Search Results (163)

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

Understanding and mapping forest fire vulnerability is essential for informed landscape management and disaster risk reduction, especially in the context of increasing anthropogenic and climatic pressures. This study aims to model and spatially predict forest fire vulnerability across Romania using two machine learning algorithms: MaxEnt and XGBoost. We integrated forest fire occurrence data from 2006 to 2024 with a suite of climatic, topographic, ecological, and anthropogenic predictors at a 250 m spatial resolution. MaxEnt, based on presence-only data, achieved moderate predictive performance (AUC = 0.758), while XGBoost, trained on presence–absence data, delivered higher classification accuracy (AUC = 0.988). Both models revealed that the impact of environmental variables on forest fire occurrence is complex and heterogeneous, with the most influential predictors being the Fire Weather Index, forest fuel type, elevation, and distance to human proximity features. The resulting vulnerability and uncertainty maps revealed hotspots in Sub-Carpathian and lowland regions, especially in Mehedinți, Gorj, Dolj, and Olt counties. These patterns reflect historical fire data and highlight the role of transitional agro-forested landscapes. This study delivers a replicable, data-driven approach to wildfire risk modelling, supporting proactive management and emphasising the importance of integrating vulnerability assessments into planning and climate adaptation strategies. Full article

Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.70° N) and B (116.34° E, 49.57° N) exhibited the highest number of days classified as “high” or “extremely high” risk during the April/May and September/October periods, consistent with actual fire occurrences. In summary, the integration of multi-source data fusion and Bayesian-optimized machine learning has enabled the first high-precision daily-scale wildfire risk prediction for the eastern Mongolian grasslands, thus providing a scientific foundation and decision-making support for wildfire prevention and control in the region. Full article

Forest fires in the Hindu Kush Himalaya (HKH) region are increasing in frequency and severity, driven by climate variability, prolonged dry periods, and human activity. Nepal, a critical part of the HKH, recorded over 22,700 forest fire events in the past decade, with fire incidence nearly doubling in 2023. Despite this growing threat, operational early warning systems remain limited. This study presents Nepal’s first high-resolution early fire risk outlook system, developed by adopting the Canadian Fire Weather Index (FWI) using meteorological forecasts from the High-Impact Weather Assessment Toolkit (HIWAT). The system generates daily and two-day forecasts using a fully automated Python-based workflow and publishes results as Web Map Services (WMS). Model validation against MODIS, VIIRS, and ground fire records for 2023 showed that over 80% of fires occurred in zones classified as Moderate to Very High risk. Spatiotemporal analysis confirmed fire seasonality, with peaks in mid-April and over 65% of fires occurring in forested areas. The system’s integration of satellite data and high-resolution forecasts improves the spatial and temporal accuracy of fire danger predictions. This research presents a novel, scalable, and operational framework tailored for data-scarce and topographically complex regions. Its transferability holds substantial potential for strengthening anticipatory fire management and climate adaptation strategies across the HKH and beyond. Full article

Wildfires are complex natural disasters that significantly impact ecosystems and human communities. The early detection and prediction of forest fire risk are necessary for effective forest management and resource protection. This paper proposes an innovative early detection system based on a wireless sensor network (WSN) composed of interconnected Arduino nodes arranged in a hybrid circular/star topology. This configuration reduces the number of required nodes by 53–55% compared to conventional Mesh 2D topologies while enhancing data collection efficiency. Each node integrates temperature/humidity sensors and uses ZigBee communication for the real-time monitoring of wildfire risk conditions. This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. Simulations for a 300 m radius area show the non-dense hybrid topology is the most energy-efficient, outperforming dense and Mesh 2D topologies. Additionally, the dense topology achieves the lowest packet loss rate (PLR), reducing losses by up to 80.4% compared to Mesh 2D. Adaptive routing, dynamic round-robin arbitration, vertical tier jumps, and GSM connectivity ensure reliable communication in remote areas, providing a cost-effective solution for wildfire mitigation and broader environmental monitoring. Full article

Background: The forestry industry plays an important role in the economy and environmental sustainability, facing significant logistical challenges such as the geographical dispersion of plantations, the variability of raw materials, and high transportation costs. Artificial Intelligence (AI) emerges as a promising tool to optimize logistics processes, contributing to the reduction in costs, waste, and environmental impacts. Methods: This study combines a literature review and case analysis to assess the impact of AI on forestry logistics. Machine Learning algorithms, optimization systems, and monitoring tools based on the Internet of Things (IoT) and computer vision were analyzed to assess impacts in areas such as transportation planning, inventory management, and forest monitoring. Results: The results demonstrated that optimization algorithms reduced transportation costs and carbon emissions. Predictive tools proved to be effective in inventory management, while real-time monitoring with drones and sensors allowed for the identification and mitigation of environmental risks, such as pests and fires, promoting greater operational efficiency. Conclusions: AI has great potential to transform forestry logistics, improving efficiency and sustainability. However, its implementation faces barriers such as high upfront costs and limitations in data collection, and strategic collaborations are needed to maximize its impact. Full article

Forest fires pose significant threats to ecosystems, economies, and human lives. However, existing forest fire risk assessments are over-reliant on field data and expert-derived indices. Here, we assessed the nationwide forest fire risk in South Korea using a dataset of 2289 and 4578 fire and non-fire events between 2020 and 2023. Twelve remote sensing-based environmental variables were exclusively derived from Google Earth Engine, including climate, vegetation, topographic, and socio-environmental factors. After removing the snow equivalent variable owing to high collinearity, we trained three machine learning models: random forest, XGBoost, and artificial neural network, and evaluated their ability to predict forest fire risks. XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. These findings demonstrate the potential of integrating cloud-based remote sensing with machine learning for large-scale, high-resolution forest fire risk modeling and have implications for early warning systems and effective fire management in vulnerable regions. Future predictions can be improved by incorporating seasonal, real-time meteorological, and human activity data. Full article

Wildfires impact forest ecosystems, affecting tree survival and physiological responses. This study explored the effects of surface fires on Pinus densiflora and Quercus variabilis, assessing mortality, internal injuries, and canopy health. By 2024, P. densiflora had an 18.0% mortality rate, whereas Q. variabilis exhibited no crown dieback. Morphological traits, including tree height, the bark scorch index (BSI), and bark thickness, influenced fire resistance. Despite superior stand characteristics, P. densiflora showed higher mortality due to thin bark, whereas Q. variabilis maintained xylem integrity. While sonic tomography (SoT) showed no significant differences, electrical resistance tomography (ERT) detected physiological stress, with higher ERT_R and ERT_Y area ratios correlating with mortality risk. Notably, F-W-W classified trees showed elevated resistance a year before mortality, suggesting ERT as a predictive tool. ERT_R values exceeding 15.0% were associated with a 37.5% mortality rate, whereas ERT_B values below 55.0% corresponded to 42.9% mortality. Despite fire exposure, canopy responses, including chlorophyll fluorescence and photosynthetic efficiency, remained stable, indicating that the surviving trees maintained functional integrity. This study underscores ERT’s efficacy in diagnosing fire-induced stress and predicting mortality risk. The findings highlight species-specific diagnostic criteria and inform post-fire management, supporting forest resilience through the early detection of high-risk trees and improved restoration strategies. Full article

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO₂ levels, and particulate matter. A comparative analysis of six machine learning models—Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet—demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety. Full article

The complex nature of immune system behavior in both autoimmune diseases and transplant rejection can be understood through the lens of avalanche dynamics in critical-point systems. This paper introduces the concept of the “immunological avalanche” as a framework for understanding unpredictable patterns of immune activity in both contexts. Just as avalanches represent sudden releases of accumulated potential energy, immune responses exhibit periods of apparent stability followed by explosive flares triggered by seemingly minor stimuli. The model presented here draws parallels between immune system behavior and other complex systems such as earthquakes, forest fires, and neuronal activity, where localized events can propagate into large-scale disruptions. In autoimmune conditions like systemic lupus erythematosus (SLE), which affects multiple organ systems including the kidneys in approximately 50% of patients, these dynamics manifest as alternating periods of remission and flares. Similarly, in transplant recipients, the immune system exhibits metastable behavior under constant allograft stimulation. This critical-point dynamics framework is characterized by threshold-dependent activation, positive feedback loops, and dynamic non-linearity. In autoimmune diseases, triggers such as UV light exposure, infections, or stress can initiate cascading immune responses. In transplant patients, longitudinal analysis reveals how monitoring oscillatory patterns in blood parameters and biological age markers can predict rejection risk. In a preliminary study on kidney transplant, all measured variables showed temporal instability. Proteinuria exhibited precise log–log linearity in power law analysis, confirming near-critical-point system behavior. Two distinct dynamic patterns emerged: large oscillations in eGFR, proteinuria, or biological age predicted declining function, while small oscillations indicated stability. During avalanche events, biological age increased dramatically, with partial reversal leaving persistent elevation after acute episodes. Understanding these dynamics has important implications for therapeutic approaches in both contexts. Key findings suggest that monitoring parameter oscillations, rather than absolute values, better indicates system instability and potential avalanche events. Additionally, biological age calculations provide valuable prognostic information, while proteinuria measurements offer efficient sampling for system dynamics assessment. This conceptual model provides a unifying framework for understanding the pathogenesis of both autoimmune and transplant-related immune responses, potentially leading to new perspectives in disease management and rejection prediction. Full article

In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, forest fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing ecological and economic losses, improving forest fire management efficiency, and ensuring personnel safety and property security. To enhance comprehensive understanding of wildfire prediction research, this paper systematically reviews studies since 2015, focusing on two key aspects: datasets with related tools and prediction algorithms. We categorized the literature into three categories: statistical analysis and physical models, traditional machine learning methods, and deep learning approaches. Additionally, this review summarizes the data types and open-source datasets used in the selected literature. The paper further outlines current challenges and future directions, including exploring wildfire risk data management and multimodal deep learning, investigating self-supervised learning models, improving model interpretability and developing explainable models, integrating physics-informed models with machine learning, and constructing digital twin technology for real-time wildfire simulation and fire scenario analysis. This study aims to provide valuable support for forest natural resource management and enhanced environmental protection through the application of remote sensing technologies and artificial intelligence algorithms. Full article

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance fire risk assessment in Chongli District. Our framework innovatively combines DEM data with Faster Regions with Convolutional Neural Networks (Faster R-CNN) and CNN-based methods, breaking through the limitations of traditional approaches that rely on manual feature extraction. It is capable of automatically identifying critical terrain features, such as mountain peaks and water systems, with higher accuracy and efficiency. DEMs provide high-resolution topographical information, which deep learning models leverage to accurately identify and delineate key geographical features. Our results show that the integration of DEMs and deep learning significantly improves the accuracy of fire risk assessment by offering detailed and precise terrain analysis, thereby providing more reliable inputs for fire behavior prediction. The extracted mountain peaks and water systems, as fundamental inputs for fire behavior prediction, enable more accurate predictions of fire spread and potential impact areas. This study not only highlights the great potential of combining geospatial data with advanced machine learning techniques but also offers a scalable and efficient solution for forest fire risk management in mountainous regions. Future work will focus on expanding the dataset to include more environmental variables and validating the model in different geographical areas to further enhance its robustness and applicability. Full article

Wildfires represent an increasing threat to ecosystems and communities, driven by climate change, fuel dynamics, and human activities. In Ambato, Ecuador, a city in the Andean highlands, these risks are exacerbated by prolonged droughts, vegetation dryness, and urban expansion into fire-prone areas within the Wildland–Urban Interface (WUI). This study integrates climatic, ecological, and socio-economic data from 2017 to 2023 to assess wildfire risks, employing advanced geospatial tools, thematic mapping, and machine learning models, including Multinomial Logistic Regression (MLR), Random Forest, and XGBoost. By segmenting the study area into 1 km² grid cells, microscale risk variations were captured, enabling classification into five categories: ‘Very Low’, ‘Low’, ‘Moderate’, ‘High’, and ‘Very High’. Results indicate that temperature anomalies, reduced fuel moisture, and anthropogenic factors such as waste burning and unregulated land-use changes significantly increase fire susceptibility. Predictive models achieved accuracies of 76.04% (MLR), 77.6% (Random Forest), and 76.5% (XGBoost), effectively identifying high-risk zones. The highest-risk areas were found in Izamba, Pasa, and San Fernando, where over 884.9 ha were burned between 2017 and 2023. The year 2020 recorded the most severe wildfire season (1500 ha burned), coinciding with extended droughts and COVID-19 lockdowns. Findings emphasize the urgent need for enhanced land-use regulations, improved firefighting infrastructure, and community-driven prevention strategies. This research provides a replicable framework for wildfire risk assessment, applicable to other Andean regions and beyond. By integrating data-driven methodologies with policy recommendations, this study contributes to evidence-based wildfire mitigation and resilience planning in climate-sensitive environments. Full article

The confluence of global warming, the urban heat island effect, and alterations in the nature of underlying surfaces has led to a continuous escalation in the frequency, scale, and intensity of fires within urban green spaces. Mitigating or eliminating the adverse effects of such fires on the service functions of urban ecosystems, while enhancing the resilience of urban greening systems in disaster prevention and risk reduction, has become a pivotal challenge in modern urban development and management. Academic focus has progressively broadened from isolated urban and forest domains to encompass the more intricate environments of the Wildland–Urban Interface (WUI) and urban–suburban forests, with a particular emphasis on the distinctive characteristics of urban greening and in-depth research. This study employs a combination of CiteSpace bibliometric analysis and a narrative literature review to comprehensively examine three critical aspects of urban fire safety as follows: (1) the evaluation of the fire-resistant performance of landscape plants in urban green spaces; (2) the mechanisms of fire behavior in urban greening systems; and (3) the assessment and prediction of urban fire risks. Our findings indicate that landscape plants play a crucial role in controlling the spread of fires in urban green spaces by providing physical barriers and inhibiting combustion processes, thereby mitigating fire propagation. However, the diversity and non-native characteristics of urban greenery species present challenges. The existing research lacks standardized experimental indicators and often focuses on single-dimensional analyses, leading to conclusions that are limited, inconsistent, or even contradictory. Furthermore, most current fire spread models are designed primarily for forests and wildland–urban interface (WUI) regions. Empirical and semi-empirical models dominate this field, yet future advancements will likely involve coupled models that integrate climate and environmental factors. Fire risk assessment and prediction represent a global research hotspot, with machine learning- and deep learning-based approaches increasingly gaining prominence. These advanced methods have demonstrated superior accuracy compared to traditional techniques in predicting urban fire risks. This synthesis aims to elucidate the current state, trends, and deficiencies within the existing research. Future research should explore methods for screening highly resistant landscape plants, with the goal of bolstering the ecological resilience of urban greening systems and providing theoretical underpinnings for the realization of sustainable urban environmental security. Full article

Wildfires pose severe threats to terrestrial ecosystems by causing loss of biodiversity, altering landscapes, compromising ecosystem services, and endangering human lives and infrastructure. Chile, with its diverse geography and climate, faces escalating wildfire frequency and intensity due to climate change. This study employs a spatial machine learning approach using a Random Forest algorithm to predict wildfire risk in Central and Southern Chile under current and future climatic scenarios. The model was trained on a time series dataset incorporating climatic, land use, and physiographic variables, with burned-area scars as the response variable. By applying this model to three projected climate scenarios, this study forecasts the spatial distribution of wildfire probabilities for multiple future periods. The model’s performance was high, achieving an Area Under the Curve (AUC) of 0.91 for testing and 0.87 for validation. The accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) values were 0.80, 0.87, and 0.73, respectively. Currently, the prediction of wildfire risk in Mediterranean-type climate areas and the central Araucanía are most at risk, particularly in agricultural zones and rural–urban interfaces. However, future projections indicate a southward expansion of wildfire risk, with an overall increase in probabilities as climate scenarios become more pessimistic. These findings offer a framework for policymakers, facilitating evidence-based strategies for adaptive land management and effective mitigation of wildfire risk. Full article