Neotropical mammal responses to megafires in the Brazilian Pantanal.

The increasing frequency and severity of human-caused fires likely have deleterious effects on species distribution and persistence. In 2020, megafires in the Brazilian Pantanal burned 43% of the biome's unburned area and resulted in mass mortality of wildlife. We investigated changes in habitat use or occupancy for an assemblage of eight mammal species in Serra do Amolar, Brazil, following the 2020 fires using a pre- and post-fire camera trap dataset. Additionally, we estimated the density for two naturally marked species, jaguars Panthera onca and ocelots Leopardus pardalis. Of the eight species, six (ocelots, collared peccaries Dicotyles tajacu, giant armadillos Priodontes maximus, Azara's agouti Dasyprocta azarae, red brocket deer Mazama americana, and tapirs Tapirus terrestris) had declining occupancy following fires, and one had stable habitat use (pumas Puma concolor). Giant armadillo experienced the most precipitous decline in occupancy from 0.431 ± 0.171 to 0.077 ± 0.044 after the fires. Jaguars were the only species with increasing habitat use, from 0.393 ± 0.127 to 0.753 ± 0.085. Jaguar density remained stable across years (2.8 ± 1.3, 3.7 ± 1.3, 2.6 ± 0.85/100 km2), while ocelot density increased from 13.9 ± 3.2 to 16.1 ± 5.2/100 km2. However, the low number of both jaguars and ocelots recaptured after the fire period suggests that immigration may have sustained the population. Our results indicate that the megafires will have significant consequences for species occupancy and fitness in fire-affected areas. The scale of megafires may inhibit successful recolonization, thus wider studies are needed to investigate population trends.

A crescente frequência e gravidade dos incêndios causados pelo homem provavelmente terão efeitos deletérios na distribuição e persistência das espécies. Em 2020, mega incêndios no Pantanal brasileiro queimaram 43% do bioma e resultaram na mortalidade em massa da vida selvagem. Nós investigamos mudanças no uso ou ocupação do habitat para uma comunidade de oito espécies de mamíferos na Serra do Amolar, Brasil, após os incêndios de 2020, usando um conjunto de dados de armadilhas fotográficas instaladas no período pré e pós­fogo. Além disso, estimamos a densidade de duas espécies naturalmente marcadas, a onça­pintada Panthera onca e a jaguatirica Leopardus pardalis. Das oito espécies, seis (a jaguatirica, o cateto Dicotyles tajacu, o tatu­canastra Priodontes maximus, a cutia Dasyprocta azarae, o veado mateiro Mazama americana e a anta Tapirus terrestris) tiveram ocupação reduzida após os incêndios, e uma teve uso de habitat estável (a onça­parda, Puma concolor). O tatu­canastra apresentou o declínio mais acentuado na ocupação após os incêndios de 0,431 ± 0,171 para 0,077 ± 0,044. A onça­pintada foi a única espécie com uso crescente de habitat, de 0,393 ± 0,127 para 0,753 ± 0,085. A densidade da onça­pintada permaneceu estável ao longo dos anos (2,8 ± 1,3, 3,7 ± 1,3, 2,6 ± 0,85/100 km2), enquanto a densidade da jaguatirica aumentou de 13,9 ± 3,2 para 16,1 ± 5,2/100 km2. No entanto, o baixo número de onças­pintadas e jaguatiricas recapturadas após o período do fogo sugere que a imigração pode ter sustentado as populações. Nossos resultados indicam que os mega incêndios terão consequências significativas para a ocupação e resiliência das espécies nas áreas afetadas pelo fogo. A escala dos mega incêndios pode inibir uma recolonização bem­sucedida, pelo que são necessários estudos mais amplos para investigar as tendências populacionais.

A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm.

Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.

Forest waste composting-operational management, environmental impacts, and application.

In Portugal, the number of fires and the size of burnt areas are rising dramatically every year, increasing with improper management of agroforestry wastes (AFRs). This work aims to study the composting of these wastes with minimal operational costs and understand the environmental impact and the compost application on burnt soil. Thus, a study of life cycle assessment (LCA) was carried out based on windrow composting processes, considering the avoided environmental impacts associated with the end-product quality and its application as an organic amendment. Three composting piles were made with AFRs from the Residual Biomass Collection Centre (RBCC) in Bodiosa (Portugal). Sewage sludges (SS) from an urban wastewater treatment plant were used as conditioning agent. One pile with AFRs (MC) and another with AFRs and SS (MCS) were managed according to good composting practices. Another pile with the AFRs was developed without management (NMC), thus with a minimal operational cost. Periodically, it was measured several physical and chemical parameters according to standard methodologies. Eleven environmental impacts of compost production, MC and MCS, were analyzed by a LCA tool, and their effect on the growth of Pinus pinea was evaluated, using peat as reference. Composting evolution was expected for both piles. Final composts, MC and MCS, were similar, complying with organic amendment quality parameters. Compost NMC, with no operational management, showed the highest germination index. Piles MC and MCS showed similar environmental impacts, contributing to a negative impact on global warming, acidification, and eutrophication. Greater growth was obtained with application of MCS, followed by MC, and finally, peat. Composting is a sustainable way to valorize AFRs wastes, producing compost that could restore burnt soils and promote plant growth and circular economy.

A European-scale analysis reveals the complex roles of anthropogenic and climatic factors in driving the initiation of large wildfires.

Analysing wildfire initiation patterns and identifying their primary drivers is essential for the development of more efficient fire prevention strategies. However, such analyses have traditionally been conducted at local or national scales, hindering cross-border comparisons and the formulation of broad-scale policy initiatives. In this study, we present an analysis of the spatial variability of wildfire initiations across Europe, focusing specifically on moderate to large fires (> 100 ha), and examining the influence of both human and climatic factors on initiation areas. We estimated drivers of fire initiation using machine learning algorithms, specifically Random Forest (RF), covering the majority of the European territory (referred to as the "ET scale"). The models were trained using data on fire initiations extracted from a satellite burned area product, comprising fires occurring from 2001 to 2019. We developed six RF models: three considering all fires larger than 100 ha, and three focused solely on the largest events (> 1000 ha). Models were developed using climatic and human predictors separately, as well as both types of predictors mixed together. We found that both climatic and mixed models demonstrated moderate predictive capacity, with AUC values ranging from 79 % to 81 %; while models based only on human variables have had poor predictive capacity (AUC of 60 %). Feature importance analysis, using Shapley Additive Explanations (SHAP), allowed us to assess the primary drivers of wildfire initiations across the European Territory. Aridity and evapotranspiration had the strongest effect on fire initiation. Among human variables, population density and aging had considerable effects on fire initiation, the former with a strong effect in mixed models estimating large fires, while the latter had a more important role in the prediction of very large fires. Distance to roads and forest-agriculture interfaces were also relevant in some initiation models. A better understanding of drivers of main fire events should help designing European forest fire management strategies, particularly in the light of growing importance of climate change, as it would affect both fire severity and areas at risk. Factors of fire initiation should also be part of a comprehensive approach for fire risk assessment, reduction and adaption, contributing to more effective wildfire management and mitigation across the continent.

Detection of forest fires and pollutant plume dispersion using IoT air quality sensors.

The widespread adoption of Internet of Things (IoT) sensors has revolutionized our understanding of the formation and mitigation of air pollution, significantly improving the accuracy of predictions related to air quality and emission sources. This study demonstrates the use of IoT air quality sensors to detect forest fire incidents by focusing on an area affected by forest fires in Tak Province, Thailand, from January to May 2021. We employed PM2.5 and carbon monoxide measurements from IoT sensors for forest fire detection and utilized the number of hotspots reported through satellite and human observations to identify forest fire incidents. Our data analysis revealed three distinct periods with forest fires and three periods without fires (non-forest fires). For model training, two forest fire and non-forest fire periods were selected and the remaining periods were set aside for validation. J48, a computer algorithm that helps make decisions by organizing information into a tree-like structure based on key characteristics, was used to construct the decision-tree model. Our model achieved an accuracy rate of 72% when classifying forest fire incidents using the training data and a solid accuracy of 69% on the validation data. In addition, we investigated the dispersion of PM2.5 plumes using a regression model. Notably, our findings highlighted the robust explanatory power of the lag time in PM2.5, for predicting PM2.5, in the next 15 min. Our analysis highlights the potential of IoT-based air quality sensors to enhance forest fire detection and predict pollution plume dispersion once fires are detected.

Greenhouse gas emission in the whole process of forest fire including rescue: a case of forest fire in Beibei District of Chongqing.

In the context of global high temperature, the harm of greenhouse gases (GHG) emissions caused by frequent forest fires to the environment cannot be ignored. Existing research only calculates the GHG generated by the burning of forest vegetation, ignoring the GHG generated by the fire-driven social rescue activities. Taking the forest fire in Beibei District, Chongqing City, China, as an example, this paper studies and establishes the GHG emission accounting method for the whole process of forest fire from ignition to fire extinguishing through three processes: vegetation burning, rescue transportation, and on-site fire extinguishing. It covers three GHG calculation types: biomass burning, traffic activity level comprehensive energy consumption, and machine energy consumption. Among them, the CO2 produced by the burning of coniferous forest, the support transportation of rescue teams in Yunnan province, and the motorcycle transportation at the fire extinguishing site accounted for a relatively high proportion in the corresponding processes, reaching 12,761.445 t, 118.750 t, and 1056.980 t, respectively. Finally, through data analysis, suggestions on GHG emission reduction related to forest tree regulation and optimization of rescue and fire extinguishing management are put forward, which provides a direction for future research on carbon reduction in the whole process of forest fire events.

The adoption of climate-smart agriculture to address wildfires in the Maya Golden Landscape of Belize: Smallholder farmers' perceptions.

Ecosystems around the globe are enduring wildfires with greater frequency, intensity, and severity and this trend is projected to continue as a result of climate change. Climate-smart agriculture (CSA) has been proposed as a strategy to prevent wildfires and mitigate climate change impacts; however, it remains poorly understood as a strategy to prevent wildfires. Therefore, the authors propose a multimethod approach that combines mapping of wildfire susceptibility and social surveys to identify priority areas, main factors influencing the adoption of CSA practices, barriers to their implementation, and the best CSA practices that can be implemented to mitigate wildfires in Belize's Maya Golden Landscape (MGL). Farmers ranked slash and mulch, crop diversification, and agroforestry as the main CSA practices that can be implemented to address wildfires caused by agriculture in the MGL. In order to reduce wildfire risk, these practices should, be implemented in agricultural areas near wildlands with high wildfire susceptibility and during the fire season (February-May), in the case of slash and mulch. However, socio-demographic and economic characteristics, together with a lack of training and extension services support, inadequate consultation by agencies, and limited financial resources, hinder the broader adoption of CSA practices in the MGL. Our research produced actionable and valuable information that can be used to design policies and programs to mitigate the impacts of climate change and wildfire risk in the MGL. This approach can also be used in other regions where wildfires are caused by agricultural practices to identify priority areas, barriers and suitable CSA practices that can be implemented to mitigate wildfires.

Continent-based systematic review of the short-term health impacts of wildfire emissions.

This review systematically gathers and provides an analysis of pollutants levels emitted from wildfire (WF) and their impact on short-term health effects of affected populations. The available literature was searched according to Population, Exposure, Comparator, Outcome, and Study design (PECOS) database defined by the World Health Organization (WHO) and a meta-analysis was conducted whenever possible. Data obtained through PECOS characterized information from the USA, Europe, Australia, and some Asian countries; South American countries were seldom characterized, and no data were available for Africa and Russia. Extremely high levels of pollutants, mostly of fine fraction of particulate matter (PM) and ozone, were associated with intense WF emissions in North America, Oceania, and Asia and reported to exceed several-fold the WHO guidelines. Adverse health outcomes include emergency department visits and hospital admissions for cardiorespiratory diseases as well as mortality. Despite the heterogeneity among exposure and health assessment methods, all-cause mortality, and specific-cause mortality were significantly associated with WF emissions in most of the reports. Globally, a significant association was found for all-cause respiratory outcomes including asthma, but mixed results were noted for cardiovascular-related effects. For the latter, estimates were only significant several days after WF emissions, suggesting a more delayed impact on the heart. Different research gaps are presented, including the need for the application of standardized protocols for assessment of both exposure and adverse health risks. Mitigation actions also need to be strengthened, including dedicated efforts to communicate with the affected populations, to engage them for adoption of protective behaviors and measures.

Chir pine forest and pre-monsoon drought determine spatial, and temporal patterns of forest fires in Uttarakhand Himalaya.

Associated with farming practices (between 300 and 2000 m elevations), human-ignited small, and patchy surface forest fires occur almost every year in Uttarakhand (between 28°43`- 31°27` N and 77°34`- 81°02`E; area 51,125 km2), a Himalayan state of India. Using fire incidence data of 19 years (2002-2020) generated by MODIS, we analysed the factors which drive temporal and spatial patterns of fire in the region. The fire incidence data were organized by 24 forest divisions, the unit of state forest management and administration. The standardized regression model showed that pre-monsoon temperature (March to May or mid-June), proportional area of the forest division under chir pine (Pinus roxburghii) forest (positive effect), and pre-monsoon and winter precipitation (negative effect) accounted for 56% of the variance in fire incidence density (FID). The pre-monsoon temperature (warmer) and precipitation (lower) were significantly different in 2009, 2012, 2016 and 2019, the years with high FID (average 54.9 fire/100 km2) than the rest of years with low FID (average 20.9 fire/100 km2). During the two decades of warming, high FID (> 30 incidence per year /100 km2) occurred after every three to four years, and fire peaks tended to increase with time. The study suggests that effective fire management can be attained by improving pre-monsoon precipitation forecasting and targeting forest compartments with a higher occurrence of chir pine and fire-vulnerable oaks. Furthermore, since fires are human-ignited, periodical analysis of changes in population distribution and communities' dependence on forests would need to be conducted. Supplementary Information: The online version contains supplementary material available at 10.1007/s42965-023-00306-9.

Anthropogenic climate change impacts exacerbate summer forest fires in California.

Record-breaking summer forest fires have become a regular occurrence in California. Observations indicate a fivefold increase in summer burned area (BA) in forests in northern and central California during 1996 to 2021 relative to 1971 to 1995. While the higher temperature and increased dryness have been suggested to be the leading causes of increased BA, the extent to which BA changes are due to natural variability or anthropogenic climate change remains unresolved. Here, we develop a climate-driven model of summer BA evolution in California and combine it with natural-only and historical climate simulations to assess the importance of anthropogenic climate change on increased BA. Our results indicate that nearly all the observed increase in BA is due to anthropogenic climate change as historical model simulations accounting for anthropogenic forcing yield 172% (range 84 to 310%) more area burned than simulations with natural forcing only. We detect the signal of combined historical forcing on the observed BA emerging in 2001 with no detectable influence of the natural forcing alone. In addition, even when considering fuel limitations from fire-fuel feedbacks, a 3 to 52% increase in BA relative to the last decades is expected in the next decades (2031 to 2050), highlighting the need for proactive adaptations.

Alterações climáticas e sua repercussão sobre a saúde humana em países da América do Sul / Climate change and its impact on human health in South America

Nas últimas duas décadas as mudanças climáticas têm se intensificado, causado danos ao meio ambiente e aos indivíduos que nele habitam. Várias ações do ser humano têm contribuído para que cada vez mais essas mudanças climáticas sejam mais presentes e intensas. O aumento das desigualdades e vulnerabilidades sociais, o desmatamento, os incêndios florestais voluntários, a degradação do solo e a poluição ambiental aliados à variabilidade climática global da temperatura da água do mar podem potencialmente levar a eventos climáticos extremos, potencializando os efeitos negativos sobre a saúde. Neste trabalho é apresentado um resumo do relatório do Lancet Countdown South America, fruto da colaboração acadêmica multidisciplinar de instituições de ensino e agências sul-americanas de saúde de 12 países (Argentina, Bolívia, Brasil, Colômbia, Chile, Equador, Guiana, Paraguai, Peru, Uruguai, Venezuela e Suriname) publicado por Hartinger e cols. (2023). Este estudo é uma alerta, pois nele são publicados os resultados do levantamento sobre mudanças climáticas e seus efeitos sobre a saúde humana no continente sul-americano. Conhecê-las é o primeiro passo para que políticas de saúde pública sejam instituídas, e, preferencialmente, de modo preventivo.

Climate change has intensified in the last two decades, damaging the environment and those who inhabit it. Human activity has increased the prevalence and intensity of these changes. Increased social inequality and vulnerability, deforestation, intentional forest fires, soil degradation, and environmental pollution, when associated with sea temperature variability, can lead to extreme weather events, increasing negative health effects. This report summarizes Lancet Countdown South America (Hartinger et al. 2023), the result of multidisciplinary collaboration between education institutions and South American health agencies from 12 countries: Argentina, Bolivia, Brazil, Colombia, Chile, Ecuador, Guyana, Paraguay, Peru, Uruguay, Venezuela and Suriname. This should be considered a wake-up call because it contains the results of a climate change survey and its effects on human health in South America. Knowing these effects is the first step toward appropriate, preferably preventive, public health policies.

Effects of forest residue mulching on organic matter and nutrient exports after wildfire in North-Central Portugal.

The quick implementation of emergency stabilization measures is vital for minimizing post-fire soil erosion and the associated fertility loss. Mulching has proven to be highly effective in minimizing post-fire soil erosion, however few studies have investigated its impacts on organic matter (OM) and nutrient mobilization from burned forest areas. This study evaluates the effectiveness of forest residue mulching at reducing OM, N and P losses within the sediments after a moderate-severity wildfire over a period of 5 years (Ermida, North-central Portugal). Untreated and mulched plots of ca. 100 m2 were bounded with geotextile fabric and sediments were collected from silt fences after a total of 29 periods. During the first five years after the fire, the accumulated OM, N and P exportations in the untreated plots were, respectively, 199, 5.2 and 0.38 g m-2; and mulch significantly reduced these figures in, respectively, 91 %, 94 % and 95 % (p < 0.05). The overall OM content in the sediments of the untreated plots (45 %) was not different from the OM content of the mulched plots (34 %, p = 0.16). However, the N (8.9 g kg-1) and Pav contents (0.62 g kg-1) in the untreated plots were significantly higher than the N (5.6 g kg-1; p < 0.05) and Pav contents (0.36 g kg-1; p < 0.05) in the mulched plots. This effect was especially noticeable in the first year after fire. OM and TN contents in the sediments were highly variable throughout the study period, whereas Pav contents declined sharply in the first post-fire rainfall events, maintaining low values afterwards. The main factors driving nutrient exports were ash and litter cover, whereas no significant relationship was observed for OM exports. The present work has shown that forest residues application can be a sustainable strategy for the conservation of soil carbon and nutrients in fire-affected areas.

Pyrogenic carbon stocks and its spatial variability in soils from savanna-forest ecotone in amazon.

Forest fragments from Amazon are important long-term carbon (C) reservoirs with an essential role in the global C balance. They are often impacted by understory fires, deforestation, selective logging and livestock. Forest fires convert soil organic matter into pyrogenic carbon (PyC), but little is known about its distribution and accumulation along the soil profile. Thus, the objective of this study is to estimate the refractory carbon stocks derived from PyC accumulated in the soil vertical profile of different seasonal forest fragments in the Amazon. Sixty-nine soil cores (1 m deep) were collected in 12 forest fragments of different sizes considering edge and interior gradients. The mean total organic C (TOC) and PyC contents for the edge and interior gradients were 0.84% and 0.009%, respectively. The PyC/TOC ratio ranged from 0.53% to 1.78%, with an average of 1.32% and increasing in depth, being low when compared with other studies, where the contribution of PyC to TOC ranges from 1 to 9%. PyC stocks on the edge (1.04 ± 0.04 Mg ha-1) differed significantly from the interior (1.46 ± 0.03 Mg ha-1). The analyzed forest fragments presented a weighted PyC stock of 1.37 ± 0.65 Mg ha-1. The vertical distribution of PyC declined in depth with 70% of PyC concentrated in the surface soil layers (0-30 cm). These results indicate that the PyC accumulated in the vertical profile of soils in forest fragments in Amazonia are important, and they need to be considered in Brazilian and global reports on carbon stocks and fluxes.

The relationship between knowledge and disaster preparedness of undergraduates responding to forest fires.

Forest and land fires are disasters that regularly occur on the island of Kalimantan, Indonesia. Higher education students on the island of Kalimantan are considered vulnerable to these disasters, hence the need for mandatory disaster knowledge and preparedness for every individual in the area. This research aimed to: (1) determine disaster knowledge and student preparedness in dealing with forest and land fire disasters, (2) determine the relationship between knowledge and student preparedness. This study used a quantitative correlation method with a questionnaire. The data were processed using the Statistical Package for the Social Sciences 21. The research sample used purposive sampling because it followed the needs of the study, which involved 300 students affected by forest fires, representing three universities located in a forest fire-prone area in West Kalimantan province, Indonesia. Each campus has 100 students, a total of 300 students. The results showed as many as 284 students had experienced forest and land fire disasters. In addition, 202 out of 284 students were known to have low disaster knowledge. Four main parameters of disaster preparedness were used to measure student preparedness, namely (1) knowledge and attitudes, (2) emergency response plans, (3) disaster warning system and (4) resource mobilisation. The number of students with high preparedness was 141, while 143 had low preparedness. Thus, student preparedness measures need to be increased to avoid disaster impact. Contribution: Based on the data analysis, students' knowledge and preparedness to face forest fires are positively related. It was shown that the higher the learning of students, the higher their readiness and vice versa. It is suggested that there is a need for increasing knowledge and preparedness actions for students in dealing with forest fire disasters through regular disaster lectures, simulations and training so that they can make the right decisions in coping with disasters.

Impacts of Wildfire Smoke and Air Pollution on a Pediatric Population with Asthma: A Population-Based Study.

Wildfires are increasing yearly in number and severity as a part of the evolving climate crisis. These fires are a significant source of air pollution, a common driver of flares in cardiorespiratory disease, including asthma, which is the most common chronic disease of childhood. Poorly controlled asthma leads to significant societal costs through morbidity, mortality, lost school and work time and healthcare utilization. This retrospective cohort study set in Calgary, Canada evaluates the relationship between asthma exacerbations during wildfire smoke events and equivalent low-pollution periods in a pediatric asthma population. Air pollution was based on daily average levels of PM2.5. Wildfire smoke events were determined by combining information from provincial databases and local monitors. Exposures were assumed using postal codes in the health record at the time of emergency department visits. Provincial claims data identified 27,501 asthma exacerbations in 57,375 children with asthma between 2010 to 2021. Wildfire smoke days demonstrated an increase in asthma exacerbations over the baseline (incidence rate ratio: 1.13; 95% CI: 1.02-1.24); this was not seen with air pollution in general. Increased rates of asthma exacerbations were also noted yearly in September. Asthma exacerbations were significantly decreased during periods of COVID-19 healthcare precautions.

Mapping the forest fire risk zones using artificial intelligence with risk factors data.

Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires, and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of 11 human-induced factors, a natural factor, and temperature, which is one of the risk factors that determine the conditions for the occurrence of forest fires. Moreover, k-means clustering algorithm, which is an artificial intelligence method, was employed in preparation of the fire risk zone map. Turkey was selected as the study area because there are social and economic variations among its regions. Thus, the regional forest directorates in Turkey were separated into four clusters as extreme-risk zone, high-risk zone, moderate-risk zone, and low-risk zone. Also, a map presenting these risk zones were provided. The map reveals that, in general, the western and southwestern coastal areas of Turkey are at high risk of forest fires. On the other hand, the fire risk is relatively low in the northern, central, and eastern areas.

Towards a whole-system framework for wildfire monitoring using Earth observations.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole-system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre-fire vegetation inventories, (2) active-fire monitoring, (3) post-fire assessment, and (4) multi-scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co-development of information products using cloud computing that benefits from open-access Earth observation and other geospatial data layers.

The Role of Wildfires in the Interplay of Forest Carbon Stocks and Wood Harvest in the Contiguous United States During the 20th Century.

Wildfires and land use play a central role in the long-term carbon (C) dynamics of forested ecosystems of the United States. Understanding their linkages with changes in biomass, resource use, and consumption in the context of climate change mitigation is crucial. We reconstruct a long-term C balance of forests in the contiguous U.S. using historical reports, satellite data, and other sources at multiple scales (national scale 1926-2017, regional level 1941-2017) to disentangle the drivers of biomass C stock change. The balance includes removals of forest biomass by fire, by extraction of woody biomass, by forest grazing, and by biomass stock change, their sum representing the net ecosystem productivity (NEP). Nationally, the total forest NEP increased for most of the 20th century, while fire, harvest and grazing reduced total forest stocks on average by 14%, 51%, and 6%, respectively, resulting in a net increase in C stock density of nearly 40%. Recovery from past land-use, plus reductions in wildfires and forest grazing coincide with consistent forest regrowth in the eastern U.S. but associated C stock increases were offset by increased wood harvest. C stock changes across the western U.S. fluctuated, with fire, harvest, and other disturbances (e.g., insects, droughts) reducing stocks on average by 14%, 81%, and 7%, respectively, resulting in a net growth in C stock density of 14%. Although wildfire activities increased in recent decades, harvest was the key driver in the forest C balance in all regions for most of the observed timeframe.

Extreme fire weather is the major driver of severe bushfires in southeast Australia.

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.