Search Results (433)

With increasing wildfire severity and duration driven by climate change, accurately predicting fire behavior over extended time frames is critical for effective management and mitigation of such wildfires. Fire propagation models play a pivotal role in these efforts, providing simulations that can be used to strategize and respond to active fires. This study examines the fire area simulator (FARSITE) model’s performance in simulating recent wildfire events that persisted over 24 h with limited firefighting intervention in mostly remote access areas across diverse ecosystems. Our findings reveal key insights into a prolonged wildfire scenarios potentially informing improvements in operational fire management and long-term predictive accuracy, as the area comparisons indexes showed reasonable results between the detected fires from the fire information for resource management systems (FIRMSs) in the first 24 h of the fire and the following days. A case study of a recent wildfire in Madeira Island highlights the integration of real-time weather predictions and post-event weather data analysis. This analysis underscores the potential of combining accurate forecasts with retrospective validation to improve predictive capabilities in dynamic fire environments, which guided the development of a software platform designed to analyse ongoing wildfire events in real-time, leveraging image satellite data and weather predictions. 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

The intensification of global climate change, combined with increasing human activities, has significantly increased wildfire frequency and severity, posing a major global environmental challenge. As an illustration, Guizhou Province in China encountered a total of 221 wildfires over a span of 12 days. Despite significant advancements in wildfire prediction models, challenges related to data imbalance and model interpretability persist, undermining their overall reliability. In response to these challenges, this study proposes an explainable wildfire risk prediction model (EWXS) leveraging Extreme Gradient Boosting (XGBoost), with a focus on Guizhou Province. The methodology involved converting raster and vector data into structured tabular formats, merging, normalizing, and encoding them using the Weight of Evidence (WOE) technique to enhance feature representation. Subsequently, the cleaned data were balanced to establish a robust foundation for the EWXS model. The performance of the EWXS model was evaluated in comparison to established models, such as CatBoost, using a range of performance metrics. The results indicated that the EWXS model achieved an accuracy of 99.22%, precision of 98.48%, recall of 96.82%, an F1 score of 97.64%, and an AUC of 0.983, thereby demonstrating its strong performance. Moreover, the SHAP framework was employed to enhance model interpretability, unveiling key factors influencing wildfire risk, including proximity to villages, meteorological conditions, air humidity, and variations in vegetation temperature. This analysis provides valuable support for decision-making bodies by offering clear, explanatory insights into the factors contributing to wildfire risk. Full article

Wildfires frequently occur, posing a significant threat to the operational stability of transmission lines across mountainous forest areas. Therefore, this paper reviews numerous studies conducted by domestic and international scholars on the gap breakdown tests and discharge mechanisms of transmission lines under simulated wildfire conditions. It analyses and summarizes the physical parameter measurement methods commonly used in current experiments. Combining the results of existing experiments, this study analyzes the discharge mechanisms, including the research progress made in numerical simulations. The conclusion is that existing tests are limited in their measurement methods of the physical quantities related to breakdown characteristics, and it is not easy to strictly control experimental variables when considering complex factors. Numerical simulations mainly focus on multi-physical field simulations, which consider the characteristics of vegetation fires in short gaps. The synergistic mechanism of environmental factors on gap breakdown characteristics remains unclear. This paper points out the breakdown characteristics and discharge mechanisms derived from existing experiments and numerical simulations under various influencing factors, highlighting their applicability and limitations, which differ from complex actual transmission lines in the environment. Then, we look forward to the future development of simulation test platforms that could better reflect the actual transmission line corridor environment, incorporating multi-parameter measurement and in-depth numerical simulation works that consider climate and terrain factors. Full article

This paper examines land management technologies to enhance environmental monitoring more efficiently. The study highlights the interactions between human activities and environmental systems with a data-driven environmental monitoring approach. There are many human pressures, such as pollution, land degradation, and habitat loss, negatively impacting soil health. The methodology proposed improves soil status assessments in response to evolving environmental pressures by utilizing satellite imagery and predictive modeling. The integration of Sentinel-2 imagery, the calculation of various spectral indices (NDVI, NBR, NDMI, EVI, SAVI) at different time intervals, and the application of the Isolation Forest algorithm are employed in this study to determine the specific area that is affected by the environmental issue. The chosen algorithm was favored due to its superior performance in handling high-dimensionality data, enhanced computational efficiency, provision of interpretable results, and insensitivity to disparities in class distribution. This study analyzes two separate study cases at different scales. The first involves wildfire identification achieving an overall accuracy of 98%. The second focuses on the expansion areas to pre-existing quarries with an overall accuracy of 95%. The NBR proved most effective in delineating burned areas, whereas the EVI generated the most remarkable results in the quarry case study. This approach provides an effective and scalable tool for environmental monitoring, supporting sustainable management policies, and strengthening ecosystem resilience. Full article

Machine learning and artificial intelligence models have become popular for climate change prediction. Forested regions in California and Western Australia are increasingly facing intense wildfires, while other parts of the world face various climate-related challenges. To address these issues, machine learning and artificial intelligence models have been developed to predict wildfire risks and support mitigation strategies. Our study focuses on developing wildfire prediction models using one-class classification algorithms. These include Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. The models were validated through five-fold cross-validation to minimize bias in selecting training and testing data. The results showed that these one-class machine learning models outperformed two-class machine learning models based on the same ground truth data, achieving mean accuracy levels between 90% and 99%. Additionally, we employed Shapley values to identify the most significant features affecting the wildfire prediction models, contributing a novel perspective to wildfire prediction research. When analyzing models trained on the California dataset, seasonal maximum and mean dew point temperatures were critical factors. These insights can significantly improve wildfire mitigation strategies. Furthermore, we have made these models accessible and user-friendly by operationalizing them through a REST API using Python Flask 1.1.2 and developing a web-based tool. Full article

This study investigates the physical interactions and between forest fires and the atmosphere, which often lead to conditions favourable to instability and the formation of pyrocumulus (PyCu). Using the coupled atmosphere–fire spread modelling framework, WRF-SFIRE, the Portuguese October 2017 Quiaios wildfire, in association with tropical cyclone Ophelia, was simulated. Fire spread was imposed via burnt area data, and the fire’s influence on the vertical and surface atmosphere was analysed. Simulated local atmospheric conditions were influenced by warm and dry air advection near the surface, and moist air in mid to high levels, displaying an inverted “V” profile in thermodynamic diagrams. These conditions created a near-neutrally unstable atmospheric layer in the first 3000 m, associated with a low-level jet above 1000 m. Results showed that vertical wind shear tilted the plume, resulting in an intermittent, high-based, shallow pyroconvection, in a zero convective available potential energy environment (CAPE). Lifted parcels from the fire lost their buoyancy shortly after condensation, and the presence of PyCu was governed by the energy output from the fire and its updrafts. Clouds formed above the lifted condensation level (LCL) as moisture fluxes from the surface and released from combustion were lifted along the fire plume. Clouds were primarily composed of liquid water (1 g/kg) with smaller traces of ice, graupel, and snow (up to 0.15 g/kg). The representation of pyroconvective dynamics via coupled models is the cornerstone of understanding the phenomena and field applications as the computation capability increases and provides firefighters with real time extreme fire conditions or predicting ahead of time. Full article

Wildfires pose an escalating threat to ecosystems and human settlements, making accurate forecasting essential for early mitigation. This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor–Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. The models were trained and evaluated using historical data from Chile (2000–2023), including wildfire occurrences, meteorological variables, topography, and vegetation indices. After preprocessing and class balancing, each model was tested over 100 experimental runs. All models achieved outstanding performance, with F1-Scores exceeding 0.999 and perfect AUC-ROC scores. The Transformer model showed a slight advantage over the CNN (99.94%) and Actor–Critic DRL (99.93%) in accuracy. Feature importance analysis identified wind speed, temperature, and vegetation indices as the most influential variables. While DRL offers theoretical benefits for adaptive decision-making, Transformer architectures more effectively capture spatiotemporal dependencies in wildfire dynamics. The findings can support the integration of deep learning models into early warning systems, contributing to proactive wildfire risk management. Future work will include validation with diverse regional datasets, real-time deployment, and collaboration with emergency response agencies. Full article

Accurate wildfire spread modeling critically depends on the representation of wind dynamics, which vary with terrain, land cover characteristics, and height above ground. Many fire spread models are often coupled with coarse atmospheric grids that cannot explicitly resolve the vertical variation of wind near flame heights. Rothermel’s fire spread model, a widely used parameterization, relies on midflame wind speed to calculate the fire rate of spread. In coupled fire atmosphere models such as the Community Fire Behavior Model (CFBM), users are required to specify the midflame height before running a fire spread simulation. This study evaluates the use of logarithmic interpolation wind adjustment factors (WAF) for improving midflame wind speed estimates, which are critical for the Rothermel model. We compare the fixed wind height approach that is currently used in CFBM with WAF-derived winds for unsheltered and sheltered surface fire spread. For the first time in this context, these simulations are validated against satellite and ground-based observations of fire perimeters. The results show that WAF implementation improves fire perimeter predictions for both grass and canopy fires while reducing the overestimation of fire spread. Moreover, this approach solely depends on the fuel bed depth and estimation of canopy density, enhancing operational efficiency by eliminating the need for users to specify a wind height for simulations. Full article

Understanding the characteristics of wildfires in the Beijing–Tianjin–Hebei (BTH) region is crucial for improving the monitoring of local wildfire danger. Our investigation first establishes the spatial distributions of fire weather index (FWI) distributions and satellite-observed wildfire occurrences. The FWI provides a reasonably accurate representation of wildfire danger in the BTH region. Through Self-Organizing Maps (SOM) clustering analysis, we identify nine distinct spatial patterns in FWI composites. Notably, the annual frequency of SOM modes 2 and 7 has shown a significant increasing trend over the past 40 years. The spatial distribution of the highest FWI values in these two modes is in the southern and central BTH regions, respectively. Subsequently, we examine the relationship between FWI variations and atmospheric circulation patterns. A synoptic analysis indicates that the increased fuel availability index observed in SOM modes 2 and 7 can be primarily attributed to two key factors. One is a post-trough system, which is marked by a decrease in water vapor transport. The other is a high-pressure system, which is associated with higher temperatures and drought conditions. Finally, the relative contributions of the fuel available index and the wildfire spread rate index to the FWI are quantified using a partial differential approach. The variations in the fuel available index are the primary drivers of the high FWI values in these two SOM patterns. This study underscores the importance of analyzing the synergistic effects of multiple atmospheric circulation patterns on the fuel availability index, which is critical for improving wildfire danger prediction at different timescales in the BTH region. Full article

Climate change is increasing variability in environmental conditions and the frequency and severity of natural hazards such as hurricanes, floods, and wildfires. In this paper, we use general systems theory to describe how disaster management systems are composed of four types of system regulators (aggregation, passive, error control, and anticipation) that are deployed to provide protection from natural hazards. We argue that climate change, by changing causal relationships in the environment and thereby reducing the predictability of related hazards and altering exposure to them, is likely to require that disaster management systems be restructured by changing the combinations of system regulators that are employed to prevent or mitigate disasters. This leads to the conclusion that one of the keys to developing effective policies to support adaptation to climate change and to promote sustainability hinges on understanding how disaster management systems can be interpreted as mechanisms for regulating exposure and vulnerability to minimise the threats from natural hazards. Consequently, developing methods for interpreting and modelling system regulators in disaster management systems is an important next step. 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

Open-source disaster intelligence (OSDI) is crucial for improving situational awareness, disaster preparedness, and real-time decision-making. Traditional OSDI frameworks often rely on social media data, which are susceptible to misinformation and credibility issues. This study proposes a novel AI-driven framework utilizing automated data collection from 444 large-scale online news portals, including CNN, BBC, CBS News, and The Guardian, to enhance data reliability. Over a 514-day period (27 September 2023 to 26 February 2025), 1.25 million news articles were collected, of which 17,884 were autonomously classified as disaster-related using Generative Pre-Trained Transformer (GPT) models. The analysis identified 185 distinct countries and 6068 unique locations, offering unprecedented geospatial and temporal intelligence. Advanced clustering and predictive analytics techniques, including K-means, DBSCAN, seasonal decomposition (STL), Fourier transform, and ARIMA, were employed to detect geographical hotspots, cyclical patterns, and temporal dependencies. The ARIMA (2, 1, 2) model achieved a mean squared error (MSE) of 823,761, demonstrating high predictive accuracy. Key findings highlight that the USA (6548 disasters), India (1393 disasters), and Australia (1260 disasters) are the most disaster-prone countries, while hurricanes/typhoons/cyclones (5227 occurrences), floods (3360 occurrences), and wildfires (2724 occurrences) are the most frequent disaster types. The framework establishes a comprehensive methodology for integrating geospatial clustering, temporal analysis, and multimodal data processing in OSDI. By leveraging AI automation and diverse news sources, this study provides a scalable, adaptable, and ethically robust solution for proactive disaster management, improving global resilience and preparedness. Full article

Arid and semiarid ecosystems face significant water scarcity due to high evaporation rates exceeding precipitation. This study examines temporal variations in water relations of two woody species, Vachellia schaffneri (S. Watson) Seigler & Ebinger, and Prosopis laevigata (Humb. & Bonpl. ex Willd.) M.C. Johnst, and one epiphyte, Tillandsia recurvata (L.) L. (Bromeliaceae), to assess their drought tolerance and water storage capacity. We hypothesized that species with greater water storage capacity would exhibit lower drought tolerance due to reduced osmotic adjustments, whereas species with lower storage capacity would maintain turgor through osmotic regulation and cell wall rigidity. Predawn and midday water potentials (Ψ_pd, Ψ_md) were measured, and pressure–volume (P–V) curves were used to derive parameters such as saturated water content (SWC), osmotic potential (π_o), turgor loss point (Ψ_TLP), relative water content at Ψ_TLP (RWC_TLP), bulk modulus of elasticity (ε), and full turgor capacitance (C_FT). Significant correlations were found between C_FT and Ψ_TLP (positive), π_o (positive), and ε (negative). P. laevigata and T. recurvata exhibited higher water storage capacities (41.46 and 26.45 MPa⁻¹, respectively) but had a lower ability to maintain cell turgor under drought conditions. In contrast, V. schaffneri exhibited the lowest water storage capacity (11.88 MPa⁻¹) but demonstrated the highest ability to maintain cell turgor (Ψ_TLP = −1.31 MPa) and superior osmotic adjustments (π_o = −0.59 MPa). Both V. schaffneri and P. laevigata exhibited rigid cell walls, whereas T. recurvata displayed greater elasticity in its cell structures. The lowest moisture content in V. schaffneri suggests increased flammability and fire spread potential. Future studies should focus on live fuel moisture content across more species, explore seasonal variations in hydraulic traits, and integrate these physiological parameters into fire risk models to enhance wildfire prediction and management. Full article

Quantifying forest structure to assess changing wildfire risk factors is critical as vulnerable areas require mitigation, management, and resource allocation strategies. Remote sensing offers the opportunity to accurately measure forest attributes without time-intensive field inventory campaigns. Here, we quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery. In addition, we compared UAS–SfM estimates with those derived using similar methods applied to Airborne Laser Scanning (ALS) 3D point clouds as well as traditional ground-based measurements. Canopy cover estimates were similar across remote sensing (ALS, UAS-SfM) and ground-based approaches (r² = 0.79, RMSE = 16.49%). Compared to ground-based approaches, UAS-SfM point clouds allowed for correct detection of 68% of trees and estimated tree heights were significantly correlated (r² = 0.69, RMSE = 5.1 m). UAS-SfM was not able to estimate canopy base height due to its inability to penetrate dense canopies in these forests. Since canopy cover and individual tree heights were accurately estimated at the plot-scale in this unique bioregion with diverse topography and complex species composition, we recommend UAS-SfM as a viable approach and affordable solution to estimate these critical forest parameters for predictive wildfire modeling. Full article