Search Results (396)

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

Post-fire rehabilitation structures are widely used in Mediterranean burned landscapes to reduce runoff and sediment transfer, yet their ecological associations with early vegetation recovery remain insufficiently documented. This observational study assessed vascular plant composition, species richness, vegetation cover, plant density, aboveground biomass, and soil properties across log barriers, wattles, and log dams in the burned landscape of Ancient Olympia, western Greece. The study area belongs to the humid climatic class of the United Nations Environment Programme (UNEP) aridity framework based on the Thornthwaite aridity index, providing a comparatively wetter Mediterranean post-fire context. Paired depositional and eroded microsites in operationally restored post-fire areas were monitored in 2022 and 2023. The sampling design comprised nine plots and 18 microsites (n = 9 plots, 18 microsites). Generalized estimating equations (GEE), change-score models, principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) were performed to examine associations of monitoring year, microsite condition and rehabilitation structure type with soil and vegetation patterns. A total of 27 vascular plant species belonging to 16 families were recorded. The average vegetation cover increased from 39.17 ± 21.44% in 2022 to 75.11 ± 12.90% in 2023. Model-based marginal estimates with 95% confidence intervals indicated a large positive increase in vegetation cover over this period. Further, rapid early recovery was indicated by large increases in species richness, plant density and biomass. Depositional microsites were associated with stronger recovery signals than eroded ones, characterized by a larger increase in vegetation cover, density, biomass and species richness. Among rehabilitation structures, log dams showed the highest cumulative floristic richness and a broader observed floristic spectrum, although the species-level contingency analysis provided only marginal evidence for structure-associated differences in floristic composition. Changes in selected soil properties including total nitrogen (total N), ammonium nitrogen (NH4-N), nitrate nitrogen (NO3-N), pH, electrical conductivity (EC), and exchangeable calcium (Ca), magnesium (Mg), and potassium (K), were detected between 2022 and 2023; the multivariate soil pattern was driven primarily by mineral nitrogen, pH, and EC. These findings suggest that, under operational post-fire restoration conditions, rehabilitation structures are associated not only with erosion-control functions but also with microsite differentiation that may shape early plant establishment and biodiversity recovery in Mediterranean burned landscapes. Full article

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference ($\Delta$GLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean $\Delta$GLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher $\Delta$GLI, while low severity areas showed limited or negative values. $\Delta$GLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery. Full article

Mediterranean-type forest ecosystems are becoming increasingly vulnerable to intensifying drought, threatening the resilience of even highly adapted ecosystems such as the Northern Jarrah Forest in south-western Australia. This study quantifies multi-decadal dynamics of canopy water stress using a 36-year multispectral satellite archive (1988–2024) and the newly developed Infrared Canopy Dryness Index (ICDI). We combined this spatiotemporal dataset with a MaxEnt-based risk assessment framework to identify the biophysical drivers of drought-induced canopy loss and to delineate high-risk zones under accelerating climate-forcing changes. Our results demonstrate a systematic spatial expansion of canopy dryness, paralleling a deteriorating regional climatic water balance. Hotspot analysis revealed a transition from localized, peripheral stress to widespread, chronic drought conditions across the landscape. The modelling achieved high diagnostic accuracy (AUC = 0.952), significantly outperforming conventional assessment methods. Regolith depth was identified as the primary determinant of drought-induced canopy collapse, followed by ICDI, NDVI, and slope. Crucially, high-biomass stands exhibited disproportionately higher risk of collapse, revealing a density-dependent vulnerability that suggests productive forests are approaching critical hydraulic thresholds. Conversely, lower-stature forests to the east of the study area demonstrated greater stability, likely due to reduced evapotranspirative demand. These findings provide robust spatial evidence for transitioning from reactive monitoring to proactive forest management. We conclude that targeted interventions, such as ecological thinning and prescribed burning in identified high-risk zones, are imperative to protect the forest and preserve the structural integrity of Mediterranean ecosystems in a drying climate. Full article

Fire is an essential component of species, ecosystems, and atmospheric dynamics. However, human activity has caused changes in fire regimes over the past two decades. In many cases, the spatial patterns of vegetation change after fire at the landscape scale remain unknown. The aim of this study was to evaluate spatial vegetation trends in burned areas across the Americas (2001–2024), using non-parametric tests and analyzing Normalized Difference Vegetation Index (NDVI) remote sensing products. Over a period of 24 years, fire activity burned a total area of 429.7 million hectares in 44 countries or territories and 269 ecoregions in the Americas. Regarding fire recurrence, the data indicates that 244.7 Mha (56.9%) burned only once (≤1), while 185.0 Mha (43.1%) burned multiple times (≥2), with certain regions experiencing up to 39 fires. The NDVI trend analysis showed that burned areas with increasing trends (p < 0.05) represented a total of 149.6 Mha (34.8%), primarily in Brazil (54.6 Mha, 12.7%), Argentina (17.8 Mha, 4.2%), the United States (14.4 Mha, 3.4%). In terms of decreasing NDVI trends (p < 0.05), these represented a total of 91.8 Mha (21.37%), primarily in Brazil (29.1 Mha, 6.8%), Canada (23.4 Mha, 5.4%), and the United States (14.2 Mha, 3.3%). The ecoregions with the largest areas showing increasing NDVI trends (p < 0.05) were the Cerrado (33.8 Mha, 7.8%), the Llanos (13.3 Mha, 3.1%) and the Humid Chaco (7 Mha, 1.6%). In contrast, the ecoregions with the largest areas showing decreasing NDVI trends (p < 0.05) were the Dry Chaco (9.2 Mha, 2.1%), the Cerrado (8.6 Mha, 2.0%), and the Boreal Shield (8.3 Mha, 1.9%). In terms of land cover types, savannas (37.2%) exhibited the highest proportions of increasing NDVI trends (p < 0.05), while decreasing trends were also present in savannas (28.0%) and grasslands (22.1%). Identifying spatiotemporal trends in vegetation change after fires is a fundamental step in implementing strategies and public policies to ensure ecological restoration. Moreover, given the high costs of restoration efforts, governments must work together to prevent these ecosystems from burning repeatedly. Full article

This study aimed to develop and compare prognostic models (logistic regression, Cox proportional hazards, and random forest) to assess delayed mortality in pine (Pinus densiflora) following fires in Korea. Data from a 72-month monitoring of 734 trees across four fire areas were used, accounting for 19 variables: tree size, fire severity, multispectral indices, topography, and bioclimatic variables. Key predictors of mortality included diameter at breast height (DBH), bark scorch index (BSI), delta normalized burn ratio (dNBR), slope, topographic wetness index (TWI), precipitation of the warmest quarter, temperature seasonality and isothermality. The key results indicate that the random forest model was the most effective (AUC = 0.924; sensitivity = 0.892) in identifying trees at high risk of mortality. These results suggest that nonlinear approaches are effective for predicting delayed mortality in fire-damaged pines and can support rapid decision-making in post-fire forest management and restoration under increasing wildfire risk. Full article

Methodological opacity and the omission of environmental variables in forest masks can generate biased estimates. The objective of this study was to validate a reproducible workflow for quantifying forest cover change in the area adjacent to Nevado de Colima over the 2019–2025 period, subdivided into nine assessment areas with standardized sampling based on 3 × 3 pixel kernels (900 m²). An enriched Random Forest model with slope and spectral indices (NDVI, NBR, NDWI-Gao, and BSI) classified six spectral combinations derived from Sentinel-2 L2A bands B2, B3, B4, B8, B11, and B12, together with a new index proposed in this study, Red-Enhanced Normalized Burn Ratio (RE-NBR), used as a conservative classifier and auxiliary classifier output in the probabilistic cross-check estimation. Validation employed thematic and areal metrics. All combinations reached OA values between 89.44% and 92.53% and Kappa values between 0.79 and 0.85, with Shortwave Infrared (B12, B8, B4) as the most consistent configuration across dates. Allocation disagreement systematically exceeded quantity disagreement on all dates. The Seasonal Stability Index increased from 0.73 in 2019 to 0.77 in 2025, with persistent positive asymmetry between February and April. The probabilistic cross-check adjustment produced an adjusted forest loss of 1594.74 ha and an adjusted gain of 802.65 ha over 120,289.70 ha. Within the protected natural areas, expected change was distributed unevenly among vegetation types, with pine–oak forest showing the highest total expected loss, whereas high-mountain meadow showed the highest expected gain and also remained among the covers with the highest expected loss, indicating active spatial reconfiguration in the upper ecological domain where Pinus hartwegii Lindl. is the dominant species, though no species-level classification was performed. These results provide spatial evidence to support field verification, forest-health monitoring, and management decisions in the protected high-mountain study area. 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

This study examines the effects of meteorological fire danger and human activity on wildfire ignition patterns in South Korea using records from 2004 to 2023. A percentile-based Fire Weather Index (FWI) classification, derived from negative binomial regression, identified critical daily fire frequency thresholds at FWI 4.39 (μ ≥ 1 fire/day) and FWI 6.84 (μ ≥ 2 fires/day). Bivariate LISA analysis revealed a spatial mismatch between resident population density and wildfire frequency: High–High (HH) clusters were concentrated in the Seoul metropolitan fringe, while Low–High (LH) clusters appeared in mountainous provinces where forest visitor ignitions and agricultural burning are the primary causes. In HH clusters, cigarette-related ignitions and structure-to-forest transitions were comparatively more frequent. Wildfire events were concentrated in age class 4–5 coniferous and broadleaf stands, and mean ignition-to-building distances in metropolitan areas frequently fell below 150 m. These findings suggest that prevention strategies should shift from uniform resident-oriented approaches toward spatially differentiated management targeting transient populations in LH areas and Wildland-Urban Interface (WUI) exposure in HH areas. 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

Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR’s capability to penetrate atmospheric obstructions and optical data’s spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. Full article

This study presents a comprehensive wildfire risk assessment framework for Rhodes Island, Greece, aimed at quantifying the impacts of climate change on hazard levels and vulnerability in a typical Mediterranean environment. The approach integrates Fire Weather Index (FWI) data, detailed fuel-type mapping, and multiple vulnerability indicators covering ecological, socioeconomic, and population factors, enabling spatially explicit estimates of current and future wildfire risk. Historically, Rhodes mostly faces moderate wildfire risk, mainly in central and northeastern regions, with localised areas of higher risk near settlements and key economic sites. Climate forecasts for 2025–2049 predict a notable increase in hazard, with areas experiencing extreme fire weather (FWI > 50) increasing from 15.19% to 66–72%, across all emission scenarios. Ecological vulnerability is particularly alarming, as 93% of the island is already highly susceptible; fire-prone forest and agricultural zones are expected to move into the highest ecological risk categories, especially in the central mountain areas. The devastating 2023 wildfire, which burned over 17,600 hectares, caused more than €5.8 million in direct damages and led to the largest evacuation in the island’s history, closely aligning with high-risk zones modelled in the framework. An important insight is the limited spatial variation in near-future risk between RCP 4.5 and RCP 8.5, indicating that significant wildfire intensification is largely unavoidable by mid-century, emphasising the urgent need for quick adaptation and risk mitigation efforts for Mediterranean critical infrastructure and communities. Full article

The sugarcane industry in South Africa is ranked among the top 15 producers worldwide and plays a significant role in supporting the nation’s socioeconomic development, producing approximately 2.3 million tons annually. Harvesting is largely labour-intensive and commonly involves the pre-harvest burning of sugarcane. This widespread practice is associated with (a) local air quality deterioration driven by pollutants such as carbon monoxide (CO), black carbon (BC), and sulphur dioxide (SO₂) and (b) adverse public health outcomes, including respiratory and cardiovascular diseases. This study aims to assess the air quality across KwaZulu-Natal and compare inland and coastal sugarcane-growing regions during the May–August 2023 harvest season. The CO and SO₂ concentrations are obtained from Sentinel-5P, while the BC data are sourced from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The Air Quality Index (AQI) is calculated using the CO, SO₂, PM_2.5, and NO₂ data from the Copernicus Atmosphere Monitoring Service (CAMS). The findings consistently indicate higher pollutant concentrations in inland regions, suggesting more concentrated burning activities and lower atmospheric dispersion relative to coastal areas. Overall, the results highlight the greater prevalence of poor air quality in inland sugarcane regions compared with coastal zones. Full article

Coniferous forests account for 78% of the western US forests and store a substantial amount of carbon. Wildfires significantly alter vegetation structure and associated forest carbon stocks. This study evaluates postfire biomass recovery trajectories (1984–2017) and total biomass accumulation in conifer forests that historically experienced low-severity, high-frequency fire regimes in the western US using recently launched Global Ecosystem Dynamic Investigations (GEDI) mission lidar data. All three ecoregions studied, including the Pacific Northwest (PNW), Southern Rockies (SR), and Northern Rockies (NR), show site-specific biomass recovery trajectories shaped by fire severity. The recovery trajectories were characterized by an initial decline and a variable gain with time since fire across the three ecoregions. Regions with low burn severity recovered to the unburned background state within the first three decades, while regions with higher burn severity only recovered in the Northern Rockies after five decades without fire. Moderate- and high-severity burned areas in both SR and PNW exhibited slow declines or sustained low biomass periods following fires, implying potential ecosystem transformation or an arrested state of lower biomass. Time since fire and fire severity were identified as the most significant drivers of postfire biomass recovery, likely because they reflect both reduced seed availability and the process of seedling establishment and regeneration. In addition, distance to unburned area, drought (measured using the Standardized Precipitation Evapotranspiration Index (SPEI)), elevation, and fire size were important drivers of biomass recovery. Our results demonstrate that all three ecoregions experienced a loss of overall biomass (15–23% (+/−40%)), with the largest losses occurring in the areas with high-severity burns (59% (+/−23%)) in the Southern Rockies compared to unburned forests within the first three decades. This study thus confirms GEDI’s ability to assess disturbance-driven vegetation biomass dynamics and provides an open-science methodology that could be utilized for other regions. In conclusion, our study indicates that an increase in fire severity within low-severity, high-frequency fire regimes, beyond historically observed levels, results in greater carbon losses. It is therefore important to consider the effects of increases in fire severity on vegetation recovery trajectories to infer the future carbon potential in these ecosystems. Full article

Páramos, high-mountain tropical ecosystems, are crucial for carbon storage and water regulation for many Andean cities. However, they are subjected to wildland fires that threaten the ecosystem services they provide. Fire activity varies substantially among páramos, making it essential to understand the drivers of this spatial variability. This study evaluates the relative influence of anthropogenic and biophysical factors on fire occurrence in Colombian páramos, analyzing burned area data from 2000 to 2022 using a Random Forest model. Results indicate that fire occurrence is shaped by the interaction between human pressures and biophysical characteristics. Annual precipitation was the most influential predictor: areas with lower mean annual precipitation (<1000–1500 mm/year) were linked to greater burned area. Vegetation cover, assessed using the Normalized Difference Vegetation Index (NDVI), showed a hump-shaped relationship, with intermediate greenness levels (0.13–0.25) being most prone to burning. Anthropogenic factors, especially proximity to buildings and agricultural zones, also had a significant impact. Our results show that fire occurrence in páramos cannot be attributed solely to human pressures but results from the combined effect of anthropogenic and biophysical drivers. Understanding of these interactions underscores the need for socio-ecological perspectives to guide integrated and adaptive management of strategic high-mountain ecosystems. Full article