role during wildland fires. While the majority of the volatiles released during the pyrolysis of foliage later combust and promote fire propagation, studies on the energy released from combustion of these compounds are scarce. Samples of chamise (Adenostoma fasciculatum), Eastwood's manzanita (Arctostaphylos glandulosa), scrub oak (Quercus berberidifolia), hoaryleaf ceanothus (Ceanothus crassifolius), all native to southern California, and sparkleberry (Vaccinium arboreum), native to the southern U.S., were pyrolyzed at 725 °C with a heating rate of approximately 180 °C/s to mimic the conditions of wildland fires. Tar and light gases were collected and analyzed. Tar from chamise, scrub oak, ceanothus and sparkleberry was abundant in aromatics, especially phenol, while tar from manzanita was mainly composed of cycloalkenes. The four major components of light gases were CO, CO2, CH4 and H2. Estimated values for the high heating values (HHVs) of volatiles ranged between 18.9 and 23.2 (MJ/kg of biomass) with tar contributing to over 80% of the HHVs of the volatiles. Therefore, fire studies should consider the heat released from volatiles present in both tar and light gases during pyrolysis. [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]

In recent years, the frequency of wildfires worldwide has been gradually increasing, posing significant threats to global ecosystems and human society. Given that plants serve as the primary fuel in natural environments, accurately assessing the flammability of plants is crucial for wildfire management and fire ecology studies. Plant flammability is a multifaceted trait influenced by various physiological, physical, and chemical characteristics of plants. Currently, there is no universally accepted standard for quantifying plant flammability. By analyzing published research over the past few decades, this study found that 17.27% of studies assessed plant flammability by measuring flammability-related characteristics, such as moisture content, leaf size, bark thickness, oil content, and terpene content; a total of 34.55% of studies assessed plant flammability through burning experiments by measuring burning parameters, such as ignition time, duration of combustion, and flame spread rate. The remaining studies, approximately 50%, used a combination of burning experiments and flammability-related characteristic measurement to assess plant flammability. This study outlined the current status of plant flammability measurements, discussed the merits of each measurement method, and proposed suggestions for enhancing the assessment of plant flammability, with the aim of contributing to the standardization of plant flammability measurements. [ABSTRACT FROM AUTHOR]

The objective of this research was to analyze the P. brutia natural regeneration and the environments where it was established in the absence of a wildfire in the open formations of the species in the middle elevations of the central part of Cyprus. Forty-eight rectangular plots of 0.1 ha were established in three site productivity types. Individuals of the species with a height from 0.1 m to 1.5 m were considered regeneration plants. Within each plot, various measurements and estimations were made. Moreover, 480 hemispherical photographs were taken. Additionally, hemispherical photographs were taken above each of the recorded naturally regenerated P. brutia plants. In all the site productivity types, the most regeneration plants were established and grew under the process of facilitation. In each of the site productivity types, there were no differences in the light condition diversity between the environments where the regeneration plants grew and the existing light conditions. Regeneration exhibited a small number of plants and constituted only a very small proportion of all trees in the three site productivity types. To achieve sustainability in P. brutia forests, forest practices must develop strategies for the enhancement of P. brutia regeneration in the studied area and in analogous environments. [ABSTRACT FROM AUTHOR]

In this article, we describe multiannual variations of the atmospheric aerosol optical depth values in the widely understood area of the Baltic Sea. We present the trends in the changes and depict unusual aerosol situations. As a result of analyses, we focus on 2019, since unusually high values of aerosol optical depth were recorded in several stations around the Baltic. We match the high aerosol levels with an unusually high number of wildfires across Europe in spring and summer, which emitted large quantities of aerosols into the atmosphere that were distributed over vast European areas in part by the wind. We then connect this case with the climate change consequences. Since aerosols influence the radiative budget of the planet by directly affecting the atmospheric radiation budget, it is obvious that human well-being is in danger due to wildfires, as well as from the atmospheric perspective. Climate change will lead to more frequent fires; thus, humans need to develop solutions to decrease the risk of fire outbreaks. [ABSTRACT FROM AUTHOR]