Search Results (134)

Unmanned aerial vehicles (UAVs) have become an important component of multi-sensor remote sensing frameworks for post-fire forest monitoring because they provide ultra-high-resolution imagery for evaluating fine-scale vegetation response. This study assessed early-stage post-fire burn severity and forest health condition in a natural mixed forest affected by the 2024 wildfire in Nanyo, Yamagata, northeastern Japan. Burn severity was quantified using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery acquired five months after the fire (October 2024). High-resolution UAV RGB orthomosaics and field surveys were used to classify trees into healthy, damaged, and dead categories. Mean plot-level burn severity was estimated using a weighted midpoint dNBR approach, and the tree mortality rate was calculated from plot-based tree counts. The results showed that low and moderate–low burn severity classes dominated most plots, with mean dNBR values ranging from 0.085 to 0.386. UAV-based interpretation revealed substantial variability in tree health condition among plots. In 2024, fire effects were expressed mainly as canopy damage rather than immediate stand-level mortality. Mortality rates ranged from 14.9% to 58.6%, and some higher-severity plots contained greater damage. Overall, Sentinel-2 dNBR captured landscape-scale burn severity patterns, whereas UAV imagery improved interpretation of fine-scale health variability in heterogeneous burned forests. Full article

Forest fires pose a considerable threat to the security of ecosystems and human society, rendering accurate assessments of fire severity critical for ecological recovery and effective fire management. The differenced Normalized Burn Ratio (dNBR) has been employed to evaluate forest fire severity; however, it presents notable uncertainties owing to variations in data sources, temporal phases, and environmental factors. To address these challenges, this study analyzed 10 forest fires occurring between 2006 and 2023 in central Yunnan Province, China. First, a rapid sampling method utilizing very high-resolution imagery was developed to assess the performance of dNBR classification under varying conditions. Second, the study identified the optimal post-fire observation window and compared classification thresholds and accuracy between Landsat and Sentinel-2 imagery in assessing fire severity. Finally, the research explored the impacts of topographic correction and pre-fire vegetation differences on classification outcomes. The findings revealed the following: (1) Imagery captured in the spring of the fire year, characterized by minimal vegetation interference, demonstrated the highest classification stability and superior capability for identifying high-severity burns. (2) Landsat outperformed Sentinel-2 in regional accuracy (0.92 vs. 0.87), and direct threshold transfer between sensors resulted in a 39% underestimation of high-severity areas, underscoring the necessity for sensor-specific calibration. (3) Topographic correction provided limited practical benefits, merely yielding a marginal improvement in accuracy (+1.44%) with the SCS+C model in steep terrain, and was generally unnecessary. (4) The influence of pre-fire vegetation was discovered to be threshold-dependent: dNBR performed reliably in forests with pre-fire NDVI > 0.5, while adjusted approaches were solely recommended for sparse or heterogeneous vegetation. Overall, this study establishes a systematic framework for optimizing dNBR-based severity assessment, enhancing its accuracy and operational utility in forest fire management. Full article

In this paper, aiming at the aging problem of rubber sealing strips in key parts of hydropower units under long-term load, this study proposes a quantitative aging-feature-extraction technique centered on the ratio of the short-axis length to the original diameter (b/D) of serviced rubber strips. Through a systematic approach combining theoretical analysis, numerical simulation, and measured data calculations, the research first derives from energy principles that the elastic modulus (E) and yield stress (σ_s) are key physical parameters characterizing rubber aging, reflecting the material’s energy storage capacity and irreversible deformation threshold, respectively. Based on this, a radial compression simulation model of rubber strips is established, focusing on the cross-sectional deformation laws under 25% and 30% compression ratios in serviced conditions. It is found that the short-axis diameter ratio b/D exhibits a significant linear relationship with the dimensionless yield stress (σ_s/E), and a quadratic relationship with the dimensionless unit-length reaction force (F/ED). Using measured data, fluororubber (FKM) and nitrile rubber (NBR) specimens after 17 years of service are selected for radial compression experiments to extract the elastic modulus. The calculated results are compared with elasticity modulus estimates based on hardness empirical formulas (Gent’s and Qi’s formulas), showing consistency, particularly with Qi’s formula for NBR. This method enables rapid and accurate assessment of rubber aging, demonstrating the effectiveness and practicality of using b/D as a feature parameter. The study provides a quantitative and convenient tool for condition monitoring and life prediction of industrial equipment seals, especially suitable for the operation and maintenance of rubber components in complex environments such as hydropower units. Full article

While the effects of formulation variables of a rubber compound are well established for conventional rubber manufacturing techniques, their role in extrusion-based additive manufacturing remains underexplored. This study explores the impact of different base rubbers (NBR and EPDM) and curing agents (sulfur and peroxide) on processability and final part characteristics in material extrusion additive manufacturing applications. Under identical printing conditions, sulfur-cured NBR exhibits greater post-print shrinkage (12%) than sulfur-cured EPDM (7%). However, sulfur-cured NBR achieves a higher degree of adhesion between printed layers than sulfur-cured EPDM, as suggested by the % retention of the bulk materials’ ultimate stress by the printed parts (84–100% and 51–62%, respectively). Additionally, a peroxide-cured NBR formulation was compared against the same sulfur-cured NBR formulation. Printed parts from the peroxide-cured NBR formulation showed higher shrinkage (16%) and lower % retention of the bulk materials’ ultimate stress (26–33%) than the sulfur-cured NBR formulation. Additionally, the tensile behavior of all three rubber compounds was found to be strongly dependent on printing orientation, showing the anisotropic behavior typical of extrusion-based additive manufacturing. Sulfur-cured NBR showed the least anisotropy for stress at break (0.82) and strain at break (0.90), whereas peroxide-cured NBR showed the highest anisotropy in stress (0.74) and strain (0.82). The anisotropy ratios for sulfur-cured NBR and EPDM compounds were very similar for stress (0.82 vs. 0.82) and comparable for strain (0.90 vs. 0.87). Notably, the peroxide cure system provided almost twice as much available printing time as the sulfur cure system. This report on the effects of base rubber and curing agents on 3D printability and part properties provides a background to guide future efforts to design rubber compounds for 3D printing applications. Full article

Wildfires pose increasing challenges for Mediterranean landscapes, making rapid and reliable mapping of burn severity essential for management and recovery planning. This study applies an integrated geospatial workflow to wildfires that occurred in Greece during the 2024 summer season. Sentinel-2-derived dNBR and RBR indices were used to map burn severity, while CORINE Land Cover and Tree Cover Density datasets provided complementary context for interpreting how severity varied across different vegetation types and canopy-density conditions. A one-way ANOVA was used to summarize differences in burned area among severity classes. The results show that low and moderate-low severity levels dominated most fire perimeters, whereas high-severity patches were spatially limited and typically coincided with densely forested areas. Validation against Copernicus Emergency Management Service data yielded an overall agreement of approximately 94%, indicating that the applied multispectral workflow produced severity extents broadly consistent with independent operational products. By applying a consistent methodology across multiple fire events, this study demonstrates the value of combining spectral indices with land-cover information for interpreting severity patterns and supporting post-fire management. The findings highlight the usefulness of freely accessible remote sensing data for timely fire assessment in Mediterranean environments and provide a basis for future multi-regional and multi-year comparisons. Full article

Twin wildfires burned over 9500 ha in Seferihisar, İzmir, western Türkiye, on 29—30 June 2025 under extreme fire weather conditions. This study reconstructs the spatiotemporal progression of the fires and examines the drivers of contrasting behaviors and burn severity. Multi-source datasets—Sentinel-2 imagery, VIIRS/MODIS thermal detections, MTG images and thermal detections, aerial photos, and ground data—were integrated to delineate progression polygons and compute rate of spread (ROS), fuel consumption (FC), and fire-line intensity (FI). Kuyucak fire showed rapid early growth, burning 3554 ha in 2.5 h (mean ROS of 5.0 km h⁻¹; mean FI of 37,789 kW m⁻¹), driven by strong northeasterly winds of 40–50 km h⁻¹, steep terrain, dense Pinus brutia fuels, and very low dead fine-fuel moisture (<6%). Kavakdere fire advanced more slowly (mean ROS of 1.6 km h⁻¹) across open grassland and cropland, yielding lower FC and FI. Synoptic analysis revealed a strong pressure-gradient-induced northeasterly wind regime linked to a mid-tropospheric geopotential height dipole between Central Europe and the Eastern Mediterranean, while WRF simulations indicated a dry boundary layer and enhanced low-level winds during peak spread. Sentinel-2 dNBR burn severity mapping showed substantial spatial variability tied to fuel and topography contrasts. Findings demonstrate how twin ignitions under similar weather conditions can produce divergent outcomes, underscoring the need for terrain- and fuel-aware strategies during extreme Mediterranean fire outbreaks. Full article

This study presents the development and mechanical characterization of a full-scale helmet manufactured from a polyester matrix composite reinforced with woven jute fabric using vacuum infusion. Laminates with two and four reinforcement layers were produced and assembled using four joining configurations: seamless, stitched, bonded, and hybrid (bonded + stitched). Tensile tests were performed according to ASTM D3039, while frontal and lateral compression tests followed ABNT NBR 7471, aiming to evaluate the influence of laminate thickness and joining strategy on mechanical performance. In tension, the seamless configuration reached maximum loads of 0.80 kN (two layers) and 1.60 kN (four layers), while the hybrid configuration achieved 0.79 kN and 1.43 kN, respectively. Stitched and bonded joints showed lower strength. Under compression, increasing the laminate thickness from two to four layers reduced frontal elongation from 15.09 mm to 9.97 mm and lateral elongation from 13.73 mm to 7.24 mm, corresponding to stiffness gains of 50.3% and 87.3%, respectively. Statistical analysis (ANOVA/Tukey, α = 0.05) confirmed significant effects of thickness and joint configuration. Although vacuum infusion is a well-established process, the novelty of this work lies in its application to a full-scale natural-fiber helmet, combined with a systematic evaluation of joining strategies and a direct correlation between standardized tensile behavior and structural compression performance. The four-layer hybrid laminate exhibited the best balance between strength, stiffness, and deformation capacity. Full article

The 2025 large-scale forest fire in North Gyeongsang Province (Gyeongbuk) caused habitat fragmentation and disrupted ecological networks. This study quantitatively assessed both structural and functional connectivity loss and derived scientifically grounded restoration priorities. Fire intensity was assessed using Sentinel-2-based dNBR, and connectivity changes before and after the fire were analyzed by integrating MSPA (Morphological Spatial Pattern Analysis) and Omniscape (circuit theory-based model). MSPA captured extreme fragmentation, showing an 84% reduction in core habitats and a 976% increase in isolated patches, but failed to reflect functional movement flows. Omniscape approximated this using circuit theory, quantifying a 60% loss in cumulative current flow within the fire boundary and confirming that structural disconnection led to functional connectivity collapse. The restoration priority assessment (53 patches), based on source–sink theory, identified 14 high-priority patches (66% of total area). These patches were characterized by their adjacency to undamaged external cores, which serve as potential population sources for post-restoration recolonization. Notably, the top-priority areas were identified as key connection points within the national ecological corridor where Juwangsan National Park, the Nakdong Ridge, and Grade 1 Ecological Natural Areas overlap. This study demonstrated that integrating MSPA with Omniscape can simultaneously quantify both morphological fragmentation and functional disconnection caused by forest fires. This framework suggests that restoration planning should consider connectivity with broader ecological networks, in addition to recovering lost habitat area. Full article

Accurate mapping of burned forest areas in mountainous regions is essential for wildfire assessment and post-fire ecological management. This study develops an FS-SNIC-ML workflow that integrates multi-source optical fusion, semi-automatic sample generation, feature selection, and object-based machine-learning classification to support reliable burned-area mapping under complex terrain conditions. A pseudo-invariant feature (PIFS)-based fusion of Sentinel-2 and Landsat 8 imagery was employed to generate cloud-free, gap-free, and spectrally consistent pre- and post-fire reflectance datasets. Burned and unburned samples were constructed using a semi-automatic SAM–GLCM–PCA–Otsu procedure and county-level stratified sampling to ensure spatial representa-tiveness. Feature selection using LR, RF, and Boruta identified dNBR, dNDVI, and dEVI as the most discriminative variables. Within the SNIC-supported GEOBIA framework, four classifiers were evaluated; RF performed best, achieving overall accuracies of 92.02% for burned areas and 94.04% for unburned areas, outperforming SVM, CART, and KNN. K-means clustering of dNBR revealed spatial variation in fire conditions, while geographical detector analysis showed that NDVI, temperature, soil moisture, and their pairwise interactions were the dominant drivers of wildfire hotspot density. The proposed workflow provides an effective and transferable approach for high-precision burned-area extraction and quantification of wildfire-driving factors in mountainous forest regions. Full article

Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas contribute to potential vegetation shifts. This case study of the Los Angeles Bobcat Fire in 2020 uses Google Earth Engine (GEE) and Python 3.10.18 to access and visualize variations in Difference Normalized Burn Ratio (dNBR) area, Normalized Difference Vegetation Index (NDVI), and OpenET’s evapotranspiration (ET) across three dominant National Land Cover Database (NLCD) vegetation classes and dNBR classes via monthly time series and seasonal analysis from 2016 to 2024. Burn severity was determined based on Landsat-derived dNBR thresholds defined by the United Nations Office for Outer Space Affairs UN-Spider Knowledge Portal. Our study showed a general reduction in dNBR class area percentages, with High Severity (HS) dropping from 15% to 0% and Moderate Severity (MS) dropping from 45% to 10%. Low-Severity (LS) areas returned to 25% after increasing to 49% in May of 2022, led by vegetation growth. The remaining area was classified as Unburned and Enhanced Regrowth. Within our time series analysis, HS areas showed rapid growth compared to MS and LS areas for both ET and NDVI. Seasonal analysis showed most burn severity levels and vegetation classes increasing in median ET and NDVI values while 2024’s wet season median NDVI decreased compared to 2023’s wet season. Despite ET and NDVI continuing to increase post-fire, recent 2024 NLCD data shows most Forests and Shrubs remain as Grasslands, with small patches recovering to pre-fire vegetation. Using GEE, Python, and available satellite imagery demonstrates how accessible analytical tools and data layers enable wide-ranging wildfire vegetation studies, advancing our understanding of the impact wildfires have on ecosystems. Full article

Eaton Canyon in California serves as the focal point for a comprehensive post-wildfire ecological impact assessment. This study employs an approach integrating satellite imagery from the European Space Agency’s Sentinel constellation to study an area of 271.49 km². The data encompasses both radar and multispectral data, offering a multi-dimensional view of the affected landscape. The analysis leverages the power of the random forest algorithm. Firstly, three widely used indices—the difference normalized burn ratio (dNBR), relative burn ratio (RBR), and relative difference normalized burn ratio (RdNBR)—were calculated and compared based on their accuracy and Kappa index. Secondly, we developed a fusion approach by using all the fire indices to obtain a precise severity map by classifying the affected area into distinct severity classes. Thirdly, a separate fusion approach was developed utilizing the normalized difference vegetation index (NDVI), radar vegetation index (RVI), and modified normalized difference vegetation index (MNDVI) to analyze the distribution of vegetation before and after the wildfire. The merger proposals were developed using a combination of index values to obtain better information on the fire severity map and post-fire vegetation distribution. The results indicated an accuracy of 78% when employing the dNBR index. A higher accuracy of 81% was observed with the RBR index, while the RdNBR demonstrated an accuracy of 95%. Our approach, which combines all fire indicators, offers optimal accuracy of 99%. A percentage of 56.76% did not burn due to the topography of the canyon creating natural firebreaks. Areas classified as low severity (7.83%) showed minimal damage with minimal tree mortality. Moderate- to low-severity areas (5.83%) represented regions with partial crown burns and some tree mortality. Moderate- to high-severity areas (7.22%) showed significant tree mortality. Finally, high-severity areas (22.36%), characterized by complete tree mortality and significant loss of vegetation cover, were largely concentrated in specific sections of the canyon, likely influenced by factors such as slope and fuel type. These findings provide valuable information for post-fire ecological recovery efforts and future land management strategies in Eaton Canyon and similar fire-prone landscapes. Full article

We present a site-agnostic workflow to identify Fireline Tactical Support Points (FTSPs) and corridors following wildfire where spectral-change proxies (dNBR, RdNBR, and dNDVI) are paired pre/post-fire and co-registered on a 20 m grid together with a 72 h rainfall accumulation layer, which is treated as an operational feasibility and safety overlay, complementing access and terrain. Applied to the Vesuvius National Park (Italy) wildfire episode of August 2025, the pipeline yields suitability/susceptibility surfaces, ranked factors, and corridor candidates, with estimated successes including coherent prioritization within high-severity mosaics, improved continuity toward existing access routes, and reduced overlap with mapped sensitive areas at like-for-like suitability. Low-carbon staging is retained as a design safeguard, while detailed greenhouse-gas accounting is intentionally deferred to future, fleet-resolved multi-criteria analyses. The approach enables rapid, repeatable decision support and is relevant to SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). Full article

This study examines the swelling behaviour of nitrile-butadiene rubber (NBR) when interacting with tyre pyrolysis oils (TPO), with a focus on the chemical composition of TPO and their interaction with rubber matrices. Initially, a comparative analysis with conventional diesel fuel (DF) was performed using advanced analytical techniques, including two-dimensional gas chromatography coupled to mass spectrometry (2D-GC/MS), infrared (IR) spectroscopy, and nuclear magnetic resonance (¹H-NMR) spectroscopy. The analysis revealed that TPO contains a significantly higher proportion of aromatic hydrocarbons than DF, along with unsaturated and oxygen-containing compounds not present in DF. Based on these compositional differences, blends of TPO and DF were formulated and evaluated for their suitability as liquid energy carriers according to the specifications of DF. In principle, blends with an addition of up to 5 vol% TPO in DF are technically suitable for use as fuel. Subsequently, the sorption behaviour of TPO, DF, and their blends in NBR was investigated. The swelling potential was determined based on mass, density, and volume, and the changes in the hardness and tensile strength of NBR were recorded. The results demonstrate that TPO induces pronounced swelling in NBR, as evidenced by a marked increase in mass uptake and volume expansion. A linear increase was observed between the degree of swelling and the increasing TPO content in the blends. Mechanical property assessments revealed a corresponding decrease in the hardness and tensile strength of NBR upon exposure to TPO, with the most severe effects associated with neat TPO. This work provides a comprehensive assessment of TPO as a potential blend component for DF. It highlights the need for careful consideration of material compatibility in practical applications. 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

This study examined the structural and ecological drivers of burn severity during the March 2025 wildfire in Uiseong County, Republic of Korea, with a focus on developing a predictive framework using the differenced Normalized Burn Ratio (dNBR). Seventeen candidate variables were evaluated, among which the forest type, stand age, tree height, diameter at breast height (DBH), and Normalized Difference Vegetation Index (NDVI) were consistently identified as the most influential predictors. Burn severity increased across all forest types up to the 4th–5th age classes before declining in older stands. Coniferous forests exhibited the highest severity at the 5th age class (mean dNBR = 0.3069), followed by mixed forests (0.2771) and broadleaf forests (0.2194). Structural factors reinforced this pattern, as coniferous and mixed forests recorded maximum severity within the 5–11 m height range, while broadleaf forests showed relatively stable severity across 3–21 m but declined thereafter. In the final prediction model, NDVI emerged as the dominant variable, integrating canopy density, vegetation vigor, and moisture conditions. Notably, NDVI exhibited a positive correlation with burn severity in coniferous stands during this early-spring event, diverging from the generally negative relationship reported in previous studies. This seasonal anomaly underscores the need to interpret NDVI flexibly in relation to the forest type, stand age, and phenological stage. Overall, the model results demonstrate that mid-aged stands with moderate heights and dense canopy cover are the most fire-prone, whereas older, taller stands show reduced susceptibility. By integrating NDVI with structural attributes, this modeling approach provides a scalable tool for the spatial prediction of wildfire severity and supports resilience-based forest management under climate change. Full article