Forest Fires Research Articles

Predicting the Duration of Forest Fires Using Machine Learning Methods

For thousands of years forest fires played the role of a regulator in the ecosystem. Forest fires contributed to the ecological balance by destroying old and diseased plant material; but in the modern era fires are a major problem that tests the endurance not only of government agencies around the world, but also have an effect on climate change. Forest fires have become more intense, more destructive, and more deadly; these are known as megafires. They can cause major economic and ecological problems, especially in the summer months (dry season). However, humanity has developed a tool that can predict fire events, to detect them in time, but also to predict their duration. This tool is artificial intelligence, specifically, machine learning, which is one part of AI. Consequently, this paper briefly mentions several methods of machine learning as used in predicting forest fires and in early detection, submitting an overall review of current models. Our main overall objective is to venture into a new field: predicting the duration of ongoing forest fires. Our contribution offers a new way to manage forest fires, using accessible open data, available from the Hellenic Fire Service. In particular, we imported over 72,000 data from a 10-year period (2014–2023) using machine learning techniques. The experimental and validation results are more than encouraging, with Random Forest achieving the lowest value for the error range (8–13%), meaning it was 87–92% accurate on the prediction of forest fire duration. Finally, some future directions in which to extend this research are presented.

Public perceptions of the health impacts of climate change: a survey study in Greece

Abstract Background Climate change is the biggest global health threat of the 21st century. Despite the tremendous challenges, many people remain unaware of the human health implications of climate change. People perceptions about the health harms of climate change can increase public support for the action needed to reduce climate change. The aim of this study was to examine the public perception of climate change as a human health issue in Greece. Methods A population survey study was conducted from October to December 2023 in the municipalities of Messini and Alexandroupoli, Greece, following proportional quota sampling. A questionnaire was developed and included measures about the socio-demographic background of participants, their understanding of key facts about climate change and their views about climate change thread to human health. Results 406 individuals with a mean age 37.7 (sd = 14.8) participated in the study. Participants understood that climate change is happening (93.6%) and 54% of them think climate change is mostly or entirely caused by human activities, whereas 38% think it is caused equally by human activities and natural causes, and about 8% think climate change is mostly or entirely caused by natural causes. A majority of participants reported that climate change has adversely affected the health of people in various way, either a moderate amount or a great deal, including physical or mental harm from forest fires (90%), physical or mental harm from storms and floods (87%), heat-related illnesses (82%), illness due to reduced outdoor air quality (70%) and loss of housing for residents displaced by extreme weather events (70%). Conclusions Participants viewed climate change as an important cause of health harm, however they partially understood that climate change is caused by humans. Public health authorities should educate the public and policymakers about the climate change and advocate for policies to address the human health challenges of climate change. Key messages • Public health authorities should educate the public and policymakers about the climate change problem and its impact to human health. • Providing people with information about the health harms of climate change can increase public support for the actions needed to reduce emissions.

Integrated Anomaly Detection and Early Warning System for Forest Fires in the Odisha Region

The present study aims to develop a random forest algorithm-based classifier to predict the occurrence of fire events using observed meteorological parameters a day in advance. We considered the skin temperature, the air temperature close to the surface, the humidity close to the surface level, and soil moisture as important meteorological factors influencing forest fire occurrence. Twenty additional parameters were derived based on these four parameters that account for the energy exchanged in sensible and latent forms and the change in parameters in recent trends. We used the mutual information approach to identify critical meteorological parameters that carry significant information about fire occurrence the next day. The top nine parameters were then fed as input to the random forest algorithm to predict fire/no fire the next day. The weighted data sampling and SMOTE techniques were employed to address the class imbalance in the fire data class. Both techniques correctly classified fire incidents well, given the meteorological input from the previous days. This study also showed that as the class imbalance increases to 1:9, the performance based on the precision, recall, F1 score, and accuracy are maximum, showing the model’s ability to perform with class imbalance. Both techniques helped the random forest algorithm forecast fire instances as the data sample size increased.

Smoldering Ignition and Transition to Flaming Combustion of Pine Needle Fuel Beds: Effects of Bulk Density and Heat Supply

The smoldering of pine needle fuel beds (PNBs) has been a common subject of research because of its importance in initiating the rekindling of forest floor fires. Experimental studies of the coupling effects of the bulk density and external heat supply on smoldering in PNBs have been scarce up to now. In this study, laboratory smoldering experiments were conducted to study the coupling effects of bulk density (30–55 kg m−3) and heat supply (ignition-off temperature Toff = 190 °C and 230 °C). Different ignition modes were observed under the same conditions, including non- ignition (NI), flaming ignition (FI), and the smoldering-to-flaming (StF) transition. The results in this study showed that the bulk density had distinct effects on different ignition modes: the increase in the bulk density facilitated the StF transition but impeded the FI. The coupling effects between the bulk density and heat supply became more intricate, especially at lower bulk densities and at a reduced heat supply. Additionally, a simple energy balance equation was established to explain the coupling effects of bulk density and heat supply on ignition behavior. The critical mass loss rate (MLR) for the StF transition ranged from 0.01 g s−1 to 0.03 g s−1, while the critical MLR for FI was 0.035 g s−1. The modified combustion efficiency (MCE) index for the StF transition decreased from approximately 79.6% to 70.1% as the density increased from 30 kg m−3 to 55 kg m−3. In contrast, the MCE for FI was approximately 90% across all the bulk densities. The StF transition delay time increased from 50 s at 30 kg m−3 to 1296 s at 55 kg m−3 when Toff = 230 °C. Further reduction in heat supply led to an increase in the delay time for the StF transition by diminishing the intensity of smoldering combustion. This work advances the fundamental understanding of how heat supply and bulk density impact smoldering ignition modes, ultimately aiding in the development of wildfire prevention strategies.

A Case Study on the Integration of Remote Sensing for Predicting Complicated Forest Fire Spread

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. The timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and extinguish fires using scientific methods. This paper provides an analysis of models for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model (1983), which is noted for its broad adaptability in China as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model improves the existing model by adding elevation an factor and optimizes the method for acquiring certain parameters. By considering regional landforms (ridge lines, valley lines, and slopes) and vegetation coverage, this paper establishes three-dimensional visual interpretation markers for identifying hotspots; the orientation of the hotspots can be identified to simulate the spread of the fire uphill, downhill, in the direction of the wind, left-level slope, and right-level slope. Then, the data of Sentinel-2 and DEM were used to invert the fuel humidity and slope of pixels in the fire line areas. The statistical inversion data from pixels, which replaced fixed-point values in traditional models, were utilized for predicting forest fire spread speed. In this paper, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China, and verified using high-time-resolution Himawari-8 data, Gaofen-4 data, and historical data. The results demonstrate that the direction and maximum speed of fire spread for the fire lines in Baifen Mountai, Jiaguer Villageand, Muchanggou, Xujiabaozi, and Zhaizigou are uphill, 16.5 m/min; wind direction, 17.32 m/min; wind direction, 1.59 m/min; and wind direction, 5.67 m/min. The differences are mainly due to the locations of the fire lines, moisture content of combustibles, and maximum slopes being different. Across the entire fire line area, the average rate of increase in the area of open flames within one hour was 3.257 hm2/10 min (square hectares per 10 min), closely matching the average increase rate (3.297 hm2/10 min) monitored by the Himawari-8 satellite in 10 min intervals. In contrast, conventional fixed-point fire spread models predicted an average rate of increase of 3.5637 hm2/10 min, which shows a larger discrepancy compared to the Himawari-8 satellite monitoring results. Moreover, when compared to the fire spot monitoring results from the Gaofen-4 satellite taken 54 min after the initial location of the fire line, the predictions from the RS-enabled fire spread model, which integrates remote sensing interpretations, closely matched the actual observed fire boundaries. Although the predictions from the RS-enabled fire spread model and the traditional model both align with historical data in terms of the overall fire development trends, the RS-enabled model exhibits higher reliability and can provide more accurate information for forest fire emergency departments, enabling effective pre-emptive measures and scientific firefighting strategies.

Nest site selection and threats to nesting colonies of white-rumped Vulture (Gyps bengalensis) in Himachal Pradesh

White-rumped Vulture (Gyps bengalensis) suffered the most significant population decline among the three species of Gyps vultures impacted by the South Asia-wide diclofenac toxicity of the 1990s. Although the population lost about 99 % of its individuals, nesting populations still remain in a few pockets in India. One such population is known from the Himalayan foothills in the Kangra region, representing the northernmost nesting population of the species in India. From 2020–2024, we carried out an extensive study on the nesting ecology of the species, identifying 17 colonies with 617 active nests in 553 trees in an area of 5739 SqKm, constituting the highest nesting population reported in India. The smallest colony had ten nests, while the largest had 68. Except for a single nest on a Ficus religiosa tree, all others were on old-growth Chir Pine, Pinus roxburghii, having an average GBH of 254.8 cm (± 49.3 SD). By analyzing 18 variables, we determined vulture preferences for nest tree characteristics. Results indicate that nesting site selection primarily depends on GBH, canopy cover and nearest nest distance. About 80 % of nests were found between 600 and 800 m elevation. The primary threats include forest fire, resin tapping, and tree felling. We recommend protecting larger-sized old-growth forests through awareness campaigns with forest managers and local communities to safeguard the nest sites of the critically endangered bird.

Role of positive outlier cloud-to-ground lightning strokes in initiating forest fires in India

Role of positive outlier cloud-to-ground lightning strokes in initiating forest fires in India

A fundamental analysis on PAHs in food products to detect the toxicity index using fuzzy logic system

Polycyclic Aromatic Hydrocarbons (PAHs) are complex chemical compounds that occur naturally in unprocessed food when it is exposed to contaminated air during transportation, natural emission such as volcano, forest fire and through pesticides spray. It is reported by different agencies that there are 16 types of PAHs in which BaP (Benzo[a] pyrene), BaA (Benz[a]anthracene), BbF(Benzo [b] fluoranthene), Chr (Chrysene) are considered to be carcinogenic and it can occur due to different processes. In processed food it occurs due to various processing methods like overheating, incomplete burning, drying etc. The presence of PAH in food is conventionally found through analytical, traditional, and semi-automatic methods. These methods are found to be valuable but expensive and time-consuming. Further, these methods are used only for the detection of PAHs and the toxicity level is measured or identified based on expert knowledge of researchers and the Standards. Therefore, in this research, a simple harmfulness index system has been developed using Fuzzy Logic System(FLS). The proposed system has been designed based on the PAH values of different food and food products. Hence to initiate the study and to determine the significance of the results, PAH data have been collected from different articles that have investigated food products experimentally. These PAH data were analyzed using statistical measures such as Min, Mean, Max, Standard Deviation, Variance and Kurtosis method. Based on the observations from the results, the fuzzy sets were designed with four membership functions for each PAH and the rules were framed. The strength output from the inference engine has been associated with harmfulness index such as normal, low risk, medium risk, and high risk. From the evaluation, it can be observed that 89.72% of the food samples were recognized along with their degree of harmfulness. Also it can be inferred that 11% of the misclassified samples showed clear metrics of their harmfulness with PAH variations.

The history of fighting forest fires in pre–revolutionary Russia (XVIII – early XX centuries)

Throughout its history, Russia has faced various geopolitical, environmental, and social challenges. Some of them have remained in the past, and some continue to operate now. One of these challenges is the problem of forest fires. They were a severe disaster for our people during the time of Kievan Rus, and remain so in the XXI century. Since ancient times, the state has paid attention to the prevention of forest fires, trying to prevent them, and if this failed, it was engaged in the organization of firefighting. This article provides an overview of the history of the spread of forest fires from the time of Kievan Rus to the end of the XIX century. and their causes. The analysis of the development of state measures aimed at combating forest fires is given – the formation and development of forest legislation, the formation of a corps of foresters and fire chiefs, the organization of firefighting and the involvement of peasants in this matter. The role of zemstvo institutions in the fight against forest fires in the second half of the XIX century is considered. The article concludes that the state policy of combating forest fires has gone a long way from punishing arsonists to creating a developed structure of forest protection agencies and a set of laws, but as far as extinguishing fires, the main burden fell on the shoulders of peasants. The relevance of the study is due to the fact that forest fires still occur on a different scale every year and the historical experience of fighting them can be useful nowadays. На протяжении своей истории Россия сталкивалась с различными геополитическими, экологическими, социальными вызовами. Некоторые из них остались в прошлом, а некоторые продолжают действовать и сейчас. Одним из таких вызовов является проблема лесных пожаров. Они являлись тяжелым бедствием для нашего народа во времена Киевской Руси, остаются таковым и в XXI в. Государство с древних времен уделяло внимание профилактике лесных пожаров, пытаясь предотвратить их, а если это не удавалось – то занималось организацией пожаротушения. В данной статье представлен обзор истории распространения лесных пожаров со времен Киевской Руси до конца XIX в. и их причин. Дается анализ развития государственных мер, направленных на борьбу с лесными пожарами, – становление и развитие лесного законодательства, формирование корпуса лесничих и пожарных старост, организация пожаротушения и привлечение крестьян к этому делу. Рассматривается роль земских учреждений в борьбе с лесными пожарами во второй половине XIX в. В статье делается вывод о том, что государственная политика борьбы с лесными пожарами прошла большой путь развития от наказания для поджигателей до создания развитой структуры лесоохранительных органов и комплекса законов, но в том, что касается тушения пожаров, основная нагрузка ложилась на плечи крестьян. Актуальность исследования обусловлена тем, что лесные пожары и сейчас в разных масштабах происходят каждый год и исторический опыт борьбы с ними может быть полезен в наши дни.

Where to start with climate-smart forest management? Climatic risk for forest-based mitigation

Abstract. Natural disturbances like windthrows or forest fires alter the provision of forest ecosystem services such as timber production, protection from natural hazards, and carbon sequestration. After a disturbance, forests release large amounts of carbon and therefore change their status from carbon sinks to carbon sources for some time. Climate-smart forest management may decrease forest vulnerability to disturbances and thus reduce carbon emissions as a consequence of future disturbances. But how can we prioritise the stands most in need of climate-smart management? In this study we adopted a risk mapping framework (hazard times vulnerability) to assess the risk to climate-related forest ecosystem services (carbon stock and sink) in forests prone to windthrow (in the Julian Alps, Italy) and forest fires (in the Apennines, Italy). We calculated hazard by using forest fire and windthrow simulation tools and examined the most important drivers of the respective hazards. We then assessed vulnerability by calculating current carbon stocks and sinks in each forest stand. We combined these values together with the calculated hazard to estimate “carbon risk” and prioritised high-risk stands for climate-smart management. Our findings demonstrate that combining disturbance simulation tools and forest carbon measurements may aid in risk-related decision-making in forests and in planning decisions for climate-smart forestry. This approach may be replicated in other mountain forests to enhance our understanding of their actual carbon vulnerability to forest disturbances.

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones.

Evolution of Reactive Organic Compounds and Their Potential Health Risk in Wildfire Smoke.

Wildfires are an increasing source of emissions into the air, with health effects modulated by the abundance and toxicity of individual species. In this work, we estimate reactive organic compounds (ROC) in western U.S. wildland forest fire smoke using a combination of observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign and predictions from the Community Multiscale Air Quality (CMAQ) model. Standard emission inventory methods capture 40-45% of the estimated ROC mass emitted, with estimates of primary organic aerosol particularly low (5-8×). Downwind, gas-phase species abundances in molar units reflect the production of fragmentation products such as formaldehyde and methanol. Mass-based units emphasize larger compounds, which tend to be unidentified at an individual species level, are less volatile, and are typically not measured in the gas phase. Fire emissions are estimated to total 1250 ± 60 g·C of ROC per kg·C of CO, implying as much carbon is emitted as ROC as is emitted as CO. Particulate ROC has the potential to dominate the cancer and noncancer risk of long-term exposure to inhaled smoke, and better constraining these estimates will require information on the toxicity of particulate ROC from forest fires.

Rising forest exposure and fire severity from climate warming amplify tree cover losses from wildfire in California

Abstract Warmer temperatures and severe drought are driving increases in wildfire activity in the western United States, threatening forest ecosystems. However, identifying the influence of fire severity on tree cover loss (TCL) is challenging using commonly used categorical metrics. In this study, we quantify regional trends in wildfire-driven TCL as the product of annual burned area, average forest exposure (pre-fire tree cover), and average fire severity (relative loss of tree cover). We quantified these trends with Landsat-based 30 m resolution fire and tree cover datasets for California wildfires from 1986–2021. Rates of TCL rose faster than trends in burned area, with the magnitude of tree cover area loss per unit of area burned increasing by 70% from 0.20 ± 0.05 during 1986–1996 to 0.34 ± 0.10 during 2011–2021. Forest exposure (pre-fire tree cover) within fires increased by 41% from a decadal mean of 23.4% ± 5.5% (1986–1996) to 33.1% ± 7.8% (2011–2021). Increasing forest exposure is associated with a recent expansion of fires in dense northern forests. Concurrently, fire severity (relative TCL) rose by 30% from a decadal mean of 50.4% ± 7.2% during 1986–1996 to 65.6% ± 6.5% during 2011–2021. We developed and applied a simple conceptual framework to quantify the combined effect of wildfires affecting denser forests and burning more severely. The combined effect of these two processes contributed to nearly half (47%) of the TCL since 1986, highlighting that recent changes in burned areas alone cannot explain observed tree cover trends. Linear regression analysis revealed that warmer summers and drier winters were significant drivers of increasing forest exposure, fire severity, and burned area (R 2 from 0.54 to 0.80, p ⩽ 0.001), particularly in the northern forests. Climate extremes had a disproportionate impact on dense forests that were once more resistant to wildfire but now face risks from a shifting wildfire regime.

Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management

AbstractForests play a pivotal role in protecting the environment, preserving vital natural resources, and ultimately sustaining human life. However, the escalating occurrences of forest fires, whether of human origin or due to climate change, poses a significant threat to this ecosystem. In recent decades, the emergence of the IoT has been characterised by the utilisation of smart sensors for real‐time data collection. IoT facilitates proactive decision‐making for forest monitoring, control, and protection through advanced data analysis techniques, including AI algorithms. This research study presents a comprehensive approach to deploying a dynamic and adaptable network topology in forest environments, aimed at optimising data transmission and enhancing system reliability. Three distinct topologies are proposed in this research study: direct transmission from nodes to gateways, cluster formation with multi‐step data transmission, and clustering with data relayed by cluster heads. A key innovation is the use of high‐powered telecommunication modules in cluster heads, enabling long‐range data transmission while considering energy efficiency through solar power. To enhance system reliability, this study incorporates a reserve routing mechanism to mitigate the impact of node or cluster head failures. Additionally, the placement of gateway nodes is optimised using meta‐heuristic algorithms, including particle swarm optimisation (PSO), harmony search algorithm (HSA), and ant colony optimisation for continuous domains (ACOR), with ACOR emerging as the most effective. The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.

Development of 1 ×1 km gridded emission inventory for air quality assessment and mitigation strategies from open biomass burning in Karnataka, India

In this work, we have developed a high-resolution (1 ×1 km grid) emission inventory of greenhouse gases and air pollutants from open biomass burning (OBB) in Karnataka for 2000, 2005, 2010, 2015, 2020, and 2022. Secondary and field datasets were used to validate the emission inventories. The GIS-based statistical model and activity data were used to estimate greenhouse and air pollutant gas emissions from OBB (Forest, grassland, and crop fires). The uncertainty analysis of the emissions were done using the Monte Carlo Simulation (MCS) model and Low Emission Analysis Platform (LEAP) tool was used for projecting the emissions to 2050 based on the annual average growth rate. According to our results, the total OBB burned areas (%) in Karnataka during the aforementioned years were 24.43, 15.69, 15.95, 11.41, 26.73, and 5.78. The results showed that total annual average OBB emissions in Karnataka for 2000–2022 in Gg were SO2 (6.67), NOx (9.48), NH3(9.80), CO (670.12), OC (59.78), BC (5.09), CO2 (11071 .11), CH4(33.68), PM10(84.29), PM2.5(81.08), NMVOC (74.13), and NO2 (4.80) respectively. The districts of Kalburgi, Raichur, Bagalkot, Uttara Kannada, and Shivamogga have the highest emissions from OBB in Karnataka from 2000 to 2022. The emission uncertainty results showed ±26.92, ±28.17, and ±30.27 % for the crop, grassland, and forest fires, respectively. Under the business-as-usual (BAU) scenarios, the CH4 emissions from forest, grassland, and crop fires are projected at 8823, 2.64, and 5262 metric tonnes by 2030 and 58046, 10, and 30700 metric tonnes by 2050, respectively. The developed high-resolution inventories are the most up-to-date surface emission datasets with hotspots in Karnataka from OBB emission. National Clean Air Programme (NCAP) and India’s Net-Zero Emission target by 2070, our high-resolution emission inventories and mitigation strategies may be helpful for air quality monitoring, health benefits analysis, and policy-making studies in Karnataka, India.

THE IMPORTANCE OF FOREST FIRE PREVENTION ACTIVITIES

This study addresses the importance of forest fire prevention and combat activities, emphasizing environmental education as a tool for preventing forest fires in Canaã dos Carajás. The social relevance of the topic is highlighted by the significant impact of forest fires on the environment, economy, and public health, especially in the context of the Amazon Rainforest, which is essential for global climate regulation. The research is descriptive and combines data collection methods, including document analysis and semi-structured interviews. The document analysis involves reviewing reports, public policies, scientific articles, and educational materials, while the interviews are conducted with a representative sample of residents, educators, local authorities, and civil defense professionals. The general objective of the study is to analyze the effectiveness of environmental education activities in preventing forest fires. The specific objectives include analyzing residents’ perceptions of the importance of prevention, the influence of public policies, and proposing improvements in environmental education strategies. The research identified the urgent need for effective conservation policies, given the importance of the Amazon Rainforest in climate regulation. In Canaã dos Carajás, rapid urbanization driven by mining and a lack of resources and infrastructure are critical challenges for forest fire prevention. The interdisciplinary approach, using qualitative and quantitative techniques, allowed for a detailed analysis of the challenges faced and best practices. Location maps and satellite images were used to analyze the distribution of fire outbreaks. The results underline the importance of an integrated approach to fire prevention, highlighting the effectiveness of public policies and the need for ongoing training. This study provides valuable contributions to the formulation of public policies and educational strategies, promoting environmental education and active community involvement as key elements for effective forest fire prevention.

Spatiotemporal Analysis of Open Biomass Burning in Guangxi Province, China, from 2012 to 2023 Based on VIIRS

Open biomass burning has significant adverse effects on regional air quality, climate change, and human health. Extensive open biomass burning is detected in most regions of China, and capturing the characteristics of open biomass burning and understanding its influencing factors are important prerequisites for regulating open biomass burning. The characteristics of open biomass burning have been widely investigated at the national scale, with regional studies often focusing on northeast China, but few studies have examined regional discrepancies in spatiotemporal variations over a long timescale in Guangxi province. In this study, we used the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), combined with land cover data and high-resolution remote sensing images, to extract open biomass burning (crop residue burning and forest fire) fire points in Guangxi province from 2012 to 2023. We explored the spatial density distribution and temporal variation of open biomass burning using spatial analysis methods and statistical methods, respectively. Furthermore, we analyzed the driving forces of open biomass burning in Guangxi province from natural (topography, climate, and plant schedule), policy, and social (crop production and cultural customs) perspectives. The results show that open biomass burning is concentrated in the central, eastern, and southern parts of the study area, where there are frequent agricultural activities and abundant forests. At the city level, the highest numbers of fire points were found in Baise, Yulin, Wuzhou, and Nanning. The open biomass burning fire points exhibited large annual variation, with high levels from 2013 to 2015 and a remarkable decrease from 2016 to 2020 under strict control measures; however, inconsistent enforcement led to a significant rebound in fire points from 2021 to 2023. Forest fires are the predominant type of open biomass burning in the region, with forest fires and crop residue burning accounting for 76.82% and 23.18% of the total, respectively. The peak period for crop residue burning occurs in the winter, influenced mainly by topography, planting schedules, crop production, and policies, while forest fires predominantly occur in the winter and spring, primarily influenced by topography, climate, and cultural customs. The results indicate that identifying the driving forces behind spatiotemporal variations is essential for the effective management of open biomass burning.

Global rise in forest fire emissions linked to climate change in the extratropics.

Climate change increases fire-favorable weather in forests, but fire trends are also affected by multiple other controlling factors that are difficult to untangle. We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls. This delineation revealed that rapidly increasing forest fire emissions in extratropical pyromes, linked to climate change, offset declining emissions in tropical pyromes during 2001 to 2023. Annual emissions tripled in one extratropical pyrome due to increases in fire-favorable weather, compounded by increased forest cover and productivity. This contributed to a 60% increase in forest fire carbon emissions from forest ecoregions globally. Our results highlight the increasing vulnerability of forests and their carbon stocks to fire disturbance under climate change.

Seasonal changes in black carbon footprint on the Antarctic Peninsula due to rising shipborne tourism and forest fires.

Refractory black carbon (rBC) has great potential to increase melting when deposited on snow and ice surfaces. Previous studies attributed sources and impacts of rBC in the northern Antarctic Peninsula region by investigating long-range atmospheric transport from South Hemisphere biomass burning and industrial regions or by assessing impacts from local tourism and research activities. We used high-resolution measurements of refractory rBC in a firn core collected near the northern tip of the Antarctic Peninsula, as well as atmospheric rBC from Modern-Era Retrospective Analysis for Research and Applications, Version 2, satellite measurements, modeling, burned area data, and tourism statistics, to assess combined impacts of both long-range transported rBC and locally emitted rBC. Our findings suggest that tourism activities have a regional rather than local impact and the increase in rBC concentrations during late spring-summer, influenced by tourism activities and fires in the Southern Hemisphere, can enhance ice melt. This highlights the need for strategies to reduce local and distant rBC emissions.

FGYOLO: An Integrated Feature Enhancement Lightweight Unmanned Aerial Vehicle Forest Fire Detection Framework Based on YOLOv8n

To address the challenges of complex backgrounds and small, easily confused fire and smoke targets in Unmanned Aerial Vehicle (UAV)-based forest fire detection, we propose an improved forest smoke and fire detection algorithm based on YOLOv8. Considering the limited computational resources of UAVs and the lightweight property of YOLOv8n, the original model of YOLOv8n is improved, the Bottleneck module is reconstructed using Group Shuffle Convolution (GSConv), and the residual structure is improved, thereby enhancing the model’s detection capability while reducing network parameters. The GBFPN module is proposed to optimize the neck layer network structure and fusion method, enabling the more effective extraction and fusion of pyrotechnic features. Recognizing the difficulty in capturing the prominent characteristics of fire and smoke in a complex, tree-heavy environment, we implemented the BiFormer attention mechanism to boost the model’s ability to acquire multi-scale properties while retaining fine-grained features. Additionally, the Inner-MPDIoU loss function is implemented to replace the original CIoU loss function, thereby improving the model’s capacity for detecting small targets. The experimental results of the customized G-Fire dataset reveal that FGYOLO achieves a 3.3% improvement in mean Average Precision (mAP), reaching 98.8%, while reducing the number of parameters by 26.4% compared to the original YOLOv8n.