Search Results (323)

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

Wildfires are increasing in frequency and severity worldwide, with long-lasting effects on forest vegetation and fungal communities essential for seedling establishment and forest resilience. Nothofagus pumilio, the dominant tree of South American temperate forests, forms key fungal symbioses but shows limited post-fire regeneration. Root-fungal community responses in seedlings under long-term post-fire conditions remain unknown. We characterized root-associated fungal communities in N. pumilio seedlings growing in burned (eight years post-fire) and unburned forest of the southern Andes, using ITS1 metabarcoding. We assessed diversity, composition, and abundance of root-associated fungi, including ectomycorrhizal fungi and dark septate endophytes. Diversity and evenness were significantly lower in burned than in unburned forest, with no significant differences in richness or composition. The ectomycorrhizal genus Cortinarius was less abundant in burned-forest seedlings. In seedlings from burned forest, the dark septate endophyte Cadophora was significantly more abundant, whereas Exophiala and Cladophialophora were less abundant than in unburned forest. Despite these shifts, burned areas retained root-associated fungal taxa linked to plant-beneficial functions and shared most OTUs with unburned forest, indicating an incomplete but ongoing recovery. Further research should evaluate how these shifts affect early seedling performance, and assess the role of persistent beneficial taxa for restoration after wildfires. Full article

Forest fires are a recurring disturbance in Mediterranean ecosystems, but they also impact air quality and public health, particularly given recent trends towards increasingly widespread and extreme fires. This study analyzed six large fires that occurred in Sardinia, Italy, between 2009 and 2021, in order to evaluate their impact on ground-level PM₁₀ concentrations and to investigate the influence of fire size, fuel type, and meteorological conditions. The analysis included data on fire perimeters and land cover, meteorological conditions, smoke plume trajectory simulations using HYSPLIT, satellite imagery, and PM₁₀ concentration measurements from the regional air quality monitoring network. The six case studies differed markedly in terms of burned area, vegetation composition, duration, and weather context. The results showed that the extent of the fire is likely not the most significant factor influencing the increase in PM₁₀ observed in the days following the fires. The most pronounced increases in PM₁₀ concentrations were recorded during the Isili and Montiferru fires, which differed in burned area but were similar in terms of fuel composition, dominated by forest and shrubland vegetation. These factors, together with favorable atmospheric conditions for plume transport and particulate matter deposition, likely contributed to the observed increases in PM₁₀, including exceedances of WHO and national daily limit values. By contrast, Bonorva, Ittiri, and Borore showed limited or no clear accumulation of PM₁₀, despite large burned areas in some cases. These findings suggest that the effects of wildfires on air quality in the Mediterranean region can be influenced by several features, such as meteorological conditions, biomass burned, area burned, severity and intensity of fires. Furthermore, the observed exceedance of WHO thresholds highlights the need to integrate public health considerations into wildfire risk management in the Mediterranean basin. Full article

This study investigated the effects of controlled burns on biomass dynamics and fire behavior in areas of stricto sensu grassland in southern Brazil. The experiment consisted of conducting controlled burns in three experimental plots divided into five subplots, with data collection on fire behavior (rate of spread, fireline intensity, residence time, and heat released) followed by monitoring of vegetation recovery over 19 months. To assess the influence of fire on biomass increase, the Gompertz model was fitted, correlating the Absolute Growth Rate (AGR), the time to peak productivity and the time at which the growth curve reaches 95% of the asymptote with the fire behavior variables. The results demonstrated that low-intensity controlled burns were effective in reducing fuel load. Correlation analysis indicated that fire behavior variables had little influence on the rate of vegetation recovery, suggesting that the ecosystem tolerates low-severity disturbances well. The Gompertz model showed a satisfactory fit (R² = 0.86 for total biomass), proving to be a useful tool for management. It is concluded that the use of low-intensity prescribed burns is a viable and safe practice for protected areas. Full article

While remote sensing has been widely used to estimate vegetation biochemical and structural properties, relatively little work has systematically and experimentally linked vegetative fuel spectral reflectance to independently measured fuel combustibility, free-spreading fire behaviour, and fire emissions. This limits the development and validation of remote sensing products for operational wildland fire applications. We present a pilot-study evaluation of an experimental method that integrates imaging spectroscopy with free-spreading fires in a combustion wind tunnel to investigate relationships between wildland fuel spectral reflectance and combustibility, fire behaviour, and fire emissions. The pilot study used three common Australian fuels at two combustibility levels. Pre- and post-burn imaging spectroscopy observations (400–2500 nm) were collected during 26 experiments, alongside measurements of fuel biochemistry, calorimetry, moisture, rate of spread, combustion efficiency, and gaseous and particulate emissions. Statistically significant differences between fuel type and combustibility were found in fuel moisture, rate of spread, and emissions, with corresponding differences evident in the spectral signatures. Partial least squares regression (PLSR) indicated that pre-fire spectral information was informative for predicting several fire behaviour and emissions metrics. These results demonstrate the feasibility of the proposed methodology and provide a foundation for extending it to a wider range of wildland fuels. Data generated using this methodology have the potential to improve interpretation of remote sensing datasets and inform the design of future satellite instruments, with potential applications in assessing fuel condition, predicting fire behaviour, and estimating wildland fire emissions. Full article

Riparian zones provide vital ecosystem services, including water purification, soil aeration, and recreation. Anthropogenic activities and invasive plant species threaten native vegetation and alter fire patterns. This study investigates the impact of invasive vegetation cover (IVC) on riparian fire patterns in Otay Valley Regional Park, San Diego, California, using Sentinel-2 imagery to analyze 13 fires that occurred in 2019. The impact of IVC on fire patterns was assessed using high-resolution Normalized Difference Vegetation Index (NDVI) and Differenced Normalized Burn Ratio (dNBR) from 2019 to 2023. We found nuanced fire dynamics relationship driven by species-specific traits. Results showed that post-fire NDVI was consistently highest in areas with <25% IVC, suggesting more stable vegetation recovery in native areas. In contrast, areas with >75% IVC had high NDVI variability and greater canopy loss, particularly where species such as Melilotus albus and mixed annual forbs dominated. IVC was evaluated descriptively rather than as an inferential predictor due to the small number of fire counts. Descriptive patterns indicate that post-fire vegetation response varied by dominant invasive species, with resilient taxa such as Arundo donax, Tamarix ramosissima, and Eucalyptus spp. showing evidence of rapid or sustained recovery. These findings highlight the complexity of fire dynamics in invaded riparian systems and the importance of species-specific monitoring. We recommend integrating remote sensing with targeted invasive vegetation species management to improve fire resilience and ecological integrity in urban riparian corridors. Full article

In wildfire environments, high temperatures generated by wildfires may cause thermal aging, deformation, and even burning damage to the silicone rubber sheds of composite insulators, thereby deteriorating their surface hydrophobicity and insulation characteristics. Meanwhile, ash and carbonaceous particles produced by vegetation combustion tend to accumulate on insulator surfaces, forming conductive contamination layers that reduce surface resistance, intensify leakage current activity, and increase the risk of flashover. To investigate these effects, FXBW₄-35/70 composite insulators were selected as the research object. A simulated burning test platform was established to evaluate variations in the mechanical properties of insulator sheds under wildfire conditions. In addition, the feasibility of using simulated ash was assessed. AC flashover tests were conducted on contaminated insulators to quantify the influence of ash deposition on flashover performance. Beyond confirming the thermal aging behavior of silicone rubber under wildfire exposure, this study establishes a quantitative relationship between wildfire ash deposition, equivalent contamination severity, and flashover performance. A correction model for post-fire pollution withstand voltage is further proposed, providing a practical basis for condition assessment and maintenance of transmission line insulators after wildfire events. 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

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Wildfires are an increasingly recurrent disturbance in Mediterranean forest landscapes, yet fire severity assessment remains limited in data-scarce regions such as Lebanon. This study aims to assess wildfire severity patterns and identify the main environmental drivers influencing fire severity across the forests of Akkar, northern Lebanon, within a Digital Landscapes framework. Fire severity was mapped using the Differenced Normalized Burn Ratio (dNBR) derived from multi-temporal Landsat-8 imagery (2013–2024) processed in Google Earth Engine. Vegetation productivity was assessed through annual Net Primary Productivity (NPP), while topographic variables (elevation, slope, and aspect) were derived from a Digital Elevation Model. The results reveal heterogeneous fire severity patterns over the study period and pronounced spatial variability in NPP, with no consistent linear relationship between productivity and fire severity. Principal Component Analysis (PCA) was applied to explore multivariate relationships between fire severity, productivity, and terrain. PCA results show that the first two components explain 77.4% of the total variance, indicating that fire severity is primarily structured by topographic factors, particularly elevation and solar exposure, while vegetation productivity plays a secondary role. These findings highlight the dominant influence of terrain on wildfire severity in Mediterranean mountainous landscapes, and demonstrate the value of integrating remote sensing, cloud-based platforms, and multivariate analysis for fire assessment in data-scarce regions. The study contributes to the advancement of Digital Landscapes approaches by providing a scalable and data-driven framework for understanding fire dynamics and supporting future landscape management and risk assessment strategies. Full article

Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R² = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights. 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

Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University and others provide controlled environments for algorithms’ evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of five dimensionality reduction strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)-based selector together with a clustering based baseline, K-Means Clustering-Based Band Selection (KMCBS). These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing. Full article

Quicker identifying abilities are required due to the rising frequency and severity of wildfires. Although polar-orbiting satellites with medium and high resolution can accurately identify wildfires, the majority of available fire detection images originate from such platforms. However, their low temporal revisit rates restrict the potential for early warning. Geostationary satellites provide minute-level, continuous monitoring that corresponds with the quick onset of wildfires; however, their dependence on conventional threshold methods and coarse spatial resolution result in notable detection errors. This study developed an integrated deep learning framework for accurate wildfire detection in low-resolution geostationary imagery in order to get over these restrictions. A novel dynamic index, the Dynamic Normalized Burn Ratio—Thermal (DNBRT), was proposed to characterize wildfire progression by integrating instantaneous thermal anomalies with dynamic vegetation signals. Based on this, a Fire Spatiotemporal Network (FST-Net) was designed, with an efficient residual backbone, a Convolutional Block Attention Module (CBAM) for feature refinement, and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal evolution. Trained and evaluated on an FY-4B-based fire/non-fire dataset, the proposed framework demonstrated superior performance. FST-Net outperformed benchmark models, improving accuracy and recall by averages of 10.30% and 9.32% respectively while achieving faster inference speed. An ablation experiment confirmed the critical role of fusing thermal and vegetation features in DNBRT, with 92.7% accuracy and 94.9% recall. Compared to the FY-4B fire product, the proposed framework enables earlier detection, maintains more complete tracking of fire progression, and exhibits greater robustness under complex burning conditions while achieving sub-hectare (0.36 ha) detection sensitivity at the 2 km resolution. By synergizing a discriminative dynamic index with an efficient spatiotemporal architecture, this work provides an effective solution for operational, real-time monitoring of small and early-stage wildfires from geostationary satellites. 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