Search Results (100)

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article

Wildfires are increasing in frequency and severity, with Australia’s 2019–2020 Black Summer burning over 18 million hectares. Accurate prediction of wildfire behavior is essential for effective risk assessment and emergency response. This study presents a machine learning framework for predicting wildfire dynamics across Australia’s seven regions using the IBM wildfire dataset. Various Machine Learning (ML) models were evaluated to forecast three key indicators: Fire Area (km²), Fire Brightness Temperature (K), and Fire Radiative Power (MW). Lasso Regression consistently outperformed the other models, achieving an average RMSE of 0.04201 and R² of 0.29355. Performance varied across regions, with stronger results in areas like New South Wales and Queensland, likely influenced by differences in topography, microclimate, and vegetation. However, limitations include the exclusion of ignition sources such as lightning and human activity, which are critical for capturing the environment accurately and improving predictive accuracy. Future work will integrate these factors alongside more detailed weather and vegetation data. Practical implementation may face challenges related to real-time data availability, system integration, and response coordination, but this approach offers promising potential for operational wildfire decision support. Full article

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent heterogeneous fuels and simulate fire behavior processes with greater detail than conventional models. However, they require accurate information about species composition and 3D distribution of fuel mass and bulk density at the voxel level. Working in a Mediterranean ecosystem study area we developed a methodology based on the use of geometric and spectral features from UAS-based digital aerial photogrammetric point clouds for (i) species segmentation and classification using machine learning algorithms, (ii) generation of biomass prediction models at individual plant level, and (iii) creation of 3D fuel scenarios and modeling wildfire behavior. Field measurements were conducted on 22 circular plots with a radius of 5 m. Data from the field measurements, combined with species-specific allometric equations, were used for the evaluation of classification and prediction models. Fire behavior variables such as rate of spread, heat release rate, and mass loss rate were monitored and assessed as outputs from 20 different scenarios using FDS. The overall species classification accuracy was 80.3%, and the biomass regression R² values obtained by cross-validation were 0.77 for Pinus halepensis and 0.83 for Anthyllis cytisoides. These results are encouraging further improvement based on the integration of sensors onboard UAS, and the characterization of fuels for fire behavior modeling. These high-resolution fuel representations can be coupled with standard risk assessment tools, enabling fire managers to prioritize treatment areas and plan for resource deployment. Full article

Wildfire regimes are closely linked to changes in landscape structure, yet the influence of accelerated land use transitions on fire activity remains poorly understood, particularly in rapidly transforming regions like central Chile. Although land use change has been extensively documented in the country, the specific role of the speed, extent, and spatial configuration of these transitions in shaping fire dynamics requires further investigation. To address this gap, we examined how landscape transitions influence fire frequency in central Chile, a region experiencing rapid land use change and heightened fire activity. Using multi-temporal remote sensing data, we quantified land use transitions, calculated landscape metrics to describe their spatial characteristics, and applied intensity analysis to assess their relationship with fire frequency changes. Our results show that accelerated landscape transitions significantly increased fire frequency, particularly in areas affected by forest plantation rotations, new forest establishment, and urban expansion, with changes exceeding uniform intensity expectations. Regional variations were evident: In the more densely populated northern areas, increased fire frequency was primarily linked to urban development and deforestation, while in the more rural southern regions, forest plantation cycles played a dominant role. Areas with a high number of large forest patches were especially prone to fire frequency increases. These findings demonstrate that both the speed and spatial configuration of landscape transitions are critical drivers of wildfire activity. By identifying the specific land use changes and landscape characteristics that amplify fire risks, this study provides valuable knowledge to inform fire risk reduction, landscape management, and urban planning in Chile and other fire-prone regions undergoing rapid transformation. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Early evacuation during bushfires remains the safest strategy; however, in many realistic scenarios, timely evacuation is challenging, making safe sheltering a last-resort option to reduce risk compared to late evacuation attempts. However, most Australian homes in bushfire-prone areas are neither designed nor retrofitted to provide adequate protection against extreme bushfires, raising safety concerns. This study addresses this gap by investigating the concept of retrofitting a part of the residential buildings as attached safe rooms for sheltering and protection of valuables, providing a potential last-resort solution for bushfire-prone communities. Numerical simulations were conducted using the Fire Dynamics Simulator to assess heat transfer and internal temperature conditions in a representative residential building under bushfire exposure conditions. The study investigated the impact of the placement of the safe room relative to the fire front direction, failure of vulnerable building components, and the effectiveness of steel shutters in response to internal temperatures. The results showed that the strategic placement of safe rooms inside the building, along with adequate protective measures for windows, can substantially reduce internal temperatures. The findings emphasised the importance of maintaining the integrity of openings and the external building envelope, demonstrating the potential of retrofitted attached safe rooms as a last-resort solution for existing residential buildings in bushfire-prone areas where the entire building was not constructed to withstand bushfire conditions. Full article

The modern Chinese architectural heritage combines sturdy Western materials with delicate Chinese styling, mainly adopting brick-timber structural systems that are highly vulnerable to fire damage. The study assesses the fire spread characteristics of the First Faculty House, a 20th-century architectural heritage located at a university in China. The assessment is carried out by analyzing building materials, structural configuration, and fire load. By using FDS (Fire Dynamics Simulator (PyroSim version 2022)) and SketchUp software (version 2023) for architectural reconstruction and fire spread simulation, explores preventive measures to reduce fire risks. The result show that the total fire load of the building amounts to 1,976,246 MJ. After ignition, flashover occurs at 700 s, accompanied by a sharp increase in the heat release rate (HRR). The peak ceiling temperature reaches 750 °C. The roof trusses have critical structural weaknesses when approaching flashover conditions, indicating a high potential for collapse. Three targeted fire protection strategies are proposed in line with the heritage conservation principle of minimal visual and functional intervention: fire sprinkler systems, fire retardant coating, and fire barrier. Simulations of different strategies demonstrate their effectiveness in mitigating fire spread in elongated architectural heritages with enclosed ceiling-level ignition points. The efficacy hierarchy follows: fire sprinkler system > fire retardant coating > fire barrier. Additionally, because of chimney effect, for fire sources located above the ceiling and other hidden locations need to be warned in a timely manner to prevent the thermal plume from invading other sides of the ceiling through the access hole. This research can serve as a reference framework for other Modern Chinese Architectural Heritage to develop appropriate fire mitigation strategies and to provide a methodology for sustainable development of the Chinese architectural heritage. Full article

Fire spread between buildings is the primary cause of extensive fire damage in traditional village timber structure clusters. Accurately assessing fire spread risk is crucial for the preservation of these architectural ensembles. During the development and conservation of traditional villages, fire risk dynamics may shift due to fire-resistant retrofits or layout modifications, necessitating repeated risk reevaluations. To address challenges such as the computational intensity of fire spread simulations, high costs, and data acquisition difficulties, this study proposes a directed graph-based method for fire spread risk analysis and risk level classification in timber structure clusters, accounting for their unique fire propagation characteristics. First, localized fire spread paths and propagation times between nodes (buildings) are determined through fire spread simulations, constructing an adjacency matrix for the directed graph of the building cluster. Path search algorithms then identify the spread range and velocity under specific fire scenarios. Subsequently, a zoned risk assessment model for individual buildings is developed based on critical fire spread loss and velocity, integrating each building’s fire resistance and its probability of exposure to different risk zones to determine the overall cluster’s fire spread risk level. The method is validated using a case study of a typical village in Yunnan Province. Results demonstrate that the approach efficiently computes fire spread characteristics across different scenarios and quantitatively evaluates risk levels, enabling targeted fire safety interventions based on village-specific spread patterns. Case analysis reveals significant variations in fire spread behavior: Village 1, Village 2, and Village 3 exhibit fire resistance indices of 0.59, 0.757, and 0.493, corresponding to high, moderate, and high fire spread risk levels, respectively. Full article

Assessing building fire safety risks during the early design phase is vital for developing practical solutions to minimize loss of life and property. This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. This study evaluates machine learning applications in fire safety using a comprehensive approach that combines bibliometric and content analysis methods. For this purpose, as a result of the scan without any year limitation from the Web of Science Core Collection-Citation database, 250 publications, the first of which was published in 2001, and the number has increased since 2019, were reached, and sample analysis was performed. In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). Thus, the statistical impact of factors influencing the formation of the normalized citation count was measured, and the validity of the approach used was tested. The research categories included evacuation and emergency management, fire detection, and early warning systems, fire dynamics and spread prediction, fire load, and material risk analysis, intelligent systems and cyber security, fire prediction, and risk assessment. Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. As a result, detailed literature was presented to define the academic publication profile of the research area, determine research fronts, detect emerging trends, and reveal sub-themes. Full article

Electrical cabinet fire scenarios constitute a significant risk within nuclear facilities, emphasizing the need to mitigate uncertainties in risk evaluations. Owing to the disparate nature of electrical cabinet parameters, only a few factors have been experimentally explored and statistically analyzed to assess their impact on peak HRR. In this study, we conducted both a cabinet parameter study and a combustible configuration study to systematically evaluate their influence on peak HRR and time-to-peak HRR. A series of 51 simulation matrices were created using statistical experiment design (SED) and ANOVA to quantify the influence of cabinet volume, combustible surface area, vent area, ignition characteristics, and burning behavior (e.g., HRRPUA and duration). A computational fluid dynamics (CFD) model, specifically a Fire Dynamics Simulator (FDS), was used to model the ignition source and flame spread inside of the electrical cabinet that influence peak HRR. The most impactful parameters influencing peak HRR and time-to-peak HRR were identified. The findings revealed that the configuration of combustibles and the placement of the ignition source play a pivotal role in determining the peak HRR. A partition screening analysis was conducted to identify the conditions under which the ventilation area becomes a more significant parameter. Additionally, a comparison between experimental results and numerical simulations demonstrated good agreement, further validating the predictive capability of the model. Full article

Fire regime (FR) is a key element in the study of ecosystem dynamics, supporting natural resource management planning by identifying gaps in fire patterns in time and space and planning to assess ecological conditions. Due to the insufficient consideration of integrated characterization factors, especially the insufficient research on fire season types (FST), the current understanding of the spatial heterogeneity of fire patterns in China is still limited, and it is necessary to use FST as a key dimension to classify FR zones more accurately. This study extracted 13 fire characteristic variables based on Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data (MCD64A1), active fire data (MODIS Collection 6), and land cover data (MCD12Q1) from 2001 to 2023. The study systematically analyzed the frequency, intensity, spatial distribution and seasonal characteristics of fires across China. By using data normalization and the k-means clustering algorithm, the study area was divided into five types of FR zones (FR 1–5) with significant differences. The burned areas of the five FR zones account for 67.76%, 13.88%, 4.87%, 12.94%, and 0.55% of the total burned area across the country over the 23-year study period, respectively. Among them, fires in the Northeast China Plain and North China Plain cropland areas (FR 1) exhibit a bimodal distribution, with the peak period concentrated in April and June, respectively; the southern forest and savanna region (FR 2) is dominated by high-frequency, small-scale, unimodal fires, peaking in February; the central grassland region (FR 3) experiences high-intensity, low-frequency fires, with a peak in April; the east central forest region (FR 4) is characterized by low-frequency, high-intensity fires; and the western grassland region (FR 5) experiences low-frequency fires with significant inter-annual fluctuations. Among the five zones, FST consistently ranks within the top five contributors, with contribution rates of 0.39, 0.31, 0.44, 0.27, and 0.55, respectively, confirming that the inclusion of FST is a reasonable and necessary choice when constructing FR zones. By integrating multi-source remote sensing data, this study has established a novel FR classification system that encompasses fire frequency, intensity, and particularly FST. This approach transcends the traditional single-factor classification, demonstrating that seasonal characteristics are indispensable for accurately delineating fire conditions. The resultant zoning system effectively overcomes the limitations of traditional methods, providing a scientific basis for localized fire risk warning and differentiated prevention and control strategies. Full article

Wildfires represent an increasing global concern, threatening ecosystems, human settlements, and economies. Chile, characterized by diverse climatic zones and extensive forested areas, has been particularly vulnerable to wildfire events over recent decades. In this context, real, long-term data are essential to understand wildfire dynamics and to design effective early warning and prevention systems. This paper introduces a unique dataset containing detailed wildfire occurrence and damage information across Chilean municipalities from 1985 to 2024. Derived from official records by the National Forestry Corporation of Chile CONAF, this dataset encompasses key variables such as the number of fires, total burned area, estimated material damages, and the number of affected individuals. It provides an invaluable resource for researchers and policymakers aiming to improve fire risk assessments, model fire behavior, and develop AI-driven early detection systems. The temporal span of nearly four decades offers opportunities for longitudinal analyses, the study of climate change impacts on fire regimes, and the evaluation of historical prevention strategies. Furthermore, by presenting a complete spatial coverage at the municipal level, it allows fine-grained assessments of regional vulnerabilities and resilience. Full article

The growing complexity and risk profile of fire and emergency incidents necessitate advanced training methodologies that go beyond traditional approaches. Live-fire drills and classroom-based instruction, while foundational, often fall short in providing safe, repeatable, and scalable training environments that accurately reflect the dynamic nature of real-world emergencies. Recent advancements in immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and simulation-based systems, offer promising alternatives to address these challenges. This review provides a comprehensive overview of the integration of VR, AR, MR, XR, and simulation technologies into firefighter and incident commander training. It examines current practices across fire services and emergency response agencies, highlighting the capabilities of immersive and interactive platforms to enhance operational readiness, decision-making, situational awareness, and team coordination. This paper analyzes the benefits of these technologies, such as increased safety, cost-efficiency, data-driven performance assessment, and personalized learning pathways, while also identifying persistent challenges, including technological limitations, realism gaps, and cultural barriers to adoption. Emerging trends, such as AI-enhanced scenario generation, biometric feedback integration, and cloud-based collaborative environments, are discussed as future directions that may further revolutionize fire service education. This review aims to support researchers, training developers, and emergency service stakeholders in understanding the evolving landscape of digital training solutions, with the goal of fostering more resilient, adaptive, and effective emergency response systems. Full article

(1) Stand-replacing fires may threaten the continued stability of mixed conifer forests in the U.S. Southwest. Increasing fire frequency and severity have made post-fire forest recovery trajectories uncertain for many coniferous species, potentially leading to long-term shifts in forest structure and composition. (2) The purpose of this study was to examine post-fire stand dynamics over a 10-year period, using a network of permanent plots established prior to wildfire events across Arizona and New Mexico. We assessed changes in overstory composition, regeneration, and fuel loading across different fire severities. (3) High severity fire caused near-total overstory mortality, with little to no conifer regeneration and abundant sprouting hardwood regeneration. Lower severity fire was more favorable to fire-tolerant conifer species; however, mortality among mature trees was high, and fire-intolerant conifers were either diminished or extirpated completely. (4) In high severity fires, changes in overstory and understory structure and composition may be long-lasting. Additionally, increased fuel loads following high severity fire suggests a heightened risk of reburns, potentially perpetuating ecotype conversion. Our findings highlight the need for active management strategies, including reforestation and fuel reduction treatments, to support forest resilience for mixed conifer ecosystems in the US Southwest and similar forest types in other regions in the face of ongoing climate and fire regime changes. Full article

The enclosed nature of indoor building spaces during fires creates complex fire environments and restricted evacuation routes, substantially elevating the risk of mass casualties. Traditional static evacuation routes not only overlook the complexity of fire scenarios but also fail to satisfy safety requirements for evacuation. To address this issue, this study proposes an enhanced A* algorithm to determine evacuation paths based on dynamic fire risk assessment. A dynamic fire risk assessment model is established using key fire environment parameters (e.g., temperature, visibility, and toxic gas concentration) and their corresponding personnel harm thresholds. This model quantifies fire risks within a discrete space. The A* algorithm is improved by integrating fire risk values and initial direction constraints into its heuristic function and path update strategy, thereby increasing the algorithm’s accuracy and efficiency. Using a subway station fire as a case study, the simulation results indicate that the improved algorithm can update evacuation paths in line with the dynamic evolution of fire risks. It also identifies evacuation routes by balancing fire risk, distance, and initial direction. This approach maintains the original path direction while substantially reducing path risk, achieving an approximate 70% reduction in individual evacuation path risk. This method can guide building fire safety design and the formulation of emergency evacuation plans. It also serves as a reference for path guidance during emergencies. Full article