Search Results (457)

Fuel moisture content (FMC) estimation is essential for wildfire danger assessment and fire behavior modeling. This study quantifies the value of integrating satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP and the Advanced Baseline Imager (ABI) aboard GOES-16 with High-Resolution Rapid Refresh (HRRR) numerical weather prediction data for hourly 10 h dead FMC estimation across the continental United States. We leverage the complementary characteristics of each system: VIIRS provides enhanced spatial resolution (375–750 m), while ABI contributes high temporal frequency observations (hourly). Using XGBoost machine learning models trained on 2020–2021 data, we systematically evaluate performance improvements stemming from incorporating satellite retrievals individually and in combination with HRRR meteorological variables through eight experimental configurations. Results demonstrate that while both satellite systems individually enhance prediction accuracy beyond HRRR-only models, their combination provides substantially greater improvements: 27% RMSE and MAE reduction and 46.7% increase in explained variance (R${}^2$) relative to HRRR baseline performance. Comprehensive seasonal analysis reveals consistent satellite data contributions across all seasons, with stable median performance throughout the year. Diurnal analysis across the complete 24 h cycle shows sustained improvements during all hours, not only during satellite overpass times, indicating effective integration of temporal information. Spatial analysis reveals improvements in western fire-prone regions where afternoon overpass timing aligns with peak fire danger conditions. Feature importance analysis using multiple explainable AI methods demonstrates that HRRR meteorological variables provide the fundamental physical framework for prediction, while satellite observations contribute fine-scale refinements that improve moisture estimates. The VIIRS lag-hour predictor successfully maintains observational value up to 72 h after acquisition, enabling flexible operational implementation. This research demonstrates the first systematic comparison of VIIRS versus ABI contributions to dead FMC estimation and establishes a framework for hourly, satellite-enhanced FMC products that support more accurate fire danger assessment and enhanced situational awareness for wildfire management operations. Full article

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions. Full article

Wildfires profoundly impact carbon cycles, climate, and human societies. However, a comprehensive understanding of the long-term spatiotemporal characteristics and influencing factors of global wildfires remains limited. This study analyzes the spatiotemporal patterns and influencing factors of wildfires from 1982 to 2018 using a global satellite-derived burned area (BA) product. We classified fire-prone regions into four types based on climate: Tropical dry season (Tr-ds), Arid fuel-limited (Ar-fl), Boreal hot season (Bo-hs), and Temperate dry and hot season (Te-dhs). Major fire hotspots include Africa, northern Australia, South America’s Brazilian highlands, the Indochina Peninsula, and Central Asia. The global multi-year average BA is 4.59 × 10⁸ ha yr⁻¹, with Africa (3.04 × 10⁸ ha yr⁻¹) and northern Australia (2.83 × 10⁷ ha yr⁻¹) being the most affected. Fire activity peaks annually in July–September and December–January. From 1982 to 2018, both the global and sub-regional BA show significant increasing trends, except northern and temperate areas, though reduced burn-down areas from shorter periods have been reported during the MODIS era. At both the global scale and in the Tr-ds region, wildfire activity is strongly associated with hot and dry conditions in combination with abundant fuel availability. Fire activity in the Ar-fl region is mainly constrained by fuel availability. Surface dryness plays a dominant role in fire activity in the Bo-hs. In contrast, fire activity in the Te-dhs region shows no clear pattern. The influence of different factors on the BA is subject to threshold effects. These findings contribute to a deeper understanding of long-term wildfire dynamics across different regions globally. Full article

Wildfires are increasing globally and pose major challenges for assessing post-fire vegetation recovery and ecosystem resilience. We analyzed long-term Landsat time series in two contrasting fire-prone ecosystems in the United States and Australia. Vegetation area was extracted using the Enhanced Vegetation Index (EVI) with Otsu thresholding. Recovery to pre-fire baseline levels was modeled using linear, logistic, locally estimated scatterplot smoothing (LOESS), and generalized additive models (GAM), and their performance was compared using multiple metrics. The results indicated rapid recovery of Australian forests to baseline levels, whereas this was not the case for forests in the United States. Among climatic factors, temperature was the dominant parameter in Australia (Spearman ρ = 0.513, p < 10⁻⁸), while no climatic variable significantly influenced recovery in California. Methodologically, GAM consistently performed best in both regions due to its success in capturing multiphase and heterogeneous recovery patterns, yielding the lowest values of AIC (United States: 142.89; Australia: 46.70) and RMSE_cv (United States: 112.86; Australia: 2.26). Linear and logistic models failed to capture complex recovery dynamics, whereas LOESS was highly sensitive to noise and unstable for long-term prediction. These findings indicate that post-fire recovery is inherently nonlinear and ecosystem-specific and that simple models are insufficient for accurate estimation, with GAM emerging as an appropriate method for assessing vegetation recovery using remote sensing data. This study provides a transferable approach using remote sensing and GAM to monitor forest resilience under accelerating global fire regimes. Full article

Forest fire susceptibility prediction is essential for effective management, yet considerable uncertainty persists under future climate change, especially in climate-sensitive plateau regions. This study integrated MODIS fire data with climatic, topographic, vegetation, and anthropogenic variables to construct an Extreme Gradient Boosting (XGBoost) model for the Yunnan Plateau, a region highly prone to forest fires. Compared with Support Vector Machine and Random Forest models, XGBoost showed superior ability to capture nonlinear relationships and delivered the best performance, achieving an AUC of 0.907 and an overall accuracy of 0.831. The trained model was applied to climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 to assess future fire susceptibility. Results indicated that high-susceptibility periods primarily occur in winter and spring, driven by minimum temperature, average temperature, and precipitation. High-susceptibility areas are concentrated in dry-hot valleys and mountain basins with elevated temperatures and dense human activity. Under future climate scenarios, both the probability and spatial extent of forest fires are projected to increase, with a marked expansion after 2050, especially under SSP5-8.5. Although the XGBoost model demonstrates strong generalizability for plateau regions, uncertainties remain due to static vegetation, coarse anthropogenic data, and differences among climate models. Full article

Forests play a central role in climate change mitigation by acting as biogenic carbon reservoirs and providing renewable biomass for energy systems. In Portugal, where fire-prone landscapes and species composition dynamics pose increasing management challenges, understanding the carbon storage potential of forest biomass is crucial for designing effective decarbonization strategies. This study provides a comprehensive characterization of the Portuguese forest and quantifies the biogenic carbon stored in live and dead biomass across the main forest species. Species-specific carbon contents, rather than the conventional 50% assumption widely used in the literature, were applied to National Forest Inventory data, enabling more realistic and representative carbon stock estimates expressed in kilotonnes of CO₂ equivalent. While the approach relies on inventory-based biomass data and literature-derived carbon fractions and is therefore subject to associated uncertainties, it provides an improved representation of species-level carbon storage at the national scale. Results show that Pinus pinaster, Eucalyptus globulus, and Quercus suber together represent the largest share of carbon storage, with approximately 300,000 kilotonnes of CO₂ equivalent retained in living trees. Wood is the dominant carbon pool, but roots and branches also account for a substantial fraction, emphasizing the need to consider both above- and below-ground biomass in carbon accounting. In parallel, a bibliometric analysis based on the systematic evaluation of scientific publications was conducted to characterize the evolution, thematic focus, and geographic distribution of global research on forest-based biogenic carbon. This analysis reveals a rapidly expanding scientific interest in biogenic carbon, particularly since 2020, reflecting its growing relevance in climate change mitigation frameworks. Overall, the results underscore both the strategic importance of Portuguese forests and the alignment of this research with the broader international scientific agenda on forest-based biogenic carbon. Full article

Peatlands provide essential ecological services but are highly vulnerable to degradation from drainage, leading to greenhouse gas emissions, land subsidence, and increased fire susceptibility. This study investigates peat hydrology and its relationship to fire risk in a fire-prone area in South Sumatra, Indonesia. Groundwater levels and soil moisture were continuously monitored using automated loggers, and recession analysis quantified their rates of decline. Multispectral drone imagery (NDVI, NDWI) over a 44.1-ha area assessed vegetation and surface wetness, while fire occurrences (2019–2024) were analyzed using the Fire Information for Resource Management System (FIRMS). During a 58-day dry period, groundwater depth reached 78.5 cm with a recession rate of 9.68 mm day⁻¹, while soil moisture decreased by 0.00291 m³ m⁻³ per day over 27 consecutive dry days. Drone imagery revealed that unhealthy and dead grass covered nearly 90% of the site, although wetness remained moderate (NDWI = 0.02–0.58). FIRMS data indicated that rainfall below 2000 mm year⁻¹ and prolonged dry spells (>30 days) strongly trigger peat fires. These findings correspond with early-warning model outputs based on soil moisture recession and ignition thresholds. Maintaining a high groundwater level is, therefore, crucial for reducing peat fire vulnerability under extended dry conditions. Full article

Wildfires are among the most severe disturbances in Mediterranean ecosystems, altering vegetation structure, soil properties, and hydrological functioning. Understanding post-fire hydrological dynamics is crucial for predicting flood and erosion risks and vegetation restoration in fire-prone regions. This study investigates the hydrological responses of Mediterranean watersheds following a wildfire event by integrating WiMMed (Watershed Integrated Management in Mediterranean Environments), a distributed, physically based hydrological model, with high-resolution vegetation data derived from LiDAR and Landsat imagery. A Priority Post-Fire Restoration Index (PPRI) was calculated as the weighted sum of the six parameters runoff (mm), flow accumulation (mm), distance to drainage network (m), slope (%), erodibility (K), lithology, and LiDAR index under a sediment reduction and runoff peak reduction scenario. The post-fire hydrological processes modeled with WiMMed described the dynamics of surface runoff and soil moisture redistribution across the upper soil layers after fire, and their gradual attenuation with vegetation regrowth. The spatial distribution of the PPRI identified specific zones within the burned watershed that require urgent restoration measures (10% and 4.55% under sediment reduction and peak reduction scenarios, respectively). The combined use of process-based modeling and remote sensing offers valuable insights into watershed-scale hydrological resilience and supports the design of post-fire restoration strategies in Mediterranean landscapes. Full article

Wildfires increasingly threaten Mediterranean landscapes, particularly in regions like Attica, Greece, where urban sprawl, agricultural abandonment, and climatic conditions heighten the risk at the Wildland–Urban Interface (WUI). The Mediterranean basin, recognized as one of the global wildfire “hotspots”, has witnessed a steady increase in both fire severity, frequency, and burned area during the last four decades, a trend amplified by urban sprawl and agricultural land abandonment. This study represents the first integrated, region-wide mapping of the WUI and associated wildfire risk in Attica, the most densely urbanized area in Greece and one of the most fire-exposed metropolitan regions in Southern Europe, utilizing advanced techniques such as Earth Observation and GIS analysis. For this purpose, various geospatial datasets were coupled, including Copernicus High Resolution Layers, multi-decadal Landsat fire history archive, UCR-STAR building footprints, and CORINE Land Cover, among others. The research delineated WUI zones into 40 interface and intermix categories, revealing that WUI encompasses 26.29% of Attica, predominantly in shrub-dominated areas. An analysis of fire frequency history from 1983 to 2023 indicated that approximately 102,366 hectares have been affected by wildfires. Risk assessments indicate that moderate hazard zones are most prevalent, covering 36.85% of the region, while approximately 25% of Attica is classified as moderate, high, or very high susceptibility zones. The integrated risk map indicates that 37.74% of Attica is situated in high- and very high-risk zones, principally concentrated in peri-urban areas. These findings underscore Attica’s designation as one of the most fire-prone metropolitan regions in Southern Europe and offer a viable methodology for enhancing land-use planning, fuel management, and civil protection efforts. Full article

The sustainability of apiculture within Mediterranean forest ecosystems is contingent upon the extent and health of melliferous tree habitats. This study outlines a five-year initiative (2020–2024) aimed at mapping and monitoring four principal honey-producing tree species—pine (Pinus halepensis and Pinus nigra), Greek fir (Abies cephalonica), oak (Quercus ithaburensis subsp. macrolepis), and chestnut (Castanea sativa)—across Evia, Greece. This is achieved through the utilization of high-resolution Sentinel-2 satellite imagery in conjunction with a hierarchical deep learning framework. Distinct from prior vegetation mapping endeavors, this research introduces an innovative application of a hierarchical framework for species-level semantic segmentation of apicultural flora, employing a U-Net convolutional neural network to capture fine-scale spatial and temporal dynamics. The proposed framework first stratifies forests into broadleaf and coniferous types using Copernicus DLT data, and subsequently applies two specialized U-Net models trained on Sentinel-2 NDVI time series and DEM-derived topographic variables to (i) discriminate pine from fir within coniferous forests and (ii) distinguish oak from chestnut within broadleaf stands. This hierarchical decomposition reduces spectral confusion among structurally similar species and enables fine-scale semantic segmentation of apicultural flora. Our hierarchical framework achieves 92.1% overall accuracy, significantly outperforming traditional multiclass approaches (89.5%) and classical ML methods (76.9%). The results demonstrate the framework’s efficacy in accurately delineating species distributions, quantifying the ecological and economic impacts of the catastrophic 2021 forest fires, and projecting long-term habitat recovery trajectories. The integration of a novel hierarchical approach with Deep Learning-driven monitoring of climate- and disturbance-driven changes in honey-producing habitats marks a significant step towards more effective assessment and management of four major beekeeping tree species. These findings highlight the significance of such methodologies in guiding conservation, restoration, and adaptive management strategies, ultimately supporting resilient apiculture and safeguarding ecosystem services in fire-prone Mediterranean landscapes. Full article

The mitigation of natural hazards is a persistent challenge in Chile, where recurrent events such as summer forest fires and winter floods cause severe material and human losses. Municipalities, as key actors in disaster management, often face difficulties due to fragmented territorial information and the lack of standardized tools to support decision-making. This study applies the Land Administration Domain Model (ISO 19152) International Standard to design a multipurpose cadastre adapted to the Chilean context. The methodological approach integrates cadastral data with hazard and risk information, structuring it into standardized sub-packages that facilitate spatial analysis, interoperability, and municipal planning. The proposed model demonstrates its capacity to identify risk-prone areas, link property units with hazard data, and generate reliable inputs for disaster risk reduction plans. A prototype decision-support panel illustrates how the integration of cadastral and risk data can improve access to territorial information and support local governance. The contribution of this research is twofold: first, it establishes a standardized framework for territorial information management based on ISO 19152; second, it provides municipalities with a practical tool to strengthen disaster preparedness and response, promoting more resilient and sustainable communities. Full article

As climate change increases the frequency and unpredictability of natural hazards, adapting critical infrastructure is crucial for long-term resilience. Among these hazards, embankment fires pose a growing threat to railway systems, particularly under rising temperatures and prolonged drought conditions. As part of the Horizon Europe project NATURE-DEMO, this study helps identify fire-prone rail segments and explore nature-based solutions, such as vegetation barriers, that can reduce ignition risk and enhance infrastructure resilience. In a case study, we analysed the risk of embankment fires for a section of the German railway network in detail. Based on an embankment-fire hazard indication map available for the entire German railway network, five hotspots within the study area were identified. Embankments with high fire susceptibility occur in both rural and urban areas, covering 1.1% of the study area. On the basis of published research on technical and nature-based solutions for reducing embankment fire susceptibility, we derived site-specific recommendations for the appropriate implementation of mitigation measures. Full article

The Philippines, located along the Pacific Ring of Fire, is highly susceptible to significant seismic activity arising from the active convergence of major tectonic plates. These seismic events often induce ground shaking intense enough to trigger soil liquefaction, particularly in geologically sensitive regions such as Davao del Sur. This study presents a nonlinear static and dynamic analysis of a mat foundation for a proposed midrise building located within the liquefaction-prone zone of Padada, Davao del Sur. Geotechnical data were obtained through rotary drilling and Standard Penetration Tests (SPTs), which provided the basis for developing the numerical model. Liquefaction assessment was conducted using the PLAXIS Liquefaction Model (UBC3D-PLM), confirming that the site adjacent to the Padada–Mainit River exhibits a high liquefaction potential. Subsequently, finite element analyses were performed in PLAXIS 3D using ground motion records from the 2013 Bohol Earthquake, scaled to 1.0 g, and modeled under the Hardening Soil Model with Small-Strain Stiffness (HSsmall). Results showed excess pore pressure ratios approaching 1, and vertical displacements of the mat foundation exceed 100 mm. These results suggest severe degradation in soil strength, as well as reduced friction angles and mobilized pressure. Full article

Persistent forest fire refugia are areas within fire-prone landscapes that remain fire-free over long periods of time and are crucial for ecosystem resilience. Modelling to develop maps of these refugia is key to informing fire and land use management. We predict persistent forest fire refugia using variables linked to the fire triangle (aspect, slope, elevation, topographic wetness, convergence and roughness, solar irradiation, temperature, surface wind direction, and speed) in machine learning algorithms (Random Forest, XGBoost; two ensemble models) and K-Nearest Neighbour. All models were run with and without ADASYN over-sampling and grid search hyperparameterisation. Six iterations were run per algorithm to assess the impact of omitting variables. Aspect is twice as influential as any other variable across all models. Solar radiation and surface wind direction are also highlighted, although the order of importance differs between algorithms. The predominant importance of aspect relates to solar radiation received by sun-facing slopes and resultant heat and moisture balances and, in this study area, the predominant fire wind direction. Ensemble models consistently produced the most accurate results. The findings highlight the importance of topographic and microclimatic variables in persistent forest fire refugia prediction, with ensemble machine learning providing reliable forecasting frameworks. Full article

Forest sub-surface fires represent a challenging combustion phenomenon to control, and the process of smoldering is often overlooked in wildfire incidents. Traditional forest fire research has prioritized flaming combustion over smoldering dynamics, despite its critical risk factors including sustained high temperature and ground surface collapse that significantly endanger firefighter safety. This study focuses on The Daxing’an Mountains, a prime sub-surface fire-prone region in China, employing field investigations and controlled smoldering experiments to quantify the key risk factors for sub-surface fires suppression while elucidating moisture content’s regulatory effects. The results demonstrate that sub-surface smoldering fires maintain elevated temperatures with the surface peak temperature reaching 600.24 °C and sub-surface peak temperature up to 710.70 °C. The spread rate is relatively slow (maximum 27.00 cm/h), yet exhibits pronounced fluctuations along the vertical profile, creating a critical predisposition to overhanging collapse. The moisture content has extremely significant effects (p < 0.01) on key risk factors including surface temperature, sub-surface temperature, collapse time and ignition duration. Lower moisture content prompted earlier surface collapses, whereas higher moisture content displays delayed collapse but resulted in dangerously elevated temperatures at collapse points, presenting extreme suppression risks. Full article