Search Results (66)

In January 2025, the Palisades and Eaton fires overtook large swaths of Los Angeles County, covering a combined area of approximately 152 km², composed of diverse coastal, urban, and forested environments. Aerial Light Detection and Ranging (LiDAR) surveys were commissioned directly following these fires, and compared against previously unreleased foundational LiDAR surveys captured in 2023 and 2024. The timeliness of these surveys presents a unique opportunity to approach large-scale damage characterization metrologically at sub-meter resolution. Cell-based height differencing across 367 million change-detected cells (on a 0.5 m grid) identifies 49.5 km² of vegetation loss and 1.66 km² of building footprint destruction in the Palisades fire, and 24.3 km² of vegetation loss and 1.48 km² of building footprint destruction in the Eaton fire. From the resulting volumetric loss inventory, we derive bottom-up carbon emission estimates of $255\pm 61$ kt C for the Palisades fire and $162\pm 38$ kt C for the Eaton fire. The Eaton estimate agrees to within 6% of an independent atmospheric inversion estimate derived from ground-based sensor networks, well within the propagated uncertainty of either method, providing an independent cross-validation, at the total-emission level, between LiDAR-based and atmospheric-inversion approaches to wildland–urban interface fire emissions. This paper details the segmentation and characterization methodology, and coincides with ALERTCalifornia’s public release of all described raw and derivative datasets. 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

This study presents a novel integrated methodology for optimizing forest fire emergency rescue vehicle scheduling through the synergistic combination of a multi-criteria demand urgency grading framework and mechanistic fire spread propagation modeling, enhancing spatiotemporal resource allocation efficiency under evolving wildfire scenarios. The research focuses on three core aspects: First, a multi-dimensional demand urgency evaluation system is established, incorporating fire threat, response efficiency, and path factors. Subjective and objective weights are determined through fuzzy analytic hierarchy process and entropy method, respectively, while grey relational analysis TOPSIS method is employed for prioritizing affected areas. The model’s validity is verified using wildfire data from the Greater Khingan Mountains. Second, a multi-objective vehicle scheduling model is developed, combining total rescue time, cost, and urgency ranking index via weighted sum method. A fire spread model is innovatively introduced to dynamically adjust urgency classification, with genetic algorithm (GA) and Genetic Simulated Annealing Algorithm (GASA) designed for solution optimization. Finally, empirical analysis of 13 fire cases in the Greater Khingan Mountains (2020) demonstrates that GASA outperforms GA, achieving 17% reduction in rescue time, 1% cost savings, 22% shorter travel distance, and 0.7% improvement in urgency ranking. Incorporating the fire spread model enhances the urgency ranking index by 10.78%, where the improvement is defined as the percentage increase in the achieved objective function value f3 compared to the solution obtained without dynamic fire propagation information. By integrating dynamic urgency assessment with intelligent algorithms, this research constructs a spatiotemporal-aware emergency scheduling framework aligned with forest fire evolution patterns, providing theoretical foundations and practical strategies to enhance rescue efficiency and resource allocation, with significant implications for disaster management. Full article

We revisit a hyperbolic wildfire model based on reaction–diffusion dynamics with relaxation effects and extend it by incorporating an advection transport term that accounts for wind-driven fire spread. After a planar two-dimensional reformulation and non-dimensionalization of the model, the analysis is restricted to the minimal ignition regime characterized by the presence of a logistic reaction term governing the evolution of the fire-affected tree fraction. The focus of the study is to assess the influence of the effective wind velocity on the propagation dynamics of the fire-affected tree fraction. For this purpose, analytical solutions of the extended wildfire model are derived by applying the Simple Equations Method (SEsM) in its (1,1) variant using a Riccati-type ordinary differential equation as a simple equation. The obtained families of exact solutions describe physically relevant transition fronts connecting fire-unaffected and fully fire-affected states, or vice versa. Numerical simulations of the derived analytical solutions are performed to demonstrate how the internal front thickness and the profile morphology depend on the specific variant of the Riccati-type solution and on the magnitude of the effective wind velocity. A phase-plane stability and bifurcation analysis of the reduced traveling wave system is carried out. Hopf bifurcation thresholds with respect to the effective wind velocity parameter are identified, revealing transitions between monotone front propagation and oscillatory regimes. A regime map is constructed in the parameter plane spanned by the effective wind velocity and the traveling wave speed. This regime diagram delineates regions of qualitatively different propagation behavior, including monotone advancing fronts, possible oscillatory regimes, and regimes in which traveling wave fronts cease to exist. Full article

Juniper formations are valuable habitats for fauna and flora and play an important role in protecting the ecosystem, where they grow, from erosion and degradation. Juniper habitats are included in the European Directive 92/43. Juniperus phoenicea is of great ecological importance in Mediterranean areas, as it is often among the only species that can survive under extremely unfavorable conditions. Along with other species, it forms the habitat 2250* “Coastal dunes with Juniperus spp.” Habitat 2250* is a priority habitat, and today, it is under threat due to several factors such as coastal erosion, forest fires, etc. Therefore, the main objective of this study is to investigate the factors that affect the rooting of J. phoenicea shoot cuttings collected from plants growing in their natural habitat. Specifically, the effects of the cutting collection season and the different concentrations (0, 3, 6, and 12 g·L⁻¹) of the plant growth regulator K-IBA (indole-3-butyric acid potassium salt) on the rooting of J. phoenicea shoot cuttings in two propagation systems (mist and fog) were investigated. The shoot cuttings of J. phoenicea rooted in high percentages reaching more than 90%. The factors studied played an important role, and significant differences in the rooting ability of cuttings were found, as well as in the number and length of roots. For the optimal results, cuttings should be collected in winter and treated with 6 g·L⁻¹ K-IBA under a mist system or in summer with 3 g·L⁻¹ K-IBA under a fog system. The results of the present study can be used to make up a basic step for conservation and restoration efforts and for sustainable exploitation strategies for this valuable phytogenetic resource. Full article

The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and socio-economic impacts, propagation dynamics, symmetrical or asymmetrical patterns, and even their duration. Such predictive capabilities are of critical importance for effective wildfire management, as they inform the strategic allocation of material resources, and the optimal deployment of human personnel in the field. Beyond that, examination of symmetrical or asymmetrical patterns in fires helps us to understand the causes and dynamics of their spread. The necessity of leveraging machine learning tools has become imperative in our era, as climate change has disrupted traditional wildfire management models due to prolonged droughts, rising temperatures, asymmetrical patterns, and the increasing frequency of extreme weather events. For this reason, our research seeks to fully exploit the potential of Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Grammatical Evolution, both for constructing Artificial Features and for generating Neural Network Architectures. For this purpose, we utilized the highly detailed and publicly available symmetrical datasets provided by the Hellenic Fire Service for the years 2014–2021, which we further enriched with meteorological data, corresponding to the prevailing conditions at both the onset and the suppression of each wildfire event. The research concluded that the Feature Construction technique, using Grammatical Evolution, combines both symmetrical and asymmetrical conditions, and that weather phenomena may provide and outperform other methods in terms of stability and accuracy. Therefore, the asymmetric phenomenon in our research is defined as the unpredictable outcome of climate change (meteorological data) which prolongs the duration of forest fires over time. Specifically, in the model accuracy of wildfire duration using Feature Construction, the mean error was 8.25%, indicating an overall accuracy of 91.75%. Full article

Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The purpose of this study is to investigate whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation within those buffer zones. The methodology used in this study was the development and evaluation of a complete system, which uses AI to detect the contours of buildings and the services provided by the CHAMELEON bundles to detect trees and vegetation within buffer zones. The developed AI model is effective at detecting the building contours, with a mAP50 of 0.888. The article analyses the results obtained from two use cases: a road surrounded by dense forest and an isolated building with dense vegetation nearby. The main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to manage biomass within buffer zones, thus helping to reduce the risk of wildfire propagation in wildland–urban interfaces. 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

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

Woody debris (WD) is an important element in forest ecosystems. It provides critical habitats for plants, animals, and insects. It is also a source of fuel contributing to fire propagation and sometimes leads to catastrophic wildfires. WD inventory is usually conducted through field surveys using transects and sample plots. Light Detection and Ranging (LiDAR) point clouds are emerging as a valuable source for the development of comprehensive WD detection strategies. Results from previous LiDAR-based WD detection approaches are promising. However, there is no general strategy for handling point clouds acquired by different platforms with varying characteristics such as the pulse repetition rate and sensor-to-object distance in natural forests. This research proposes a general and adaptive morphological WD detection strategy that requires only a few intuitive thresholds, making it suitable for multi-platform LiDAR datasets in both plantation and natural forests. The conceptual basis of the strategy is that WD LiDAR points exhibit non-planar characteristics and a distinct intensity and comprise clusters that exceed a minimum size. The developed strategy was tested using leaf-off point clouds acquired by Geiger-mode airborne, uncrewed aerial vehicle (UAV), and backpack LiDAR systems. The results show that using the intensity data did not provide a noticeable improvement in the WD detection results. Quantitatively, the approach achieved an average recall of 0.83, indicating a low rate of omission errors. Datasets with a higher point density (i.e., from UAV and backpack LiDAR) showed better performance. As for the precision evaluation metric, it ranged from 0.40 to 0.85. The precision depends on commission errors introduced by bushes and undergrowth. Full article

This study addresses the task of ecologically assessing the consequences of natural fires. Statistical data are presented on the carbon dioxide emissions in millions of tons and analytical data on the locations of peat fires, as well as modern methods of detection and control of peat and forest fires, divided into groups. An analysis of the works of leading Russian and international scientists and research organizations engaged in the search for methods of peat fire forecasting is also presented. Our aim was to develop a more effective method of preventing peat soil ignition by changing its physical and moisture characteristics. To that end, peat samples were selected in the Tver region. The laboratory equipment and the methodology of our experimental studies are described in detail, in which we simulated the natural climatic conditions in the center of the Russian Federation. This study provides a mathematical description of the process of spontaneous ignition, which occurs according to the following steps: a heat flow heats the surface to the ignition temperature, creating a self-heating zone; eventually, a wave of ignition (smoldering) capable of self-propagation is formed. We experimentally determined the spontaneous thermal ignition conditions in our experimental studies of the fire hazards of selected peat samples, where the test material was loaded in a cylindrical container made of brass net with a 0.8 mm mesh, of the dimensions 30 × 30 mm. Thermocouple elements were placed inside the container, fixing the temperature of the surface and the center of the sample, where the smoldering or ignition zone of the test material formed. We analyzed the results of our experimental studies on peat samples’ self-heating chemical reaction, leading us to draw conclusions about the possibility of fires on peat soil depending on its physical and chemical characteristics. We also offer recommendations that will improve peat soils’ fire safety, permitting agricultural crop production without a peat fire risk. Full article

The increasing frequency and intensity of wildfires are affecting the use of wood products in rural areas as well as at the wildland–urban interface. The enhancement in wood products’ reaction/resistance to fire is a concern often raised by national authorities. In the present study, different fire protection measures were applied to utility wood poles aiming to protect them from wildfires, ensuring their reuse in safe conditions while preventing them from contributing to the propagation of forest fires, particularly surface fires. Two of the solutions tested were based on intumescent paints, while the other one involved a system that completely covers the poles’ exteriors (a fabric-protection layer mechanically applied to the surface of the pole). These solutions were initially assessed in small-scale laboratory tests. Following these initial tests, a selected solution based on fabric protection was tested under simulated wildland fire conditions. The results obtained showed that fabric-based protection delivered satisfactory results, being easily applied on site, allowing the protection of poles already in service and the replacement of fire protection devices after a fire occurs. Full article

The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law’s texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm’s effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity. Full article

Air-cooling technology has been widely used for its water-saving advantage, and the performance of air-cooled condensers (ACC) has an important impact on the operation status of the unit. In this paper, the performance of ACC in a typical coal-fired power plant is optimized by using machine learning (ML) algorithms. Based on the real operation data of the unit, this paper establishes a back pressure optimization model by using back propagation neural network (BPNN), random forest (RF), and genetic algorithm back propagation (GA-BP) methods, respectively, and conducts a comparative analysis of performance optimization and power-saving effect of the three algorithms. The results show that three algorithms offer significant power savings in the low-load section and smaller power savings in the high-load section. Moreover, when the ambient temperature is lower than 10 °C, the power-saving effect of the three algorithms after optimization is not much different; when the ambient temperature is greater than 10 °C, the power-saving effect of the performance optimization of BPNN and RF is significantly better than that of GA-BP. The optimization method has a good effect on improving the performance of ACC. Full article

The study explores two forest fires in extreme southern Portugal aiming to increase the knowledge of how atmospheric circulation influenced the fire dynamics in each event. The meteorological conditions were simulated by the Meso-NH full-physics non-hydrostatic limited-area research model. The two numerical simulations were performed using a two-way nested domain configuration with horizontal resolutions of 2500 m and 500 m. In both cases, the large-scale atmospheric environment was marked by the Azores Anticyclone west of Portugal which induced northerly winds over the coastal of Mainland Portugal. The analysis of Tavira’s fire (18–21 July 2012, 24,800 ha of total burned area) revealed flow characteristics resembling a low-level jet located below 1 km, with stronger winds during the second day resulting in enhanced fire spread rates. The second case study (Aljezur, 19–21 June 2020; 2302 ha of burned area) highlights a fire occurring under atypical fire weather conditions, namely lower air temperature and higher relative humidity; however, orographic effects produced downslope winds favouring fire propagation. This study provides a better understanding of the fire critical conditions in extreme South Portugal and investigates the atmosphere–orography interactions in the region that played an important role in the development of these two forest fires. Increasing knowledge about large fires in Southern Portugal can support fire management practices and encourage the sustainable development of the region. Full article