Search Results (2,382)

Soil organic carbon (SOC) is essential for maintaining ecosystem health, and its depletion is widely recognized as a key indicator of soil degradation. Activities such as mining and wildfire disturbances significantly intensify soil degradation, leading to quantitative and qualitative declines in SOC. Accurate SOC monitoring is critical, yet traditional methods are often costly and time-intensive. Advances in technologies like Unmanned Aerial Vehicles (UAVs) and satellite remote sensing (SRS) now offer efficient and scalable alternatives. Combining UAV and satellite data through machine learning (ML) techniques can improve the accuracy and spatial resolution of SOC monitoring, facilitating better soil management strategies. In this context, this study proposes a methodology that integrates geochemical data (SOC) with UAV-derived information, upscaling the UAV data to satellite platforms (GEOSAT-2 and SENTINEL-2) using ML techniques, specifically random forest (RF) algorithms. The research was conducted in two distinct environments: a reclaimed open-pit coal mine, representing a severely degraded ecosystem, and a high-altitude region prone to recurrent wildfires, both characterized by extreme environmental conditions and diverse soil properties. These scenarios provide valuable opportunities to evaluate the effects of soil degradation on SOC quality and to assess the effectiveness of advanced monitoring approaches. The RF algorithm, optimized with cross-validation (CV) techniques, consistently outperformed other models. The highest performance was achieved during the UAV-to-SENTINEL-2 upscaling, with an 𝑅² of 0.761 and an rRMSE of 8.6%. Cross-validation mitigated overfitting and enhanced the robustness and generalizability of the models. UAV data offered high-resolution insights for localized SOC assessments, while SENTINEL-2 imagery enabled broader-scale evaluations, albeit with a smoothing effect. These findings underscore the potential of integrating UAV and satellite data with ML approaches, providing a cost-effective and scalable framework for SOC monitoring, soil management, and climate change mitigation efforts. Full article

This study examined atmospheric mechanisms affecting the East Bay Hills Fire (1991) in Oakland, California, using the Advanced Weather Research and Forecasting (WRF) model and North American Regional Reanalysis (NARR) dataset. High-resolution WRF simulations, initially at 16 km, were downscaled to 4 km and 1 km for analyzing primary and secondary circulations at synoptic and meso-α/meso-β scales, respectively, before the fire. Additionally, the interaction between the synoptic-scale and mesoscale environments was examined using backward trajectories derived from NARR data. The findings reveal that a strong pressure gradient created by a ridge over the Great Basin and a trough off the Pacific coast generated favorable meso-α conditions for the hot, dry northeasterly winds, known as “Diablo winds”, which initiated the wildfire in northern California. Mountain waves, enhanced by jet stream dynamics, contributed to sinking air on the Sierra Nevada’s western slopes. The main conclusion is that jet circulation did not directly transport warm, dry air to the fire but established a vertical atmospheric structure conducive to wave amplification and breaking and downward dry air fluxes leading to the necessary warm and dry low-level air for the fire. The hot–dry–windy (HDW) fire weather index further confirmed the highly favorable fire weather conditions. Full article

Wildfire is a key factor in regulating ecological processes in grassland ecosystems; however, changes in land use/cover have modified the intensity and frequency of fires as they occurred naturally. Different factors have caused a rise in woody vegetation in these ecosystems, leading to changes in species composition, diversity, and biogeochemical cycles. Prescribed burns are a tool for controlling and eradicating shrubs; however, their effectiveness depends on vegetation composition, biomass availability, and the objectives of restoration. We evaluated the effectiveness of fire as a shrub controller in a semi-arid grassland ecosystem. We measured several shrub dasometric parameters and the percentage of damage in ten 2000 m² plots three months after a prescribed burning was performed. Both crown height and width and total height were the main variables that explained the percentage of shrub damage by fire. Individuals with a height greater than 1.6 m and wide crowns did not suffer damage. Moreover, even though 97% of the total shrubs presented some fire damage, 86% recovered after the rain period. Our results show that fire could be an effective strategy to control early-growing shrubs, but on overgrazed arid lands it would be difficult to have enough biomass to implement burning programs. Full article

Black carbon (BC) aerosols emitted from biomass, fossil fuel, and waste combustion contribute to the radiation budget imbalance and are transported over extensive distances in the Earth’s atmosphere. These aerosols undergo physical and chemical modifications with co-existing aerosols (e.g., nitrate, sulfate, ammonium) through aging processes during long-range transport and are primarily removed from the troposphere by wet deposition. Using precipitation samples collected in North America between 26 October and 1 December 2020 by the National Atmospheric Deposition Program (NADP), we investigated the relationships between BC and both water-soluble ions and water-soluble organic carbon (WSOC) using Spearman’s rank coefficients. We then attempted to identify the sources of BC in the wet deposition using factor analysis (FA) and satellite data of fire smoke. BC showed a very strong correlation with nitrate (ρ = 0.83). Strong correlations were also found with WSOC, ammonium, calcium, and sulfate ions (ρ = 0.78, 0.74, 0.74, and 0.67, respectively). FA showed that BC was in the same factor as nitrate, ammonium, sulfate, and WSOC, indicating that BC could originate from secondary aerosol formation and biomass burning. Supported by satellite data of fire and smoke, BC and other correlated pollutants were believed to be associated with wildfire outbreaks in several states in the United States (US) during November 2020. Full article

Seasonally dry mature and old-growth (MOG) forests in the western USA face increasing threats from catastrophic wildfire and drought due to historical fire exclusion and climate change. The Emerald Point forest at Lake Tahoe in the Sierra Nevada of California, one of the last remaining old-growth stands at lake level, is at high risk due to elevated fuels and tree densities. The stand supports huge trees and the highest tree diversity in the Lake Tahoe Basin and protects important raptor habitat. In this study, we simulate forest response to vegetation management and wildfire to assess the impacts of four fuel-reduction scenarios on fire behavior and stand resilience at Emerald Point. Results: Our results demonstrate that restorative forest management can greatly improve an MOG forest’s resistance to catastrophic fire. Thinning to the natural range of variation for density, basal area, and fuel loads, followed by a prescribed burn, was most effective at reducing large-tree mortality, maintaining basal area, and retaining live tree carbon post-wildfire, while reducing secondary impacts. Conclusions: Our findings highlight the value of proactive management in protecting old-growth forests in seasonally dry regions from severe fire events, while also enhancing their ecological integrity and biodiversity. Full article

This paper aims to assess the temporal and spatial variability of particulate matter (PM) concentrations (PM_2.5 and PM₁₀) at several rural and urban monitoring sites located in Portugal between 2011 and 2022. The exceedances to European Union Directive limits and World Health Organization (WHO) air quality guidelines were also evaluated. Higher PM concentrations were observed mainly at urban sites (e.g., up to 156 exceedances of the WHO PM_2.5 guideline for daily average concentrations were recorded in a year), with the main contributions being from traffic emissions and industrial activities. On the other hand, the lower number of exceedances at rural sites can be attributed to long-range transport (e.g., Saharan dust) and wildfires. Temporal trends showed that PM_2.5 concentrations decreased by up to 0.6 µg/m³ per year, while PM₁₀ reductions reached 1.0 µg/m³ per year at certain sites, showing the effectiveness of air quality policies and clean technology advancements. Also, the number of exceedances of the air quality guideline of WHO for PM_2.5 at urban traffic sites like Entrecampos decreased from 140 in 2015 to 15 in 2022. Principal component analysis grouped the air monitoring sites based on PM variability. These findings provide a comprehensive understanding of the temporal variation of PM concentration, contributing to air quality management strategies and the design of mitigation measures. Full article

Analyzing wildfire behavior is crucial due to its significant environmental repercussions. Among the various influencing factors, terrain slope and wind velocity are pivotal in governing fire spread characteristics. In the present study, we investigate the influence of negative terrain slopes (up to −45°), backward wind velocities (up to 2 m/s), and their combined effects on the surface fire spread rate using the Wildland-Urban Fire Dynamics Simulator (WFDS). Wind velocity in backward flows reduces the rate of spread by 40% at 30° angles, primarily due to the suppression of radiative heat transfer leading to reduced preheating unburnt areas. However, this effect reduces on lower slopes. The key findings reveal a significant increase in fire intensity and the rate of spread when the terrain slope exceeds 20°. The fire front shape evolves from a relatively flat rounded U-shape to a V-shape; it is shown that a downward slope slightly affects the spread rate, and the fire front shape stays flat. Full article

Wildfires increasingly threaten forest ecosystems, particularly in arid Mediterranean regions impacted by climate change. This study presents a novel quantitative framework for optimizing silvicultural interventions to reduce combustion energy loads and enhance resource conservation. Using dendrometric equations, biomass removal calculations, and geospatial modeling (Ordinary Kriging, SAGA-GIS and Q-GIS), the methodology evaluates the spatial distribution of calorific energy before and after thinning interventions. The results show that a 20% thinning intervention reduced calorific energy by 13.45% and water demand by 8.38%, while thinning at 30% and 40% intensities achieved even greater reductions. Specifically, thinning reduced the higher calorific potential by 14,000 kJ/m² and saved approximately 861,390 L of water across the study area. These findings provide actionable insights for forest managers to balance ecological health, optimize thinning practices, and mitigate wildfire risks in vulnerable ecosystems. Full article

Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the integration of dense vegetation and merged canopies into three-dimensional (3D) models for fire modelling software poses significant challenges. This study proposes a method for integrating the UAS–LiDAR-derived geometric features of vegetation components—such as bark, wooden core, and foliage—into heat transfer models. The data were collected from the natural woodland surrounding an elevated building in Samford, Queensland, Australia. Aboveground biomass (AGB) was estimated for 21 trees utilizing three 3D tree reconstruction tools, with validation against biomass allometric equations (BAEs) derived from field measurements. The most accurate reconstruction tool produced a tree mesh utilized for modelling vegetation geometry. A proof of concept was established with Eucalyptus siderophloia, incorporating vegetation data into heat transfer models. This non-destructive framework leverages available technologies to create reliable 3D tree reconstructions of complex vegetation in wildland–urban interfaces (WUIs). It facilitates realistic wildfire risk assessments by providing accurate heat flux estimations, which are critical for evaluating building safety during fire events, while addressing the limitations associated with direct measurements. Full article

Wildfires in North America, particularly in western states, have caused widespread environmental, economic, social, and health impacts. Smoke from these fires travels long distances, spreading pollutants and worsening the air quality across continents. Vulnerable groups, such as children, the elderly, and those with preexisting conditions, face heightened health risks, as do firefighters working in extreme conditions. Wildfire firefighters are of particular concern as they are fighting fires in extreme conditions with minimal protective equipment. This study examined wildfire smoke during July–August 2021, when intense fires in Canada and the western U.S. led to cross-continental smoke transport and caused significant impacts on the air quality across North America. Using the GEOS-Chem model, we simulated the transport and distribution of PM_2.5 (particulate matter with a diameter of 2.5 μm or smaller), identifying significant carcinogenic risks for adults, children, and firefighters using dosimetry risk methodologies established by the U.S. EPA. Significant carcinogenic risks for adult, child, and firefighter populations due to exposure to PM_2.5 were identified over the two-month period of evaluation. The findings emphasize the need for future studies to assess the toxic chemical mixtures in wildfire smoke and consider the risks to underrepresented communities. Full article

Wildfires are a recurrent and intensifying natural hazard in Mediterranean regions like Greece, driven by prolonged heatwaves, evolving climatic conditions, and human activities. This study leverages Sentinel-2 satellite imagery and Copernicus geospatial data to assess four early-season wildfire events during May and June 2024, which collectively affected 43.44 km². Burn severity, land cover, and tree cover density were analyzed to evaluate the spatial and environmental impacts of these fires. Validation against Copernicus Emergency Management Service (CEMS) data yielded an overall accuracy of 95.79%, confirming the reliability of the methodology. The Achaia-Ilia wildfire, spanning 40.55 km², exhibited the highest severity, with 26.93% classified as moderate to high severity. Smaller fires, such as Katsimidi (0.66 km²) and Stamata (1.41 km²), revealed the influence of vegetation type and density on fire dynamics, with Stamata’s sparse tree cover mitigating fire spread. The findings highlight the utility of remote sensing technologies for wildfire monitoring, and underscore the need for tailored management strategies, from vegetation control to urban planning, to enhance ecosystem resilience and mitigate wildfire risks in Mediterranean landscapes. Full article

Wildfires in the United States have increased in frequency and severity over recent decades, driven by climate change, altered weather patterns, and accumulated flammable materials. Accurately forecasting the Fire Weather Index (FWI) is crucial for mitigating wildfire risks and protecting ecosystems, human health, and infrastructure. This study analyzed FWI trends across the Continental United States (CONUS) from 2014 to 2023, using meteorological data from the gridMET dataset. Key variables, including temperature, relative humidity, wind speed, and precipitation, were utilized to calculate the FWI at a fine spatial resolution of 4 km, ensuring the precise identification of wildfire-prone areas. Based on this, our study developed a hybrid modeling framework to forecast FWI over a 14-day horizon, integrating Graph Neural Networks (GNNs) with Temporal Convolutional Neural Networks (TCNNs), Long Short-Term Memory (LSTM), and Deep Autoregressive Networks (DeepAR). The models were evaluated using the Index of Agreement (IOA) and root mean squared error (RMSE). The results revealed that the Southwest and West regions of CONUS consistently exhibited the highest mean FWI values, with the summer months demonstrating the greatest variability across all climatic zones. In terms of model performance on forecasting, Day 1 results highlighted the superior performance of the GNN-TCNN model, achieving an IOA of 0.95 and an RMSE of 1.21, compared to the GNN-LSTM (IOA: 0.93, RMSE: 1.25) and GNN-DeepAR (IOA: 0.92, RMSE: 1.30). On average, across all 14 days, the GNN-TCNN outperformed others with a mean IOA of 0.885 and an RMSE of 1.325, followed by the GNN-LSTM (IOA: 0.852, RMSE: 1.590) and GNN-DeepAR (IOA: 0.8225, RMSE: 1.755). The GNN-TCNN demonstrated robust accuracy across short-term (days 1–7) and long-term (days 8–14) forecasts. This study advances wildfire risk assessment by combining descriptive analysis with hybrid modeling, offering a scalable and robust framework for FWI forecasting and proactive wildfire management amidst a changing climate. Full article

By developing a conceptual framework that integrates the use of fire in agricultural activities, the occurrence of wildfires, and the perception of wildfire risk, this article examines the interplay among these three elements within both wet and dry Puna grasslands. The analysis focuses on two peasant and agropastoral communities, Vilcabamba and Apachaco, both located in the Cusco region—an area with the highest incidence of wildfires in Peru. This study highlights the sociocultural significance and persistence of agricultural burnings within Puna agropastoral communities and the necessity of considering changes in agricultural activity, mutual aid systems, and communal institutions—particularly regarding land ownership—to understand the factors contributing to wildfire occurrence. Furthermore, it reveals the widespread recognition of wildfire risk among community members, who are acutely aware of both the likelihood and potential severity of wildfire events, while governmental policies aimed at addressing this hazard predominantly focus on raising awareness and enforcing bans on agricultural burning, with limited consideration of these complex sociocultural dynamics. Full article

Quercus longispica is a dominant shrub in the Himalayan subalpine region, demonstrating high levels of persistence despite high seed predation and extreme climatic conditions. However, its seed germination ecology and adaptations for seedling recruitment remain poorly understood. This study investigated the effects of temperature, water potential, and insect damage on seed germination and seedling establishment. Pre-germination seed traits and seed-to-seedling ontogeny were systematically analyzed. Our results demonstrated that seed germination percentages decreased with increasing insect damage across all temperature and water potential treatments. Cool temperatures (5–10 °C) yielded the highest germination percentages, potentially due to the suppression of parasitoid activity and mildew growth. While drought conditions also suppressed parasitoid activity, they significantly increased seed mortality. Despite a decline in seedling performance with increasing seed damage, overall seedling establishment remained robust. Several adaptive traits enable Q. longispica to persist in its harsh environment. Multi-seeded, non-apical embryos combined with rapid germination help embryos evade or escape damage from parasitism and predation. The rapid elongation of cotyledonary petioles pushes the embryo axis into the soil, with rapid nutrient and water transfer from the cotyledon to the taproot, thereby avoiding the threats of predation, drought, cold, and wildfire. Additionally, temperature-regulated epicotyl dormancy at the post-germination stage prevents the emergence of cold-intolerant seedlings in winter. This study provides the first comprehensive description of seed-to-seedling ontogeny in this Himalayan subalpine oak, offering crucial insights into the adaptive mechanisms that facilitate successful seedling recruitment in the challenging subalpine habitats. Full article

Wildfires significantly alter watershed functions, particularly the mobilization of organic carbon (OC). This study investigated OC mobility and the physicochemical characteristics of wildfire-impacted soils and ashes from the northern California and Nevada fires (Dixie, Beckworth, Caldor). Organic carbon in wildfire-derived ashes (9.2–57.3 mg/g) generally exceeded levels in the background soils (4.3–24.4 mg/g), except at the Dixie fire sites. The mobile OC fraction varied from 0.0093 to 0.029 in ashes and 0.010 to 0.065 in soils, though no consistent trend was observed between the ashes and soils. Notably, the ash samples displayed lower OC mobility compared with the soils beneath them. A negative correlation was found between the mobile OC fraction and bulk OC content. Wildfire increased the total amount of mobile OC substantially by 5.2–574% compared to the background soils. Electron paramagnetic resonance (EPR) spectra confirmed the presence of environmentally persistent free radicals (EPFRs), which correlated with observed redox reactivity. Additionally, X-ray absorption near edge structure (XANES) and X-ray fluorescence (XRF) imaging revealed that Fe(II) oxidation in soils beneath the ashes may have enhanced the OC mobility, likely driven by pyrogenic carbon and free radicals. These findings enhance our understanding of post-wildfire OC mobilization and the impact of ash–soil physicochemical properties on watershed health. Full article