Search Results (242)

This study, based on multi-source data fusion and risk index models, has developed a comprehensive methodological system for evaluating the risk of forest fires caused by human factors. The system starts with four dimensions, i.e., exposure, hazard factors, vulnerability, and prevention and control capabilities, and constructs an evaluation framework with 19 secondary indicators. It also establishes single-category risk index models for four types of dominant fire sources: agricultural activities, religious ceremonies, tourism, and power distribution lines. Through weighted synthesis and exponential smoothing algorithms, it achieves daily dynamic risk forecasting. The research took the typical forest areas in the Mentougou, Changping, and Yanqing districts of Beijing as the application demonstration areas, collecting meteorological data, geographic information data, risk census ledgers, online hiking trajectories, and 2530 social survey questionnaires to complete the local parameter calibration and validation of the model. The retrospective analysis of 22 typical human-caused fire cases from 2018 to 2025 shows that the risk percentile of the ignition points in all cases was above 87.8%, indicating that the model has a good risk identification capability. Based on the evaluation results, differentiated control measures for different types of fire sources were proposed. The research results have been integrated into Beijing’s forest fire risk monitoring and early warning system, providing a scientific tool for the refined management of human-caused fire sources. Full article

This study aimed to develop and compare prognostic models (logistic regression, Cox proportional hazards, and random forest) to assess delayed mortality in pine (Pinus densiflora) following fires in Korea. Data from a 72-month monitoring of 734 trees across four fire areas were used, accounting for 19 variables: tree size, fire severity, multispectral indices, topography, and bioclimatic variables. Key predictors of mortality included diameter at breast height (DBH), bark scorch index (BSI), delta normalized burn ratio (dNBR), slope, topographic wetness index (TWI), precipitation of the warmest quarter, temperature seasonality and isothermality. The key results indicate that the random forest model was the most effective (AUC = 0.924; sensitivity = 0.892) in identifying trees at high risk of mortality. These results suggest that nonlinear approaches are effective for predicting delayed mortality in fire-damaged pines and can support rapid decision-making in post-fire forest management and restoration under increasing wildfire risk. Full article

Lightning-ignited wildfires are an increasing hazard in boreal forests, with their frequency amplified by global warming and more frequent thunderstorms. However, the mechanisms governing lightning-induced ignition and the subsequent smoldering–flaming transition remain poorly understood. This study aims to understand the ignition mechanisms of lightning-induced forest fires by combining a physics-based heat-balance model and controlled laboratory simulations. Experiments were conducted using twelve representative surface fuel types collected from six typical forest types in the Daxing’anling region, a lightning fire-prone area in northern China. Three fundamental stages of fire behavior development were systematically investigated, including the lightning-induced ignition, smoldering propagation, and the smoldering-to-flaming transition. Fuel moisture content was varied from 5% to 45%, and wind speed was adjusted between 0 and 5 m/s. The results demonstrated that discharge energy and wind speed significantly increased ignition probability, while fuel moisture content was negatively correlated with smoldering spread rate. Wind speed showed the greatest influence on the smoldering-to-flaming transition. The findings provide new mechanistic insights into the thermal and physical processes driving lightning-induced fires, supporting predictive modeling of ignition thresholds and fire behavior under changing meteorological and fuel conditions. Full article

In recent years, global climate change has significantly increased the incidences of grassland fires, shifting their occurrence from seasonal events (primarily spring and autumn) to annual incidents. To enable a more accurate evaluation and zoning of grassland fire risk, this study established the Fire Source Hazard Index, Fire Fuel Hazard Index, and Fire Environmental Hazard Index based on multi-source data, employing the entropy weight method, random forest modeling, mathematical statistics, and spatial analysis. A comprehensive seasonal grassland fire hazard assessment model was constructed using these three indices and seasonal fire hazard zones were evaluated in Inner Mongolia. The results indicated that, among the fire source factors, the hazard weight of foreign fire sources was relatively high during spring (0.37) and summer (0.44). In autumn and winter, the hazard weights of road networks were higher, at 0.38 and 0.44, respectively. In the comprehensive hazard assessment, the fire environment hazard exhibited an objective existence with notable seasonal variation, whereas the hazard weight of fire source factors exceeded that of fuels across all seasons. The comprehensive grassland fire hazard in Inner Mongolia demonstrated distinct seasonality and regional heterogeneity. Temporally, fire hazards are widespread and intense in spring, limited and concentrated in summer, extensive yet dispersed in autumn, and lowest in winter. Spatially, grassland fire hazards decreased from east to west, with higher hazards concentrated in the eastern regions. Western Inner Mongolia had the lowest probability of fire occurrence. The validation results revealed a positive correlation between the proportion of fire points and hazard grades, confirming the rationality of the hazard classification and the accuracy of the assessment, which provides an important theoretical basis for the scientific management and effective prevention and control of grassland fires. Future research should further refine and explore more precise methods for grassland fire hazard assessment. Full article

Conventional structural design frameworks assess natural hazards as statistically independent phenomena, a practice that can lead to significant underestimation of risk for structures subjected to sequential or concurrent hazards. The generation of probabilistic fragility functions under such cascading loads, particularly for post-fire seismic events, presents a computational barrier for standard non-linear dynamic analysis. To address this barrier, this study introduces a comprehensive computational framework centered on a physics-constrained neural network (PCNN) to serve as a high-fidelity surrogate model. The framework first uses a non-linear 12-degree-of-freedom structural model to generate a baseline dataset of collapse times under post-fire, concurrent wind-earthquake loading via the computationally efficient endurance time (ET) method, confirming that wind effects are negligible under ambient conditions and that the framework correctly identifies this hazard hierarchy without prior labeling, while fire and seismic parameters dominate. This dataset is subsequently used to train the PCNN, which is validated to achieve exceptional predictive accuracy ($R^2=$ 0.991), performing on par with a state-of-the-art Random Forest model while enforcing physical constraints. A feature importance analysis confirmed that structural collapse is dominated by fire intensity (≈55%) and initial structural period (≈45%). The validated PCNN is then applied to demonstrate the framework’s capability, rapidly generating fragility curves that quantify the catastrophic effect of fire on seismic resilience. This analysis reveals that a severe 800 °C localized fire reduces the structure’s median collapse capacity by 94.7%, thereby establishing the proposed framework as a successful template for tackling complex, non-linear problems in multi-hazard engineering. Full article

We analyzed wildfire hazard profiles across the Hyrcanian temperate forests of northern Iran (Guilan Province) by simulating a large set of wildfires with FlamMap MTT. We first derived geospatial data on terrain, fuel models, weather conditions, and historical wildfire occurrence (1992–2022) for the study area. We stratified fire weather conditions and fuel moisture based on the bioclimatic classification of the study area, considering observed extreme fire weather, as well as observed and random fire ignition locations for the simulations. The wildfire simulations were used to estimate burn probability (BP), conditional flame length (CFL), fire size (FS), and crown fire probability (CFP). BP ranged from 0 to 5.0 × 10⁻², with mean values of 1.3 × 10⁻³ and 1.1 × 10⁻³ for observed and random scenarios, respectively. The mean value of CFL from random ignition simulations (0.78 m) was substantially higher than that obtained in the observed ignition simulations (0.54 m), ranging from 0 to 6.75 m. We evidenced significant differences between observed and random ignition simulations for all wildfire hazard metrics. The highest wildfire hazard profiles were observed in the Cold-Mountainous bioclimatic zone under the random ignition simulations. On average, the annual number of anthropic structures threatened by wildfires ranged from 97 (observed scenario) to 123 (random scenario). This research provides detailed and spatially explicit fire hazard and exposure maps to inform fire modeling, land management, and policy actions. Full article

The Yeongnam wildfires in March 2025 destroyed over 40 temple halls across five Buddhist monasteries in South Korea, exposing a critical gap in wildfire management for mountain-sited cultural heritage: the existing approaches rely on static hazard maps and reactive suppression, lacking real-time terrain-aware prediction and proactive resource deployment. This study proposes a Digital Twin framework coupling high-resolution wildfire simulation with adaptive water-mist optimization to address this gap. Bogwangsa Temple (est. 949 CE, ~315 m elevation, Cheonmasan Mountain, Namyangju) serves as the case study, selected for its representative vulnerability—dense Pinus densiflora forests on steep western slopes forming a continuous fire corridor, limited vehicular access, and proximity to recent large-scale fire events. A modified Rothermel model on a 1 m cellular-automata grid, driven by a 1 m DEM, Korea Forest Service fuel data, and local weather records, simulates five scenarios from normal spring to extreme dry-wind conditions through Monte Carlo ensembles. Binary integer optimization selects the minimum-cost nozzle configuration, keeping the fire-arrival probability at four heritage structures below a safety threshold via pre-emptive activation. The adaptive deployment reduces the mean fire-arrival probability by approximately 80% compared with static sprinklers while substantially lowering water consumption. Sensitivity analyses confirm that 1 m DEM resolution captures micro-terrain features that are critical to accurate spread prediction that are lost at coarser resolutions. The modular, transferable framework contributes to SDG 11 (Sustainable Cities and Communities, Target 11.4) and SDG 13 (Climate Action). Full article

Wildfires are a major natural hazard causing extensive ecological damage and endangering human survival. Previous studies on wildfires in China have mostly focused on specific regions or individual drivers, with limited systematic assessments at the long-term and national scales. The spatiotemporal patterns of wildfires and their multiple driving mechanisms under China’s diverse climatic regimes remain insufficiently understood. To bridge this gap, we combined MCD64A1 burned area data (2001–2023) with multi-source natural (meteorological, vegetation, and topographic) and anthropogenic factors, using random forest models at both the national and regional scales to examine the spatiotemporal patterns, dominant drivers, and response mechanisms of wildfires in China. The results revealed that: (1) Spatially, wildfires were concentrated in northeastern and southern China, which accounted for 86.20% of the total burned area. Temporally, northern wildfires were primarily a spring-dominated fire regime, with peak activity in March and April, whereas southern wildfires were winter-dominated, peaking in February. (2) At the national scale, elevation was the key topographic factor influencing wildfire occurrence (relative importance = 0.49), with low-elevation and gentle-slope areas being more fire-prone. At the regional scale, the driving factors exhibit spatial differentiation, forming a spatial pattern of topography-dominated and climate-dominated. (3) Partial dependence plot analysis revealed nonlinear and threshold responses. Fire probability increases rapidly when the soil moisture is below 20 mm, while extremely high land surface temperatures in arid regions suppress fire occurrence due to fuel limitations. This study enhances the understanding of spatially heterogeneous wildfire drivers in China and provides a scientific basis for region-specific wildfire prevention and management strategies. Full article

Community land management plays an important role in wildfire-prone landscapes in Mediterranean Europe. However, in Portugal, information on the spatial extent and boundaries of community land remains fragmented across multiple institutions. This study addresses a critical but often overlooked issue in wildfire management: the fragmentation of institutional data on community land boundaries in mainland Portugal and its direct implications for forest fire risk management, planning, and accountability. We harmonized georeferenced datasets from various government and public institutions, applying multi-institutional spatial integration supported by legal land use criteria using the Land Use Land Cover map 2018 (LULC2018). The resulting national map represents the first fully harmonized spatial assessment of community land (baldios) in mainland Portugal. Our results show that baldios currently occupy approximately 595 thousand hectares, significantly exceeding official estimates. Of this total, around 74% are under partial forest regime law, and approximately 76% are classified as having a high or very high wildfire hazard. This means that three out of every four hectares of baldios in mainland Portugal are structurally susceptible to extreme wildfire conditions. Beyond improving cartographic data, the study’s findings demonstrate how the lack of land registry weakens the institutional foundations for community-based wildfire management. Without a functional, legally validated national map of community land boundaries, responsibilities, co-management mechanisms, and prevention measures remain spatially inconsistent. Full article

This study presents a comprehensive wildfire risk assessment framework for Rhodes Island, Greece, aimed at quantifying the impacts of climate change on hazard levels and vulnerability in a typical Mediterranean environment. The approach integrates Fire Weather Index (FWI) data, detailed fuel-type mapping, and multiple vulnerability indicators covering ecological, socioeconomic, and population factors, enabling spatially explicit estimates of current and future wildfire risk. Historically, Rhodes mostly faces moderate wildfire risk, mainly in central and northeastern regions, with localised areas of higher risk near settlements and key economic sites. Climate forecasts for 2025–2049 predict a notable increase in hazard, with areas experiencing extreme fire weather (FWI > 50) increasing from 15.19% to 66–72%, across all emission scenarios. Ecological vulnerability is particularly alarming, as 93% of the island is already highly susceptible; fire-prone forest and agricultural zones are expected to move into the highest ecological risk categories, especially in the central mountain areas. The devastating 2023 wildfire, which burned over 17,600 hectares, caused more than €5.8 million in direct damages and led to the largest evacuation in the island’s history, closely aligning with high-risk zones modelled in the framework. An important insight is the limited spatial variation in near-future risk between RCP 4.5 and RCP 8.5, indicating that significant wildfire intensification is largely unavoidable by mid-century, emphasising the urgent need for quick adaptation and risk mitigation efforts for Mediterranean critical infrastructure and communities. Full article

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article

Wildfire escalation is increasingly threatening ecosystems and communities in Khyber Pakhtunkhwa (KP), Pakistan, particularly in forest and rangeland landscapes where ecological flammability interacts with human activity. While environmental and climatic drivers are well studied, governance factors remain underexplored despite their decisive role in shaping how ecological risk translates into disasters. Regional forests show considerable ecological diversity, including chir pine-dominated stands, mixed temperate conifer forests, broadleaved oak-associated systems, and shrub rangeland mosaics, each differing in fuel structure and fire behavior. Dependence on fuelwood collection, grazing, and forest access further influences ignition probability and fire spread. This study examines how governance failures influence wildfire risk and severity through a Governance-Fire Risk Framework. Governance is treated as a determining institutional condition affecting prevention capacity, regulation of hazardous land use, fuel management, and emergency response effectiveness. A cross-sectional survey of 540 stakeholders from rural (Dir Lower, Dir Upper) and peri-urban districts (Swat, Mansehra, Abbottabad) was analyzed using SPSS (version 26) and AMOS (version 24) (CFA and SEM). Governance failure significantly escalates wildfire risk through delayed emergency response, regulatory non-compliance, political interference, and weak institutional coordination. Institutional preparedness and response capacity reduce risks, whereas corruption intensifies them. Corruption functions through illegal land conversion, diversion of fire management resources, procurement irregularities, nepotistic staffing, and selective enforcement, increasing ignition sources, fuel accumulation, and response delays. Rural districts show stronger governance-fire linkages. Wildfire escalation in KP is governance-driven in interaction with ecological conditions and community dependence on forest resources. Effective mitigation requires anti-corruption measures, rapid response systems, stronger enforcement, and improved preparedness. The study offers a transferable governance-focused framework for wildfire management in fire-prone developing regions. Full article

Wildfires are one of the main natural hazards around the world, and are becoming increasingly important in the current context of climate change. To limit the impacts of fires, policies are implemented following various phases of risk management. These concern prevention (risk communication and information, forest monitoring, fuel management, the installation of firewalls, etc.) and suppression (firefighting interventions) measures. This article presents a systematic literature review analyzed through the prism of climate change and policy. It is carried out using a textometric approach. The corpus is composed of 720 articles published from 1997. A marked increase is evident from 2021. The analysis enables the clustering of the main issues. Six main themes were revealed by Reinert Clustering: Health issues, Disaster risk management, Natural environment, Management of the natural environment, Fire characteristics, and Fire modeling. These themes are composed of 36 sub-themes. In addition, the article shows that some issues (anthropogenic health and management/governance issues, and natural environment issues around fire and natural environment characterization) remain constant over time while others increase/decrease in importance (air quality, carbon storage and CO₂ emissions, ecosystems and biodiversity, and the effects of fires on the natural environment at the expense of anthropogenic issues). Full article

A portable and integrated monitoring device was developed to digitally assess both water levels and surrounding fire-related conditions in forest firefighting water buckets using multi-sensor fusion. The system integrates a hydrostatic liquid-level sensor with temperature–humidity and smoke sensors. Validation was performed through field-oriented experiments conducted under semi-controlled conditions. Water-level measurements were collected over a three-month period under simulated forest conditions and benchmarked against conventional steel-ruler readings. Early-stage fire monitoring experiments were carried out using dry wood and leaf litter under varying wind speeds, wind directions, and representative extreme weather conditions. The device achieved a mean water-level bias of −0.60%, a root-mean-square error of 0.64%, and an overall accuracy of 99.36%. Fire monitoring reached a maximum detection distance of 7.30 m under calm conditions and extended to 16.50 m under strong downwind conditions, with performance decreasing toward crosswind directions. Stable operation was observed during periods of strong winds associated with typhoon events, as well as prolonged high-temperature exposure. The primary novelty of this work lies in the conceptualization of a Collaborative Forest Resource–Hazard Monitoring Architecture. Unlike traditional isolated sensors, our proposed framework utilizes a dual-domain decision-making model that simultaneously assesses water-bucket storage stability and micro-scale fire threats. By implementing a robust ‘sensing–logic–alert’ framework tailored for rugged environments, this study offers a new methodological reference for the intelligent management of forest firefighting resources. Full article

Wildfires are among the most frequent and destructive natural hazards in Europe, particularly in Portugal. They have severe impacts on forests, ecosystems, human health, and infrastructure, leading to substantial socio-economic losses due to firefighting efforts and post-fire recovery costs. Moreover, wildfires cause numerous casualties each year, highlighting the need for a deeper understanding of fire behaviour to support effective firefighting strategies and ensure the safety of both responders and communities. This study examines the influence of wind flow velocity variation on fire behaviour, both in the presence and absence of an edge wall in the fuel bed, aiming to replicate the characteristics of real wildfire fronts at a laboratory scale. Experimental tests were conducted at the Forest Fire Research Laboratory (LEIF) of the University of Coimbra using a shrub mixture, composed of Ulex europaeus, Baccharis trimera, and Caralluma adscendens, representing one of the most common fine fuels in Portuguese forested landscapes. This research provides novel insights by experimentally analyzing the combined effect of wind velocity variation and fuel bed edge presence on fire behaviour, paving the way for future comparisons with numerical simulations and real wildfire fronts. As expected, increasing wind velocity and the presence of fuel bed edges resulted in higher values of rate of spread, fireline intensity, and fire intensity. Full article