Search Results (91)

Identifying small-scale burn scars is critical for global carbon accounting, yet remains computationally challenging due to spectral complexity and ground truth scarcity in heterogeneous landscapes. Conventional deep learning models often fail to generalize in such environments, lacking both domain-specific priors and representative training distributions required for precise segmentation. Here, we show that optimizing the fine-tuning of the Prithvi Earth Foundation Model (EFM) via Multidimensional Latin Hypercube Sampling (LHS) establishes a robust framework for this task. Our comparative analysis reveals that the domain-adapted Prithvi model achieves a Mean Intersection over Union (mIoU) of 0.91, outperforming standard Vision Transformers (ViT) by 31.9% and significantly surpassing reconstruction-based architectures, such as Scale-MAE. We demonstrate that LHS is superior to Simple Random Sampling (SRS) for optimizing foundation models, as it ensures statistical fidelity with a Kolmogorov–Smirnov (KS) statistic below 0.1 and effectively captures the tail distributions of fire weather indices. Furthermore, our framework exhibited exceptional data efficiency, retaining 94.5% of its peak accuracy with only 100 training samples. These findings provide a scalable solution for monitoring small-scale disasters in data-constrained regions and validate the synergy between rigorous sampling strategies and EFMs. Full article

Efficient and accurate active fire detection is crucial for timely firefighting and mitigating hazards. Geostationary satellites deliver high-frequency observations that offer valuable data for near-real-time fire monitoring. However, current operational fire detection algorithms often underutilize temporal information, failing to decouple fire-induced anomalies from inherent surface thermal heterogeneity, which results in frequent false alarms. To address this limitation, we constructed a ten-day historical background brightness temperature (BT) reference database from multi-year Himawari-8/9 data, serving as a stable, fire-undisturbed baseline. Based on this, an active fire detection algorithm was developed that integrates instantaneous spatial–contextual differences with historical deviations of these differences from the reference database. Evaluated against a robust dataset of over 55,000 fire pixels (cross-verified using 10 m Sentinel-2 burn-scar data), the proposed algorithm significantly outperforms the Himawari-8/9 Wildfire (WLF) product, achieving a commission error (CE) of 2.9%, an omission error (OE) of 37.5%, and an F1-score of 0.76. The framework demonstrated superior detection accuracy in challenging scenarios such as low-temperature, smoke-obscured, and early-stage fires, while maintained robust performance across diverse fire types. The approach enables rapid full-disk fire detection in less than one minute and can be adapted to other geostationary satellites, providing a technical foundation for building a globally coordinated fire monitoring system. Full article

During the summer fire season in the Greater Khingan Mountains, weak synoptic winds allow local mountain–valley breeze systems to predominantly influence fire spread. However, their dynamic effects remain insufficiently quantified, limiting fire forecasting accuracy. This study analyzes a decade of summer meteorological data and a high-resolution WRF-Fire simulation of a 2023 wildfire to investigate wind patterns and their impact on fire behavior. Results reveal pronounced diurnal and spatial wind variability, with low directional persistence and concentrated nighttime distributions. Under low-wind conditions, mountain–valley breezes shift from upslope during the day to downslope flows at night. Simulations and observations indicate higher nighttime wind speeds on slopes and higher daytime speeds in valleys, reflecting the combined effects of thermal circulation and gravitational acceleration. The WRF-Fire model effectively reproduced the wildfire’s macro-scale spread pattern, showing strong agreement with satellite-derived burn scars in mountainous regions. Fire progression was influenced by five distinct phases, with nocturnal mountain winds and topographic channeling accelerating spread. These highlight the role of terrain-driven mountain–valley breezes in fire propagation and provide insights to improve fire forecasting and management strategies in mountainous regions. Firefighting strategies must account for the diurnal cycle of wind, particularly the contrast between strong nighttime winds at higher altitudes and stable valley conditions. Full article

Fire is a key driver of ecosystem dynamics under global change, and understanding its complex relationship with the climate system is crucial for regional wildfire risk management and the development of ecological adaptation strategies. The western United States is a critical region for studying fire–climate interactions due to its pronounced environmental gradients, diverse fire regimes, and high vulnerability to climate change, which together provide a robust natural laboratory for examining spatial variability in fire responses. Based on tree-ring fire-scar records systematically collected from five major ecoregions in the western United States via the International Tree-Ring Data Bank (ITRDB), this study reconstructed fire history sequences spanning 430–454 years. By integrating methods such as correlation analysis, random forest regression, superposed epoch analysis, and effect size assessment, we systematically revealed the spatial differentiation patterns of fire frequency and fire spatial extent across different ecoregions, quantified the relative contributions of key climatic drivers, and identified climatic anomaly characteristics during extreme fire years. The results indicate that: (1) there are significant differences in fire frequency between different ecological areas; (2) summer drought conditions (PDSI) are the most consistent and strongest driver of fire across all ecoregions, and ENSO (NINO3) also shows a widespread negative correlation; (3) random forest models indicate that the Sierra Nevada and Madrean Archipelago ecoregions are the most sensitive to multiple climatic factors, while fire in regions such as the Northern Rockies may be more regulated by non-climatic processes; (4) extreme fire years across all ecoregions are associated with significant negative PDSI anomalies with prominent effect sizes, confirming that severe drought is the dominant cross-regional precondition for extreme fire events. This study emphasizes the region-specific nature of fire–climate relationships and provides a scientific basis for developing differentiated, ecoregion-specific fire prediction models and prevention strategies. The methodological framework and findings offer valuable insights for fire regime studies in other global forest ecosystems facing similar climate challenges. Full article

Since March 2021, a series of volcanic eruptions on Iceland’s Reykjanes Peninsula has repeatedly triggered wildfires in moss-dominated heathlands—an unprecedented phenomenon in this environment. These fires have consumed extensive organic material, posing emerging health risks and long-term ecological impacts. Using high-resolution multispectral satellite data from the Copernicus program, we present the first quantitative assessment of the spatial and temporal dynamics of volcanic wildfire activity. Our analysis reveals a cumulative burned area extending 11.4 km² beyond the lava flows, primarily across low-relief terrain. Time series of the Normalized Difference Vegetation Index (NDVI) capture both localized fire scars and diffuse, landscape-scale burn patterns, followed by slow and spatially heterogeneous recovery. Complementary ground surveys conducted in August 2024 document diverse post-fire successional pathways, with vegetation regrowth and species composition strongly governed by microtopography and substrate texture. Together, these results demonstrate that volcanic wildfires represent a novel and consequential secondary disturbance in Icelandic volcanic systems, highlighting the complex and protracted recovery dynamics of moss heath ecosystems following fire-induced perturbation. Full article

State-of-the-art historical global burned area (BA) products largely rely on MODIS data, offering long temporal coverage but limited spatial resolution. As a result, small fires and complex landscapes remain underrepresented in global fire history reconstructions. By contrast, Landsat provides the only continuous satellite record extending back to the 1980s, with substantially finer resolution. However, its use at a global scale has long been hindered by infrequent revisit times, cloud contamination, massive data volumes, and processing demands. We compared MODIS FireCCI51 with the only existing Landsat-based global product, GABAM, in a mountainous region characterized by frequent, small-scale fires. GABAM detected a higher number of burn scars, including small events, with higher Producer’s Accuracy (0.68 vs. 0.08) and similar User’s Accuracy (0.85 vs. 0.83). These results emphasize the value of Landsat for reconstructing past fire regimes in complex landscapes. Crucially, recent advances in cloud computing, data cubes, and processing pipelines now remove many of the former barriers to exploiting the Landsat archive globally. A more systematic integration of Landsat data into MODIS-based routines may help produce more complete and accurate databases of historical fire activity, ultimately enabling improved understanding of long-term global fire dynamics. Full article

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article

Human activities are increasing the occurrence of megafires that alter ecological dynamics in forest ecosystems. The objective of this study was to understand the impacts of a 610 km² megafire on patterns of tree regeneration and herbivory across three forest types (aspen/fir, oak/maple, and pinyon/juniper). Seventeen transect pairs in adjacent burned/unburned forest stands (6 aspen/fir, 5 oak/maple, and 6 pinyon/juniper) were measured. Sapling density, meristem removal, and height were measured across the transect network over a three-year period from 2019 to 2021. Tree species able to resprout from surviving roots (oak and aspen) generally responded positively to fire while species that typically regenerate by seeding showed little post-fire regeneration. Browse pressure was concentrated on deciduous tree species and was greater in burned areas but the effect diminished over the three-year study period. Meristem removal by herbivores was below the critical threshold, resulting in vertical growth over time. Our results indicate that forest regeneration within the megafire scar was generally positive and experienced sustainable levels of ungulate browsing that were likely to result in forest recruitment success. Full article

Fire occurs naturally and anthropogenically in the Cerrado biome, influenced by hydrology, climate, topography, and land use. Mapping burned areas is essential for understanding the causes of fire and improving prevention and regulation. However, fire scars are often confused with bare soil in agricultural regions. This study presents a method for mapping burned areas using spectral indices and artificial neural networks (ANN). We evaluated the accuracy of these techniques and identified the best input variables for scar detection. Using Sentinel-2 images from 2018 to 2021 during dry periods, we applied NDVI, SAVI, NBR, and CSI indices. The study included two stages: first, finding optimal classification configurations for fire scars, and second, mapping land use and cover with fire scars and crops. Results showed that using all Sentinel-2 bands and the four indices post-fire achieved over 93.7% accuracy and a kappa index of 0.92. Fire scars were mainly located in areas with temporary crops like soybean, sugarcane, rice, and cotton. This low-cost method allows for effective monitoring of fire scars, underscoring the need to regulate agricultural practices in the Cerrado, where burning poses environmental and health risks. Full article

The cessation of traditional mountain grazing has emerged as a principal driver of habitat degradation and the local extinction of Parnassius apollo (Linnaeus, 1758) in Serbia. While previous studies have cited multiple contributing factors, our research provides evidence that the abandonment of extensive livestock grazing has triggered vegetation succession, the disappearance of the larval host plant (Sedum album), and a reduction in microhabitat heterogeneity—conditions essential for the persistence of this stenophagous butterfly species. Through satellite-based analysis of vegetation dynamics (2015–2024), we identified clear structural differences between habitats that currently support populations and those where the species is no longer present. Occupied sites were characterized by low levels of exposed soil, moderate grass coverage, and consistently high shrub and tree density, whereas unoccupied sites exhibited dense encroachment of grasses and woody vegetation, leading to structural instability. Furthermore, MODIS-derived indices (2010–2024) revealed a consistent decline in vegetation productivity (GPP, FPAR, LAI) in succession-affected areas, alongside significant correlations between elevated land surface temperatures (LST), thermal stress (TCI), and reduced photosynthetic capacity. A wildfire event on Mount Stol in 2024 further exacerbated habitat degradation, as confirmed by remote sensing indices (BAI, NBR, NBR2), which documented extensive burn scars and post-fire vegetation loss. Collectively, these findings indicate that the decline of P. apollo is driven not only by ecological succession and climatic stressors, but also by the abandonment of land-use practices that historically maintained suitable habitat conditions. Our results underscore the necessity of restoring traditional grazing regimes and integrating ecological, climatic, and landscape management approaches to prevent further biodiversity loss in montane environments. Full article

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans. Full article

Wildfires threaten ecosystems, biodiversity, and human livelihood while exacerbating climate change. Accurate identification and monitoring of burned areas (BA) are critical for effective post-fire recovery and management. Although satellite multi-spectral imagery offers a practical solution for BA monitoring, existing methods often prioritize specific spectral bands while neglecting full spectral shape information, which encapsulates overall spectral characteristics. This limitation compromises adaptability to diverse vegetation types and environmental conditions, particularly across varying spatial scales. To address these challenges, we propose the time-series spectral-angle-normalized burn index (TSSA-NBR). This unsupervised BA extraction method integrates normalized spectral angle and normalized burn ratio (NBR) to leverage full spectral shape and temporal features derived from Sentinel-2 time-series data. Seven globally distributed study areas with diverse climatic conditions and vegetation types were selected to evaluate the method’s adaptability and scalability. Evaluations compared Sentinel-2-derived BA with moderate-resolution products and high-resolution PlanetScope-derived BA, focusing on spatial scale and methodological performance. TSSA-NBR achieved a Dice Coefficient (DC) of 87.81%, with commission (CE) and omission errors (OE) of 8.52% and 15.58%, respectively, demonstrating robust performance across all regions. Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. The method effectively captured fire scars and outperformed band-specific and threshold-dependent approaches by integrating spectral shape features with fire indices, establishing a data-driven framework for BA detection. These results underscore its potential for fire monitoring and broader applications in detecting surface anomalies and environmental disturbances, advancing global ecological monitoring and management strategies. Full article

Cultured epidermal autografts (CEAs) serve as an alternative permanent skin replacement, though high costs often limit their use in resource-constrained settings and to life-saving cases. This case report presents the first documented cosmetic application of a modified CEA technique in a bodybuilder, demonstrating favorable aesthetic outcomes. A 28-year-old Black male with a 20% total body surface area burn sustained in a domestic fire exhibited superficial and deep partial-thickness burns to the face, arms, torso, and feet. Refusing grafts from visible donor sites, treatment using a low-cost modified CEA approach was employed to minimize donor site morbidity. Keratinocytes harvested from a groin biopsy were cultured on Cutimed Sorbact^® (Essity AB, BSN Medical (Pty) Ltd., Pinetown, RSA) dressings with autogenous plasma and hydrogel supplementation and incubated at 37 °C for two weeks. Xenografts provided temporary coverage before CEA transplantation. Graft take was 85%, with minor (15%) loss at 21 days, requiring small autograft coverage. At two months, the Vancouver Scar Scale score was 4, indicating optimal pigmentation, smoother texture, and minimal scarring. These findings align with limited studies on CEAs for cosmetic applications, suggesting this cost-effective technique may broaden the scope of CEAs beyond life-saving interventions to include aesthetic reconstruction, reducing both donor site morbidity and scarring. Full article

The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions. Full article

Wildfires significantly impact ecosystems, economies, and biodiversity, particularly in fire-prone regions like the Caatinga biome in Northeastern Brazil. This study integrates machine learning with climate and land use data to model current and future fire dynamics in the Caatinga. Using MaxEnt, fire probability maps were generated based on historical fire scars from Landsat imagery and environmental predictors, including bioclimatic variables and human influences. Future projections under SSP1-2.6 (low-emission) and SSP5-8.5 (high-emission) scenarios were also analyzed. The baseline model achieved an AUC of 0.825, indicating a strong predictive performance. Key drivers of fire risk included the mean temperature of the driest quarter (with an importance of 14.1%) and isothermality (12.5%). Temperature-related factors were more influential than precipitation, which played a secondary role in shaping fire dynamics. Anthropogenic factors, such as proximity to farming and urban areas, also contributed to fire susceptibility. Under the optimistic scenario, low-fire-probability areas expanded to 29.129 Mha, suggesting a reduced fire risk with climate mitigation. However, high-risk zones persisted in the Western Caatinga. The pessimistic scenario projected an alarming expansion of very-high-risk areas to 12.448 Mha, emphasizing the vulnerability of the region under severe climate conditions. These findings underline the importance of temperature dynamics and human activities in shaping fire regimes. Future research should incorporate additional variables, such as vegetation recovery and socio-economic factors, to refine predictions. This study provides critical insights for targeted fire management and land use planning, promoting the sustainable conservation of the Caatinga under changing climatic conditions. Full article