Search Results (2,915)

Efficient and accurate active fire detection is crucial for timely firefighting and mitigating hazards. Geostationary satellites deliver high-frequency observations that offer valuable data for near-real-time fire monitoring. However, current operational fire detection algorithms often underutilize temporal information, failing to decouple fire-induced anomalies from inherent surface thermal heterogeneity, which results in frequent false alarms. To address this limitation, we constructed a ten-day historical background brightness temperature (BT) reference database from multi-year Himawari-8/9 data, serving as a stable, fire-undisturbed baseline. Based on this, an active fire detection algorithm was developed that integrates instantaneous spatial–contextual differences with historical deviations of these differences from the reference database. Evaluated against a robust dataset of over 55,000 fire pixels (cross-verified using 10 m Sentinel-2 burn-scar data), the proposed algorithm significantly outperforms the Himawari-8/9 Wildfire (WLF) product, achieving a commission error (CE) of 2.9%, an omission error (OE) of 37.5%, and an F1-score of 0.76. The framework demonstrated superior detection accuracy in challenging scenarios such as low-temperature, smoke-obscured, and early-stage fires, while maintained robust performance across diverse fire types. The approach enables rapid full-disk fire detection in less than one minute and can be adapted to other geostationary satellites, providing a technical foundation for building a globally coordinated fire monitoring system. Full article

Wildfires cause major damage, and their accurate detection is crucial. A common approach to near-real-time detection uses Geostationary (GEO) satellite algorithms. A standard scheme for evaluating the accuracy of a GEO-based algorithm is to compare its detections with higher-resolution Low Earth Orbit (LEO) images, considering the latter as ground truth. The primary objective of this study is to quantify the prevalence of GOES ABI/VIIRS fire detection misalignments and assess their impact on the accuracy evaluation of the GOES Fire Detection and Characterization (FDC) product. Thus, the key question is how this evaluation should be performed. To this end, a large dataset of matching FDC/VIIRS fire detections across Western U.S., Amazonas, and Patagonia was constructed. Our finding is that for nearly 12% of fire events, there are spatial misalignments between FDC and VIIRS detections. Next, we show that using VIIRS as ground truth without considering these misalignments yields highly biased estimates. This affects the evaluation of the FDC product detection capabilities. Finally, we demonstrate that using a GOES FDC/VIIRS buffer window substantially mitigates the effect of misalignments. For example, the estimated false alarm rate ranges between 26% and 36% without a window, whereas using a $3\times 3$ window yields values between 7% and 15%. Full article

Anthropogenic wildfires account for the majority of wildfire ignitions in human-dominated landscapes, yet their spatial drivers remain insufficiently understood at national scales. This study aims to identify key factors influencing anthropogenic wildfire occurrence and to develop a robust and interpretable prediction framework using nationwide data from South Korea. Wildfire occurrence records from 2011–2021 were integrated with daily meteorological, environmental, and socio-economic variables at a 1 km grid resolution. A stacking ensemble model combining Random Forest, XGBoost, LightGBM, Extra Trees, and logistic regression was implemented to improve predictive robustness under rare-event conditions. Model performance was evaluated using ROC–AUC, PR–AUC, and threshold-optimized F1-scores, and variable contributions were interpreted using feature importance and SHAP analyses. The ensemble model achieved a PR–AUC of 0.934 and an ROC–AUC of 0.941. Relative humidity and maximum temperature were identified as influential meteorological variables, while human-accessibility-related variables, particularly distance to roads and agricultural land, showed consistently high contributions to spatial ignition probability. These findings indicate that anthropogenic wildfire occurrence is shaped by interactions between fire-weather conditions and spatial patterns of human accessibility. The proposed framework provides a scalable approach for understanding anthropogenic wildfire mechanisms and supporting prevention strategies in forested landscapes. Full article

Thermal contextual algorithms for 375 m VIIRS active fire detection can produce substantial commission errors over persistent non-wildfire heat sources (e.g., refineries, gas flares, and volcanoes), and globally fixed thresholds may be suboptimal under heterogeneous thermal backgrounds. We present a lightweight spatiotemporal prior layer that augments by applying prior-guided, pixel-level parameter switching during the discrimination stage. The layer combines: (i) a persistent non-wildfire thermal anomaly mask (PFM) derived from multi-year VNP14IMG recurrence and seasonality statistics on a 0.004° grid, and (ii) a short-term heat-source mask (SHM) based on nighttime VIIRS I4/I5 brightness temperature stability to capture newly emerged or rapidly intensifying static sources. Pixels flagged by either prior are processed with a stricter parameter set, while other pixels follow the baseline setting. We evaluate the method using a stratified validation dataset (N = 3435) spanning industrial/urban clusters, volcanic regions, forest/grassland wildfires, and fragmented crop residue burning, with validation supported by independent high-resolution imagery (Sentinel-2/Landsat) and external POI datasets. The framework markedly reduces false positives in high-interference zones (industrial/urban false positive rate from 88.6% to 22.7%; volcanic from 100.0% to 57.3%) while preserving high performance for forest/grassland wildfires (F1 ≈ 0.999). For fragmented residue burning, omission error decreases from 11.2% to 1.3%, improving detection completeness without an apparent increase in commission errors. Overall, the results suggest that integrating long- and short-term spatiotemporal priors via threshold switching can improve the robustness and interpretability of contextual VIIRS fire detection under complex thermal backgrounds in the evaluated scenarios. Full article

Cellular automata-based wildfire simulation models are widely used to support fire management, risk assessment, and operational decision-making, due to their efficiency and computational advantages. However, the accuracy of these models heavily depends on the quality of input data provided by the user, including the composition and geospatial extend of forest fuels, current meteorological conditions and terrain information. This publication examines how quantitative and spatial input data uncertainties affect the estimates of the impacted areas. Using a series of simulation experiments, inaccurate data are introduced to specific input variables (such as the vegetation type and the fuel moisture content) to reflect realistic levels of uncertainty commonly observed in operational scenarios, where users with different cognitive backgrounds fail to properly identify key characteristics of a fire. Model outputs are then compared using spatial and temporal performance metrics, including the rate of spread and burned area extent. The results demonstrate that uncertainties in fuel models and meteorological inputs exert a dominant influence on simulated fire behavior. Our findings highlight the sensitivity of wildfire simulations to compounded input uncertainties and stress the need for improved in-field data acquisition strategies. Full article

School classrooms represent complex, interconnected systems where indoor environmental quality critically influences student health, cognitive performance, and educational equity. Yet traditional approaches operate in disciplinary silos, creating systemic failures in design, operation, and maintenance. This narrative review adopts a systems engineering framework to demonstrate how integrated interventions—spanning policy, design, technology, and operations—create resilient, sustainable, and healthy classroom climates. Amid escalating climate change impacts (rising temperatures, heatwaves, wildfires) and emerging threats (airborne pathogens, urban pollution), reactive measures like school closures prove pedagogically counterproductive. This review synthesizes evidence on natural, mechanical, and mixed-mode ventilation systems optimized through advanced control strategies, smart technologies, and health-centred policies. Key findings reveal that synergistic integration of Policy, Management, Construction, Operation, and Smart Technologies, in a systems engineering framework, outperforms singular strategies. Critical interventions include hybrid ventilation coupled with layered defences (HEPA filtration, UVGI), AI-driven adaptive controls using IoT sensors and Model Predictive Control to optimize energy while managing pollutant concentrations, and mandatory IAQ standards rooted in stakeholder education. By framing classrooms as interconnected engineering systems, this work provides actionable insights for architects, engineers, policymakers, and administrators, positioning future school design toward resilience, sustainability, and human-centred health outcomes. Full article

Recently, numerous countries have experienced devastating wildfires, leading to significant destruction and loss of life. These catastrophic events highlight the shortcomings in current building regulations and testing methods. There is a pressing need for a more profound understanding of the characteristics and behaviour of large outdoor fires to address these inadequacies effectively. Wildfires can spread to structures located at the wildland–urban interface, leading to further fire propagation from one building to another. In this study, the Fire Dynamics Simulator (FDS) model was validated using experimental data from the National Institute of Standards and Technology (NIST). The experiment consisted of a target wall and a small wooden shed containing six wooden cribs as fuel, with a separation distance of 3 m. Both FDS and the experiment proved that 3 m is the safe separation distance. Different shed materials, such as steel, were used, which reduced the total heat release rate by 40% and the flame height by 20%. The effects of wind speed and direction were investigated using two wooden sheds in FDS to observe fire spread between them. The safe separation distance was 3 m for both wind speeds (2 and 5 m/s) in all directions, where the critical temperature was not reached to cause self-ignition of the second shed, except in the north direction (inward) at a speed of 5 m/s. When the separation distance increased to 3.5 m, the average heat flux at the other shed reduced to 3.18 kW/m², which did not cause self-ignition. Therefore, the safe separation distance between two structures for a wind speed of 5 m/s should be 3.5 m to mitigate the spread of fire based on the shed dimensions and the fire source load. Full article

Southern Africa is highly sensitive to climate variability associated with the El Niño Southern Oscillation (ENSO), which strongly influences hydroclimate, vegetation dynamics, and atmospheric composition. This study examined the impacts of the 2015/16 El Niño on vegetation, meteorological conditions, and atmospheric emissions over Southern Africa using satellite observations and reanalysis data. Time-lagged cross-correlation analysis of seasonally adjusted time-series was applied to characterize synchronous and delayed interactions among vegetation indices, hydrological variables, meteorological drivers, and air-quality parameters. Bayesian causal impact analysis was further used to quantify El Niño-induced anomalies by comparing observed conditions with counterfactual scenarios representing the absence of the event. The results showed that vegetation greenness responds primarily to concurrent moisture availability, with strong positive associations between NDVI, precipitation, soil moisture, and canopy water. Moisture-related variables exert delayed influences on atmospheric composition, highlighting the role of wet scavenging and dilution. Carbonaceous aerosols (black carbon [BC] and organic carbon [OC]), particulate matter [PM_2.5], and aerosol optical depth exhibit strong synchronous coupling, indicating a dominant biomass-burning source. The causal impact analysis reveals statistically significant and sustained post-2015 increases in fire-related emissions (carbon monoxide [CO], BC, OC, PM_2.5, and aerosol optical depth [AOD]), particularly during austral winter and dry seasons. In contrast, precipitation, soil moisture, evapotranspiration, and vegetation greenness show persistent negative anomalies, reflecting widespread drought stress under elevated temperatures. Overall, the findings demonstrate that the 2015/16 El Niño amplified fire emissions while suppressing ecosystem functioning across Southern Africa, underscoring strong climate–fire–vegetation feedback with important air-quality and environmental implications. Full article

For the last three summers in Canada (2023–2025), and episodically in Siberia over the previous decade and a half, severe consequences from wildfires represent major ecological and societal impacts: the displacement of inhabitants; destruction of buildings, timber and infrastructure; and far-field air pollution. Wildfire occurrence is increasingly supported every summer by persistent surface warming and widespread atmospheric moisture deficits. The two recent major Canadian fire years in 2023 and 2025 show some contrasts: 2023 was dominated by an early June event with preconditioning, whereas 2025 saw repeated single events spanning June to early August, culminating in a significant late-summer event. Events in both years were associated with North Pacific–North American atmospheric blocking regimes. Over the longer term, 2003–2025, normalized June–September wildfire fraction anomalies in the Canadian sector (45–60° N, 150–60° W) show the post-2023 period as having new, clear, record-breaking fire intensities, highlighting wildfires as emerging dominant Arctic–subarctic extremes. Siberia shows an increase after 2010. Although multiple environmental Arctic–subarctic extremes are ongoing—such as sea-ice loss, storms, and glacial ice loss—the impacts from wildfires represent preeminent, growing societal consequences. Full article

This multi-university, multi-disaster study examined associations among prior trauma exposure, disaster exposure, and post-disaster life stressors with mental health outcomes, as well as the potential protective roles of a perceived altruistic community, post-disaster social support exchanges, and changes in world beliefs. University students in disaster-affected areas of the mainland United States and Puerto Rico (N = 666; 77.5% female; M age = 21.26) completed an online survey assessing disaster exposure, post-disaster life stressors, perceptions of community unity, social support exchanges, post-disaster changes in world beliefs, and symptoms of posttraumatic stress (PTSS), depression, and anxiety. Younger age emerged as a risk factor for depression and anxiety, and Black participants reported higher PTSS than White participants. Greater lifetime trauma exposure, experiencing the hurricanes in Puerto Rico or the California wildfires (compared to mainland hurricanes), and reporting more post-disaster life stressors were each associated with elevated PTSS, depression, and anxiety symptoms. In contrast, a stronger sense of an altruistic community was associated with lower levels of these symptoms. More positive post-disaster changes in beliefs about the world were related to lower PTSS and depression, whereas greater involvement in social support exchanges was associated with higher PTSS. Findings underscore the importance of identifying both risk and protective factors that shape young adults’ post-disaster adjustment. Full article

This study examines how research in the United States has addressed infrastructure resilience across different disaster contexts, situating the topic within broader discussions on climate-related risks and adaptation. Infrastructure resilience has gained increasing importance as communities face more frequent and severe natural hazards and as infrastructure systems become more complex and interconnected. A database of more than 7000 studies published over the past century by universities, research centers, and government agencies was compiled and organized, including supplemental works from regions such as Europe, Australia, Japan, Africa, and South America. The dataset provides a long-term perspective on the evolution of resilience-related research and reflects the scope of accessible literature indexed in major research repositories. Using systematic classification, each study was categorized by disaster type (i.e., floods, hurricanes, wildfires, heatwaves, and snowstorms) and by infrastructure system (i.e., transportation, water, energy, telecommunications, and buildings). A keyword-based relevance scoring method was applied to distinguish studies in which resilience is a central analytical focus from those in which it appears as a secondary or contextual concept. The results are presented through an interactive web-based platform that enables users to explore resilience research by state, year, disaster type, infrastructure category, and level of relevance. The analysis reveals a substantial increase in resilience-related publications in recent decades, with notable geographic and thematic concentrations. Transportation and water infrastructure dominates the literature, while energy systems, telecommunications, and digital infrastructure remain underrepresented. These findings highlight both progress and persistent gaps in infrastructure resilience research and support more integrated, system-oriented, and future-focused resilience planning. Full article

Background/Objectives: Fire is a dominant ecological force in Mediterranean ecosystems, shaping the adaptive traits of forest species such as Pinus brutia. Prescribed burning (also called controlled burning) is the intentional, carefully planned use of fire under specific environmental conditions to manage vegetation and reduce wildfire risk. While morphological and physiological fire adaptations are well-documented, emerging evidence highlights the role of epigenetic mechanisms—such as DNA methylation and histone modifications—in mediating stress responses. Methods: This study investigates genome-wide epigenetic changes in P. brutia following a prescribed burning experiment on Chios Island, Greece. Using methylation-sensitive amplified polymorphism (MSAP) analysis, we compared temporal shifts on epigenetic profiles before and after fire exposure extracting DNA from the same trees. Results: A significant increase in polymorphic epiloci, epigenetic diversity indices, and private epigenetic bands after prescribed burning was revealed, suggesting a stress-induced reprogramming of the epigenome. Concurrent measurements of midday needle water potential indicated an exploratory association between water stress and epigenetic shifts. Furthermore, Fireline Intensity (FI) correlated with epigenetic diversity index signaling an immediate response of the tree. Conclusions: These findings support the hypothesis that fire stress induces epigenetic responses in P. brutia, potentially enhancing resilience to future environmental challenges. Further research is required to address the level of heritability of these epigenetic changes in next generation and connect these indexes with adaptation and sustainability of forest ecosystems. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

In Canada and elsewhere, there is an ascendancy of a whole-of-society approach that centres shared responsibility for wildfire management. This article engages the debates on the rise of shared responsibility for wildfire management to argue that this context demands a renewed research focus on understanding how the public allocates responsibility for wildfire management. We illustrate this argument through a case study of public engagement with wildfire risk and shared responsibility in Whistler, British Columbia, western Canada. Our case study draws on evidence from a quantitative survey administered to 1311 participants in the spring and summer of 2024. The study reveals a near-universal concern about wildfires among the participants and a high level of risk perception. This is consistent with community climate and wildfire reports and plans. This level of concern is driving a high level of mitigation activity completion among participants, even though the level of preparedness is mixed. Our study found a marked pattern of responsibility allocation across the phases of wildfire management. Participants put the municipal government at the forefront of mitigation, preparedness, and response. The provincial government was ranked as most responsible for recovery. Homeowner responsibility declined as one moves from mitigation and preparedness through to response and recovery. Private actors, such as insurance, have greater responsibility in the recovery phase. Multivariate General Linear Models (GLMs) show that how respondents allocate responsibility for various aspects of wildfire management is influenced by home ownership, prior wildfire experience, perceived preparedness, and commitment to bearing the costs of FireSmart assessment. We conclude that a sustained research commitment is needed to further elucidate the dynamics of public expectations and attitudes in the context of shared responsibility for wildfire management. Full article

Background: Prenatal exposure to air pollutants may increase the risk of adverse respiratory outcomes, particularly in offspring of asthmatic mothers. Evidence on wildfire-related PM_2.5 exposure during pregnancy remains limited. This study investigated associations between early gestational wildfire-related PM_2.5 exposure, infant lung function, and respiratory outcomes at 6 years. Methods: Gestational wildfire-related PM_2.5 exposure patterns were characterised using group-based trajectory modelling and linked to infant lung function outcomes. Infant respiratory measurements were obtained at six weeks of age during behaviourally defined quiet sleep using tidal-breathing flow–volume loops (TBFVL). Airway mechanics at six years were assessed by impulse oscillometry (IOS) following international guideline standards. Trajectory modelling of PM_2.5 during gestation was conducted in SAS (PROC TRAJ); all additional statistical analyses were performed in Stata IC 16.1. Results: Increased mean tidal inspiratory flow (MTIF, beta coefficient [β]: 10.51 mL/s, 95% CI: 3.66 to 17.36, p = 0.003) and peak tidal inspiratory flow (PTIF, β: 12.49 mL/s, 95% CI: 2.48 to 22.51, p = 0.014) were observed in infants born to mothers with higher wildfire-related PM_2.5 exposure during early gestation (n = 420; n = 411 not exposed, n = 9 exposed). β-coefficients from infant mixed models were then used as proxy indicators and applied in linear regression models and associated with higher reactance at 5 Hz frequency (n = 73) at 6 years of age (PTIF: β: 9.88 mL/s, 95% CI: 0.10 to 19.67, p = 0.048 and MTIF: β: 13.43 mL/s, 95% CI: 1.43 to 25.44, p = 0.029). PTIF was further associated with asthma diagnoses at 6 years (aOR: 1.36, 95% CI: 1.07 to 1.73, p = 0.012; n = 259; n = 116 asthma). Conclusion: Early gestational exposure to wildfire-related PM_2.5 may be linked with altered respiratory patterns in infancy and differences in airway reactance during childhood. Findings also suggest a relationship with asthma risk, although mechanisms remain uncertain. Full article