Search Results (174)

Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate. Full article

Fire is a key driver of ecosystem dynamics under global change, and understanding its complex relationship with the climate system is crucial for regional wildfire risk management and the development of ecological adaptation strategies. The western United States is a critical region for studying fire–climate interactions due to its pronounced environmental gradients, diverse fire regimes, and high vulnerability to climate change, which together provide a robust natural laboratory for examining spatial variability in fire responses. Based on tree-ring fire-scar records systematically collected from five major ecoregions in the western United States via the International Tree-Ring Data Bank (ITRDB), this study reconstructed fire history sequences spanning 430–454 years. By integrating methods such as correlation analysis, random forest regression, superposed epoch analysis, and effect size assessment, we systematically revealed the spatial differentiation patterns of fire frequency and fire spatial extent across different ecoregions, quantified the relative contributions of key climatic drivers, and identified climatic anomaly characteristics during extreme fire years. The results indicate that: (1) there are significant differences in fire frequency between different ecological areas; (2) summer drought conditions (PDSI) are the most consistent and strongest driver of fire across all ecoregions, and ENSO (NINO3) also shows a widespread negative correlation; (3) random forest models indicate that the Sierra Nevada and Madrean Archipelago ecoregions are the most sensitive to multiple climatic factors, while fire in regions such as the Northern Rockies may be more regulated by non-climatic processes; (4) extreme fire years across all ecoregions are associated with significant negative PDSI anomalies with prominent effect sizes, confirming that severe drought is the dominant cross-regional precondition for extreme fire events. This study emphasizes the region-specific nature of fire–climate relationships and provides a scientific basis for developing differentiated, ecoregion-specific fire prediction models and prevention strategies. The methodological framework and findings offer valuable insights for fire regime studies in other global forest ecosystems facing similar climate challenges. Full article

Wildfires pose significant risks to ecosystems, human lives, and infrastructure, necessitating advanced predictive tools to mitigate their impacts. This study presents a machine learning-based framework for wildfire susceptibility mapping (WSM), designed as a predictive tool for wildfire occurrence. Using geographical information systems (GIS), a comprehensive dataset was developed by combining fourteen critical factors, including climatic, topographic, vegetation, and human activity data, from diverse sources. Four ML methods—Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), and XGBoost—were applied and compared. The results show that the XGBoost model (with an AUC of 0.96) generated the best susceptibility map. Validation using 2024–2025 fire occurrences (MODIS and Protection Civile data) showed that 87.73% of fire events were correctly captured within high and very high susceptibility zones, confirming the robustness of the proposed model. Feature importance analysis revealed that human activities, precipitation, and temperature were the most influential in wildfire prediction. These findings provide valuable insights into wildfire dynamics and contribute to the development of more effective fire prevention and mitigation strategies. Full article

Guizhou Province in China exhibits a distinctive agroforestry mosaic landscape with frequent human activity in forested areas. This region experiences recurrent forest fires, characterized by significant difficulties in suppression and high risks. Research on the prediction of forest fire occurrences holds crucial practical significance in terms of enhancing regional forest fire prevention capabilities. However, the current fire risk forecasting methods in the area consider only meteorological factors, neglecting firebrands and fuel conditions, which results in deviations between forecasted and actual fire occurrences. Therefore, this study proposes a novel fire occurrence prediction method that utilizes the ignition component (IC) from the National Fire Danger Rating System (NFDRS) to characterize the weather–fuel complex while integrating the firebrand occurrence probability to construct a predictive model. The applicability and accuracy of this method are also evaluated. The results show that, firstly, the probability of at least one daily forest fire occurrence in the study area can be expressed as a nonlinear function based on the IC. Secondly, as time progresses, the correlation between the forest fire occurrence probability and the IC shows a decreasing trend, although the differences across different time spans are not statistically significant. Thirdly, when a 5-year time span is adopted, the error in calculating the forest fire occurrence probability based on the IC is significantly lower than at other time spans. Finally, a predictive model for the forest fire occurrence probability based on the IC is established, where P = (100*IC)/(4.06 + IC), with a mean absolute error (MAE) of 4.83% and mean relative error (MRE) of 14.87%. Based on this research, the IC enables the calculation of forest fire occurrence probabilities, assessment of fire risk ratings, and guidance for fire preparedness and planning. This work also provides theoretical support and a methodological reference for conducting forest fire probability studies in other regions. Full article

Forest fires have become increasingly frequent and severe due to climate change and intensified human activities, posing critical challenges to ecological security and emergency management. Despite the availability of abundant environmental, spatial, and operational data, these resources remain fragmented and heterogeneous, limiting the efficiency and accuracy of fire prediction and response. To address this challenge, this study proposes a Semantic Digital Twin-Driven Framework for integrating multi-source data and supporting forest fire prediction and response. The framework constructs a multi-ontology network that combines the Semantic Sensor Network (SSN) and Sensor, Observation, Sample, and Actuator (SOSA) ontologies for sensor and observation data, the GeoSPARQL ontology for geospatial representation, and two domain-specific ontologies for fire prevention and emergency response. Through systematic data mapping, instantiation, and rule-based reasoning, heterogeneous information is transformed into an interconnected knowledge graph. The framework supports both semantic querying (SPARQL) and rule-based reasoning (SWRL) to enable early risk alerts, resource allocation suggestions, and knowledge-based decision support. A case study in Sichuan Province demonstrates the framework’s effectiveness in integrating historical and live data streams, achieving consistent reasoning outcomes aligned with expert assessments, and improving decision timeliness by enhancing data interoperability and inference efficiency. This research contributes a foundational step toward building intelligent, interoperable, and reasoning-enabled digital forest systems for sustainable fire management and ecological resilience. Full article

Accurate wildfire prediction and identification of key environmental drivers are critical for effective wildfire management. We propose a spatiotemporally adaptive framework integrating ST-DBSCAN clustering with GTWR-RFR. In this hybrid model, Random Forest captures local nonlinear relationships, while GTWR assigns adaptive spatiotemporal weights to refine predictions. Using historical wildfire records from Hunan Province, China, we first derived wildfire occurrence probabilities via ST-DBSCAN, avoiding the need for artificial non-fire samples. We then benchmarked GTWR-RFR against seven models, finding that our approach achieved the highest accuracy (R² = 0.969; RMSE = 0.1743). The framework effectively captures spatiotemporal heterogeneity and quantifies dynamic impacts of environmental drivers. Key contributing drivers include DEM, GDP, population density, and distance to roads and water bodies. Risk maps reveal that central and southern Hunan are at high risk during winter and early spring. Our approach enhances both predictive performance and interpretability, offering a replicable methodology for data-driven wildfire risk assessment. Full article

In recent years, ecosystems in Saudi Arabia have experienced severe degradation due to factors such as hyperaridity, overgrazing, climate change, urban expansion, and an increase in uncontrolled wildfires. Among these, wildfires have emerged as the second most significant threat to forests after urban expansion. This study aims to map wildfire susceptibility in southwestern Saudi Arabia by identifying key driving factors and evaluating the performance of several machine learning models under conditions of limited and imbalanced data. The models tested include Maxent, logistic regression, random forest, XGBoost, and support vector machine. In addition, an NDVI-based phenological approach was applied to assess seasonal vegetation dynamics and to compare its effectiveness with conventional machine learning-based susceptibility mapping. All methods generated effective wildfire risk maps, with Maxent achieving the highest predictive accuracy (AUC = 0.974). The results indicate that human activities and dense vegetation cover are the primary contributors to wildfire occurrence. This research provides valuable insights for wildfire risk assessment in data-scarce regions and supports proactive fire management strategies in non-traditional fire-prone environments. Full article

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article

This study aimed to investigate the physical factors influencing the occurrence of forest fires and to create a fire risk map of Phrae Province. Remote sensing and geographic information system (GIS) technology were applied for the analysis, focusing on seven factors: the digital elevation model (DEM); slope; Normalized Difference Vegetation Index (NDVI); aspect; and distances from people, water, and roads. All of these geographical factors can affect forest fires. This resulted in a MaxEnt (Maximum Entropy) model with an AUC (area under the curve) of 0.849, indicating its great prediction ability. The findings revealed that the variables influencing forest fire incidence were the DEM, NDVI, slope, distance from roads, distance from water, distance from communities, and aspect, in that order. Subsequently, a fire risk map for wildfires was developed by reclassifying the data into five levels—very low risk, low risk, medium risk, high risk, and very high risk—accounting for 341,395.54, 88,132.64, 76,162.41, 81,157.55, and 57,384.10 hectares or 52.99, 13.68, 11.82, 12.60, and 8.91% of the total area, respectively. The areas classified as very high risk, high risk, medium risk, and low risk included the Song, Long, and Rong Kwang Districts. The area with the lowest risk was Nong Muang Khai District. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article

Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North Kaibab (NK) Plateau in Arizona and Monroe Mountain (MM) in Utah. We used random forest models to predict vegetation attributes, evaluating the performance of full models and transferred models using R², RMSE, and bias. The MLS consistently outperformed the TLS system, particularly for canopy-related attributes and woody biomass components. However, the TLS system showed potential for capturing canopy structure attributes, while offering advantages like operational simplicity, low equipment demands, and ease of deployment in the field, making it a cost-effective alternative for managers without access to more complex and expensive mobile or airborne systems. Our results show that model transferability between NK and MM is highly variable depending on the fuel attributes. Attributes related to canopy biomass showed better transferability, with small losses in predictive accuracy when models were transferred between the two sites. Conversely, surface fuel attributes showed more significant challenges for model transferability, given the difficulty of laser penetration in the lower vegetation layers. In general, models trained in NK and validated in MM consistently outperformed those trained in MM and transferred to NK. This may suggest that the NK plots captured a broader complexity of vegetation structure and environmental conditions from which models learned better and were able to generalize to MM. This study highlights the potential of ground-based LiDAR technologies in providing detailed information and important insights into fire risk and forest structure. Full article

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

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

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

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