Electrostatic charge significantly influences microorganism–surface interactions, including viral adhesion and transmission. While bacterial surface charges are well characterized using electrophoretic mobility and X-ray photoelectron spectroscopy (XPS), similar studies for viruses are limited. This work bridges the gap by estimating the negative surface charge of the Phi6 bacteriophage using XPS data. A novel approach is applied, combining chemical functionalities derived from XPS with a system of equations to quantify surface polysaccharides, proteins, hydrocarbons, and negatively charged groups (RCOO⁻ and R₂PO₄⁻). The results indicate a predominance of proteins on the viral surface and a pH-dependent negative charge: phosphate groups dominate at low pH (1–3), while both groups contribute equally at pH 4–9. These findings provide a deeper understanding of virus–surface interactions and underscore the importance of pH in modulating viral surface charge. This method, which surpasses traditional electrophoretic mobility techniques, offers new perspectives for studying viral adhesion and developing improved antiviral materials and disinfection strategies.

This study investigates the effectiveness of combining UAV multispectral imagery, biomass-chlorophyll indices, and an unsupervised machine learning classifier (UMLC) to map carob tree dieback caused by the Barbary macaque. The data were rigorously preprocessed, twelve biomass-chlorophyll indices were derived and UMLC was implemented, analyzed, and validated. The results show that among the tested indices, TDVI and CIG exhibited similar and superior performance with excellent dynamic range, and the map generated from the TDVI threshold showed a clear spatial distribution of carob classes. Similarly, UMLC proved its ability to classify individual trees according to their degree of dieback in a complex environment, achieving an excellent overall classification accuracy of 93%. However, since the tested approaches provide almost alike results, the method based solely on spectral indices is recommended for its simplicity, facility to transfer between sensors, and it constitute an accurate alternative compared to the sophisticated and complex methods such UMLC.

Accurate vegetation mapping in mountainous Mediterranean terrain presents significant challenges due to topographical complexity and accessibility constraints. This study implements a U-Net architecture with MobileNetV2 encoder for automated vegetation segmentation using high-resolution UAV imagery in Dir El Ksiba, Morocco. The methodology leveraged 34 strategically selected images from challenging mountainous terrain, generating 9,996 standardized patches for optimized model training. Our approach targeted five vegetation classes: three phenological stages of Quercus ilex, Pinus halepensis, and Tetraclinis articulata. The U-Net+MobileNetV2 model demonstrated robust performance with validation metrics outperforming training metrics: 82.0% accuracy and mean Intersection over Union (mIoU) of 0.585. Class-specific IoU values revealed highest performance for Pinus halepensis (0.634), followed by Quercus ilex Stage 1 (0.598), Tetraclinis articulata (0.588), Quercus ilex Stage 2 (0.571), and Quercus ilex Stage 3 (0.556). The lightweight architecture (3.5M parameters) enables efficient deployment while maintaining competitive performance. This research establishes a methodological framework for applying deep learning-based vegetation segmentation in topographically complex Mediterranean ecosystems, addressing practical constraints of mountainous terrain data acquisition while maintaining scientific rigor.

Monitoring the long-term cumulative impacts of industrial mining and environmental changes in sensitive ecosystems remains a critical challenge for sustainable resource management. This study investigates 41 years (1984–2025) of land use and land cover (LULC) dynamics in the Athabasca oil sands region (Alberta, Canada) using the historical Landsat archive (TM, ETM+, and OLI). The methodology integrated temporal and spectral features through two complementary phases: automated spectral trajectory-based change detection and a Support Vector Machine (SVM) classification combining original bands with multiple spectral indices, including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), transformed difference vegetation index (TDVI), bare soil index (BSI), and Modified Normalized Difference Water Index (MNDWI). This framework quantified baseline LULC, mining expansion, tailings pond footprints, and reclamation operations across a 12,146-km² study area. The spectral trajectory analysis effectively tracked multiple temporal disturbances, revealing accelerating mining activities alongside forest harvesting, wildfire stress, and insect damage. Additionally, it captured progressive and significant spectral variations in the Athabasca River’s surface water, reflecting cumulative industrial runoff and long-term environmental pressure. Concurrently, the SVM classifications (Overall accuracy ≥ 96.8% and Kappa coefficient ≥ 94%) measured major LULC shifts. Dense coniferous forests declined by 1200 km², while mixed forests decreased from 4500 km² in 1984 to a minimum of 1200 km² in 2010, before recovering to 3040 km² by 2025 due to reclamation efforts. Conversely, active mining infrastructure and tailings ponds expanded from 95 km² (1984) to 900 km² (2025), occupying 7.41% of the landscape. Ultimately, this integrated remote sensing approach captured the complex interactions between accelerating industrial development, climate stressors, and the localized progress of land reclamation, providing a scalable framework for ecosystem monitoring in heavily disturbed landscapes.