Search Results (209)

Wildfires are a major factor influencing vegetation dynamics and biogeochemical cycles. Soil moisture (SM) and vegetation optical depth (VOD) control fuel availability and flammability, but their interactions and feedbacks with wildfire dynamics on large spatial scales remain insufficiently understood. To investigate soil and vegetation water dynamics in the vicinity of wildfires, we employ a multi-sensor remote sensing approach that combines long-term (2000–2020) microwave-based SM and VOD data with optical fire observations across Africa. In addition to characterizing regional SM and VOD anomaly patterns in the vicinity of fire activity, this study also examines whether wildfire alters the coupling between SM and VOD dynamics during dry-down periods—an aspect that has not yet been addressed at the continental scale. Our results reveal strong regional differences in pre-fire trajectories: in the Southern Sahel, both variables exhibit positive anomalies 5–6 months before a fire, indicating above-average conditions associated with vegetation growth, whereas Southern Africa exhibits a continuous decline prior to the fire. Across varying land cover classes, regions with sparse vegetation show a multi-year increase in SM and VOD prior to fires, while areas with high biomass do not exhibit long-term fuel accumulation. Our post-fire analysis reveals an accelerated loss of SM and enhanced recovery of VOD during comparable initial conditions. These results demonstrate that wildfires not only alter the soil and vegetation water states but also modify the coupling between SM and VOD during dry-down periods. The drying of SM and the gain of VOD following a fire are accelerated, indicating intensified water exchange between the soil and vegetation, as well as likely faster SM uptake by post-fire vegetation. This leads to temporary shifts in the ecohydrological functioning of African ecosystems. Full article

High-latitude regions are witnessing unprecedented wildfires and accelerated warming. This study explored wildfire patterns and land changes within the high-latitude Indigenous Tłı̨chǫ territory in the Northwest Territories, Canada. It used the Two-Eyed Seeing approach, which combines Western science, or Scientific Ecological Knowledge (SEK), with Indigenous knowledge, or Traditional Ecological Knowledge (TEK). This method integrated Earth observation data with insights from Tłı̨chǫ Elders and officials. We analyzed spatiotemporal variations in burned areas, land surface temperature (LST), albedo, snow cover, soil moisture, and land-cover types. A listening and storytelling session with community Elders provided an in-depth Indigenous perspective. Our findings indicate a concerning shift in wildfire activity on Tłı̨chǫ land, primarily driven by the interplay between climate change and land-cover changes. Land-cover estimates over the past fifteen years indicate that nearly half of the forested areas on Tłı̨chǫ-owned land have been converted to other land-cover types, with shrublands increasing twofold and grasslands expanding tenfold. We observed significant increases in spring and summer LSTs (p < 0.05), alongside decreases in precipitation and snow cover (p < 0.05), consistent with the Elders’ observations. The decline in topsoil moisture, coupled with rising temperatures, has triggered a positive feedback loop in forested areas, intensifying future wildfire risk. The study’s implications extend beyond the Tłı̨chǫ territory, suggesting a broader significance for climate resilience and Indigenous stewardship. It highlights the significance of place-based, integrated research for understanding complex wildfire behavior and land-cover transformations. The study indicates that the Two-Eyed Seeing approach, which weaves local Indigenous knowledge with quantitative Earth observations, not only improves analytical precision but also provides a collaborative framework for developing targeted strategies to mitigate the effects of increasingly severe fire regimes and land-cover changes. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Non-food cropping provides a practical strategy for the safe utilization of severely cadmium (Cd)-contaminated farmland. In this study, a field experiment was conducted to evaluate the effectiveness of layered double hydroxides (LDHs) in reducing Cd transfer from soil to Artemisia argyi, a plant used for non-food applications, and to estimate Cd release potential during moxa burning. Our results demonstrated that the application of LDHs increased soil pH and decreased the extractable Cd concentration based on CaCl₂ extraction, suggesting a reduction in Cd bioavailability. Furthermore, BCR fractionation analysis indicated a shift of Cd from more active to more stable forms, further supporting the reduction in Cd bioavailability in the soil. SEM–EDS and FTIR confirmed the lamellar morphology, CaAl composition, and hydroxyl-rich functional groups of the LDH conditioner. Plant growth was not negatively affected by LDH treatment, and Cd concentrations in roots, stems, and leaves were significantly reduced. LDHs also reduced Cd levels in processed moxa and the mass-balance-based estimate of Cd release during combustion. These findings suggest that LDHs application may help reduce Cd transfer in non-food cropping systems on severely contaminated farmland. Full article

Wildfires severely disrupt soil nitrogen (N) cycling, yet the mechanisms driving this disruption in fragile karst forest ecosystems remain poorly understood. We investigated how wildfires affect soil N transformation dynamics and the microclimatic drivers of these dynamics in a karst forest. Using an in situ paired burned versus unburned plot design, we evaluated post-fire soil physicochemical properties, N fractions, and N-acquiring enzyme activities in the 0–10 cm soil layer. Wildfires significantly deteriorated the soil microenvironment, increasing mean soil temperature by 9.93% and bulk density by 36.66%, while sharply reducing soil water content, porosity, and saturated hydraulic conductivity. Consequently, the fires severely depleted total and organic soil N pools. Furthermore, N-acquiring enzymes (urease, protease, nitrate reductase, and nitrite reductase) initially declined in activity before gradually recovering. Notably, partial least squares structural equation modeling (PLS-SEM) revealed a fundamental shift in the drivers of nitrogen transformation. In unburned soil, abiotic climatic factors regulated N dynamics. After wildfire, enzyme-mediated biological processes controlled N dynamics, and these processes were constrained by altered soil physics. Restoring soil physical structure and stimulating enzymatic mineralization are therefore critical, rate-limiting steps for the recovery of soil N reservoirs in fire-prone karst landscapes. Full article

Wildland fire scientists have made substantial advances in measuring fire behavior, but properly collecting data is often beyond the capacity of prescribed fire managers and by definition all but impossible for wildfire events. While a method for the immediate assessment of burn severity has been developed around multispectral imagery from space-based Earth observation systems, there has been little comparison of these post hoc metrics to actual fire behavior. Meanwhile, the application of research results from experimental prescribed burns to rangeland affected by wildfire can be impeded by a lack of understanding of how immediate burn severity differs between wildfires and prescribed burns, especially in rangelands. Overall, much of what is known about wildland fire behavior, severity, and effects comes from forests, whereas rangelands are characterized by having lower fuel loads comprised of fine vegetation that promotes high rates of spread and brief residence time. This paper provides rangeland-specific information on the relationships between direct field-based fire behavior measurements and a space-based index of burn severity (differenced Normalized Burn Ratio, ΔNBR, from Sentinel-2 imagery), and uses those data to compare burn severity across 54 prescribed burns in North Dakota, USA, and 28 nearby wildfires in the US Northern Great Plains. In prescribed burns, remotely sensed burn severity increased with rate of spread and flame temperature 15 cm above the ground, but had no statistically significant relationship with soil surface temperature. In the semi-arid western zone of the Northern Great Plains, wildfires and prescribed burns had similar, low–moderate severity; wildfires in the eastern zone tended to be of moderately high severity and thus greater than the low severity of the experimental prescribed burns. By describing meaningful gradients in surface fire behavior in rangelands with ΔNBR, even those without the capacity to measure fire behavior in the field can monitor prescribed fire effectiveness and incorporate burn severity in adaptive management plans. Understanding the relationship between burn severity across wildfires and prescribed burns is a critical step in applying knowledge gained from research on prescribed fires to areas impacted by wildfire. Resistance to prescribed burning might be overcome by increasing livestock managers’ experience with post-fire forage resources through grazing areas burned in unintentional wildfires, but current practice and policy discourage or outright prevent ranchers from doing so. Future research ought to connect burn severity with ecosystem recovery metrics to ensure post-fire grazing does not impair rangeland sustainability. Full article

Agricultural crop residue burning is a major driver of seasonal PM2.5 pollution and greenhouse gas (GHG) emissions in Northern Thailand. This study quantified GHG emissions from the open burning of rice, maize, and sugarcane residues across six provinces (Chiang Mai, Mae Hong Son, Lampang, Uttaradit, Nakhon Sawan, and Kamphaeng Phet) from 2019 to 2024 using the 2006 IPCC emission methodology. Spatiotemporal patterns of fire hotspots were characterized using MODIS and VIIRS satellite data, combined with kernel density estimation (KDE) and land-use classification in ArcGIS Pro. Total non-CO₂ GHG emissions (CH₄ and N₂O, expressed as CO₂-eq using GWP100 from IPCC AR5) over the six years totaled 2,599,551 tCO₂-eq, with major rice contributing the largest share (35%), followed by sugarcane (24%), second rice (21%), and maize (20%). Nakhon Sawan was the leading emitter (41%), reflecting its extensive rice and sugarcane cultivation. Pearson correlation analysis revealed consistently positive relationships between daily fire hotspot counts and PM2.5 concentrations (r = 0.30–0.84), with the strongest correlations observed in Mae Hong Son, where basin topography traps pollutants. Time-series analysis confirmed pronounced seasonal PM2.5 peaks that exceeded Thailand’s 24-h NAAQS limit (37.5 μg/m³) by 7–9 times in severe years. Biochar production via pyrolysis was evaluated as a zero-burning alternative, with an estimated annual carbon sequestration potential of 2.3–3.5 million tCO₂-eq, substantially exceeding emissions from open burning. These findings indicate that crop-residue valorization options—including biochar production, composting, and biochar co-compost—could theoretically offset agricultural GHG emissions and reduce field-burning PM2.5 emissions in Northern Thailand. However, the realized mitigation will depend on (i) verification of biochar long-term stability in tropical Thai soils through dedicated in situ trials, (ii) economic incentives that offset biochar production costs of approximately 1500–3500 THB per tonne, and (iii) integration within a policy mix that combines burning bans, mechanization support, and farmer extension services. Without these enabling conditions, biochar should be regarded as a future-perspective option rather than an immediately deployable solution. Full article

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

Wildfires are among the key drivers of transformation in boreal ecosystems; however, the mechanisms of postfire recovery in the arid regions of Eurasia remain insufficiently understood. The aim of this study was to identify the role of burn severity and associated edaphic and hydrological factors in shaping the natural regeneration trajectories of Scots pine forests in the Kostanay region of northern Kazakhstan. This study is based on the integration of field data on seedling regeneration and soil conditions with the analysis of long-term satellite-derived indices (NDVI, NDMI, and NBR). Sample plots were grouped according to fixed burn severity classes, which enabled a consistent statistical comparison and reduced the interpretative ambiguity that has characterized previous studies in the region. The results indicate that pine forest regeneration is most successful under low and moderate burn severity, where seed sources are preserved and favourable moisture conditions are maintained. In contrast, high burn severity leads to a reduction in regenerative potential and a shift in recovery trajectories toward deciduous or sparsely vegetated communities. The spectral indices derived from the remote sensing data strongly agreed with the field-based indicators, confirming their suitability for assessing postfire vegetation dynamics. Soil properties act as important modifying factors of recovery processes, particularly under conditions of limited water availability. These findings enhance the current understanding of postfire recovery mechanisms in the arid part of the boreal zone and highlight the need for differentiated management of postfire landscapes. The integration of field observations with remote sensing data provides a robust framework for monitoring and forecasting recovery processes under an increasingly intensified fire regime. Full article

The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field observations (2022–2025) across 24 plots with four burn severities. The Penman–Monteith–Leuning model provided physically based benchmarks. Results revealed three distinct recovery phases: destruction/stagnation (0–7 months, ET at 6%–10% of pre-fire levels), rapid recovery (8–19 months), and stabilization (20–37 months, reaching 100% ET recovery). The coupled LSTM–Transformer ensemble achieved superior performance (RMSE = 0.10 mm·day⁻¹, NSE = 0.98), outperforming single models by 31% in uncertainty reduction. SHAP analysis identified phase-dependent factor shifts: soil water content dominated Stage I (42.5%), while leaf area index (LAI) controlled Stages II–III (>48%). A bimodal LAI time-lag effect emerged: 4–7 days (leaf water potential equilibrium, 27.7% contribution) and 8–14 days (root uptake compensation, 21.7%). Burn severity significantly extended time-lags (severe burns: 12/21 days vs. unburned: 5/12 days), indicating hydraulic system reconstruction requirements. Despite equivalent LAI recovery, severe burns maintained 12%–15% ET reduction, suggesting lasting hydraulic limitations. This study demonstrates that physics-constrained AI models effectively capture complex post-fire ecohydrological dynamics while providing mechanistic interpretability, advancing understanding of vegetation–water coupling reconstruction under increasing fire frequency. Full article

Water from mountain regions is a crucial natural resource because of its major economic, social, and environmental significance. Wildfires may disrupt the normal functioning of the hydrological cycle, limiting water resources for nearby areas and degrading water quality in mountainous regions as contaminants enter water systems from the burning of vegetation and soil. In August 2022, the Serra da Estrela mountain, situated in the Mediterranean biogeographical region, was affected by a large wildfire that consumed 270 km² of the Serra da Estrela Natural Park, often resulting in severe vegetation burn, although the soil burn severity was low to moderate in most of the area. The research objective is to assess the impact of this wildfire on the hydrogeochemistry of groundwater and surface water in the Manteigas-Covão da Ametade sector of Serra da Estrela in the context of a wildfire with limited soil burn severity. Groundwater and surface water samples were collected from October 2022 to September 2023 and were analyzed for pH, Total Organic Carbon, electrical conductivity, major ions, potentially toxic elements, iron (Fe), and Polycyclic Aromatic Hydrocarbons. A stormy event in mid-September 2022, occurring before the first sampling campaign, removed most of the ash layer and likely caused transient hydrogeochemical changes in streams. However, the analytical results from the sampled waters revealed that the post-wildfire hydrogeochemical effects are not evident. In fact, the hydrogeochemical changes observed in groundwater and surface water appear to be primarily influenced by the regular hydrological behaviour of aquifers and streams. The low to moderate soil burn severity, the high soil hydrophobicity, and the temporal distribution of precipitation explain why the hydrogeochemistry was primarily influenced by groundwater flow paths, the types and weathering of local lithologies, soil types, dilution effects following wet periods, and seasonal changes in the tributaries feeding into streams, rather than by post-wildfire effects. These outcomes provide valuable insights for water resource management and for developing strategies to mitigate wildfire impacts in mountainous environments. Full article

Assessing post-fire disturbance in Mediterranean ecosystems is essential for quantifying ecological impacts and guiding restoration strategies. This study evaluates fire severity following an extreme wildfire event (~28,000 ha) in northwestern Spain using vegetation indices (VIs) derived from PRISMA hyperspectral imagery, validated against field-based Composite Burn Index (CBI) measurements at the vegetation, soil, and site levels across three vegetation formations (coniferous forests, broadleaf forests, and shrublands). Hyperspectral VIs were benchmarked against multispectral VIs derived from Sentinel-2. Hyperspectral VIs yielded stronger correlations with CBI values than multispectral VIs. Vegetation-level CBI showed the highest correlations, reflecting the sensitivity of most VIs to canopy structural and compositional changes. Indices incorporating red-edge, near-infrared (NIR), and shortwave infrared (SWIR) bands demonstrated the greatest explanatory power. Among hyperspectral indices, DVIRED, EVI, and especially CAI performed best. For multispectral data, NDRE, CIREDGE, ENDVI, and GNDVI were the most effective. These findings highlight the strong potential of hyperspectral remote sensing for accurate, scalable post-fire severity assessment in heterogeneous Mediterranean ecosystems. 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

Wildfire-induced disturbances to soil and vegetation can significantly impact streamflows for years, depending upon the degree of burn severity. Accurately predicting the effects of wildfire on streamflow at the watershed scale is essential for effective water budget management. This study presents a novel approach to generating a burn severity map on a small scale by integrating unmanned aerial vehicle (UAV)-based thermal imagery with Landsat-derived Differenced Normalized Burn Ratio (dNBR) and upscaling burned severity to the entire burned area. The approach was applied to the Thompson Ridge Fire perimeter, and the upscaled UAV-Landsat-based burn severity map achieved an overall accuracy of ~73% and a kappa coefficient of ~0.62 when compared with the Burned Area Emergency Response’s (BAER) fire product as a reference map, indicating moderate accuracy. We then tested the transferability of burn severity information to a Beaver River watershed by applying Random Forest models. Predictors included topography, spectral bands, vegetation indices, fuel, land cover, fire information, and soil properties. We calibrated and validated the Distributed Hydrology Soil Vegetation Model (DHSVM) against observed streamflow and Snow Water Equivalent (SWE) data within the Beaver River watershed and measured model performance using Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS) metrics. We adjusted soil (maximum infiltration rate) and vegetation (fractional vegetation cover, snow interception efficiency, and leaf area index) parameters for the post-fire model setup and simulated streamflow for the post-fire years without vegetation regrowth. Streamflow simulations using the upscaled and transferred UAV-Landsat burn severity map and the Burned Area Emergency Response’s (BAER) fire product produced similar post-fire hydrologic responses, with annual average flows increasing under both approaches and the UAV-Landsat-based simulation yielding slightly lower values, by less than 6% compared to the BAER-based simulation. Our results demonstrate that the UAV-satellite integration method offers a cost- and time-effective method for generating a burn severity map, and when combined with the transferability method and hydrologic modeling, it provides a practical framework for predicting post-fire streamflow in both burned and unburned watersheds. Full article