Search Results (4,441)

Soil organic carbon (SOC) is a critical indicator of soil health, directly influencing crop productivity, soil structure, and environmental sustainability. Existing SOC estimation techniques using satellite reflectance data are effective for large-scale applications; however, their accuracy is reduced due to various types of noisy samples caused by vegetation interference, sensor-related anomalies, atmospheric effects, and other spectral distortions. This study proposes a robust data refinement framework capable of handling any soil sample, whether clean or noisy, by identifying and correcting noisy samples to enable more accurate SOC estimation outcomes. The approach first explores complex global relationships among spectral bands to understand and represent subtle patterns in soil reflectance using the Transformer network. To remove redundancy and retain only essential information of the transformed features, we apply a dimensional reduction technique for efficient analysis. Building upon this refined representation, noisy samples are detected without relying on strict data distribution assumptions, ensuring effective identification of noisy samples in diverse conditions. Finally, instead of excluding these noisy samples, the proposed framework corrects their reflectance through a conditional Generative Adversarial Network (cGAN) to align with expected soil spectral characteristics, thereby preserving valuable information for more accurate SOC estimation. The proposed approach was evaluated on benchmark satellite datasets, demonstrating superior performance over existing noise correction techniques. Experimental validation using the Landsat 8 dataset demonstrated that the proposed framework improved SOC estimation performance by increasing $R^2$ by 1.52%, reducing RMSE by 4.45%, and increasing RPD by 5.14% compared to the best baseline method (OC-SVM + Kriging). These results confirm the framework’s effectiveness in enhancing SOC estimation under noisy conditions. This scalable framework supports accurate SOC monitoring across diverse conditions, enabling informed soil management and advancing precision agriculture. Full article

Recent advances in hyperspectral imaging (HSI) and multimodal deep learning have opened new opportunities for crop health analysis; however, most existing models remain limited by dataset scope, lack of interpretability, and weak cross-domain generalization. To overcome these limitations, this study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches. The proposed architecture enables robust and interpretable feature learning across heterogeneous data sources, facilitating the estimation of spectral proxies of chlorophyll content, plant vigor, and disease stress indicators rather than direct physiological measurements. Experiments were performed on seven publicly available benchmark datasets encompassing diverse spectral and visual domains: three hyperspectral datasets (Indian Pines with 16 classes and 10,366 labeled samples; Pavia University with 9 classes and 42,776 samples; and Kennedy Space Center with 13 classes and 5211 samples), two plant disease datasets (PlantVillage with 54,000 labeled leaf images covering 38 diseases across 14 crop species, and the New Plant Diseases dataset with over 30,000 field images captured under natural conditions), and two chlorophyll content datasets (the Global Leaf Chlorophyll Content Dataset (GLCC), derived from MERIS and OLCI satellite data between 2003–2020, and the Leaf Chlorophyll Content Dataset for Crops, which includes paired spectrophotometric and multispectral measurements collected from multiple crop species). To ensure statistical rigor and spatial independence, a block-based spatial cross-validation scheme was employed across five independent runs with fixed random seeds. Model performance was evaluated using $R^2$, RMSE, F1-score, AUC-ROC, and AUC-PR, each reported as mean ± standard deviation with 95% confidence intervals. Results show that Agri-DSSA consistently outperforms baseline models (PLSR, RF, 3D-CNN, and HybridSN), achieving up to $R^2=0.86$ for chlorophyll content estimation and F1-scores above 0.95 for plant disease detection. The attention distributions highlight physiologically meaningful spectral regions (550–710 nm) associated with chlorophyll absorption, confirming the interpretability of the model’s learned representations. This study serves as a methodological foundation for UAV-based and field-deployable crop monitoring systems. By unifying hyperspectral, chlorophyll, and visual disease datasets, Agri-DSSA provides an interpretable and generalizable framework for proxy-based vegetation stress estimation. Future work will extend the model to real UAV campaigns and in-field spectrophotometric validation to achieve full agronomic reliability. Full article

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and classification across five reclaimed wellsites in Alberta, Canada. A deep learning workflow using 3D convolutional neural networks was applied to LiDAR and MS data collected in spring, summer, and autumn. Results show that LiDAR alone provided high accuracy for tree segmentation and height estimation, with a mean intersection over union (mIoU) of 0.94 for vegetation filtering and an F1-score of 0.82 for treetop detection. Incorporating MS data improved deciduous/coniferous classification, with the highest accuracy (mIoU = 0.88) achieved using all five spectral bands. Coniferous species were classified more accurately than deciduous species, and classification performance declined for trees shorter than 2 m. Spring conditions yielded the highest classification accuracy (mIoU = 0.93). Comparisons with ground measurements confirmed a strong correlation for tree height estimation (R² = 0.95; root mean square error = 0.40 m). Limitations of this technique included lower performance for short, multi-stemmed trees and deciduous species, particularly willow. This study demonstrates the value of integrating 3D structural and spectral data for monitoring forest recovery and supports the use of UAV remote sensing for scalable post-disturbance vegetation assessment. The trained models used in this study are publicly available through the TreeAIBox plugin to support further research and operational applications. Full article

Wetland diversity and associated wildlife is declining globally. The Szaporca Old-Drava oxbow, one of Hungary’s first wetlands designated under the Ramsar Convention, has been threatened by desiccation, prompting the implementation of water replenishment interventions. This study aimed to determine which aquatic vertebrate species can be detected using bottle traps in the oxbow and whether the traps’ efficiency and capture success change following hydrological restoration. Our results showed that the relative frequency of smooth newts caught with bottle traps was 10%, while that of other amphibians (Danube crested newt, common spadefoot toad, edible frog) and fish (European weather loach, European mudminnow, Danube whitefin gudgeon) species did not even reach 1%. Based solely on the relative frequency data of the smooth newt, we found that both the capture efficiency (10.4% vs. 3%) and capture success (17% vs. 7.4%) of bottle traps significantly declined following an increase in water volume in the oxbow. Sex ratio patterns also shifted markedly: in the year of water scarcity, the male-to-female ratio of smooth newt was 2.7:1, whereas during water-abundant conditions it increased to 7:1. Water replenishment raised the water level by nearly one meter, resulting in the partial inundation of terrestrial vegetation and consequent habitat alterations. While water supplementation clearly supports the persistence of rare, protected, strictly protected and endemic aquatic species, our findings highlight the importance of considering hydrological conditions when interpreting the results of long-term monitoring in wetland ecosystems. Full article

Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early detection critical for preserving ecosystem integrity. This study proposes a novel framework that integrates self-supervised learning (SSL), supervised segmentation, and multi-sensor data fusion to enhance vegetation classification in the Bayinbuluke Alpine Wetland, China. High-resolution satellite imagery from PlanetScope-3 and Jilin-1 was fused, and SSL methods—including BYOL, DINO, and MoCo v3—were employed to learn transferable feature representations without extensive labeled data. The results show that SSL methods exhibit consistent variations in classification performance, while multi-sensor fusion significantly improves the detection of rare and fragmented vegetation patches and enables the early identification of invasive species. Overall, the proposed SSL–fusion strategy reduces reliance on labor-intensive field data collection and provides a scalable, high-precision solution for wetland monitoring and invasive species management. Full article

Pepper (Capsicum spp.) is an economically valuable crop worldwide including in the United States due to its nutritional benefits in human health and widespread use as a spice or vegetable. Although numerous viruses have been reported infecting peppers in the USA, little is known about the diversity and distribution of pepper-infecting viruses in Oklahoma. To address this knowledge gap, we conducted a comprehensive pepper virome study to identify viruses infecting pepper and their incidence across six different counties in Oklahoma. A total of 310 plant samples including pepper and other potential hosts were collected during the 2021 and 2022 growing seasons. Samples were analyzed using high-throughput sequencing (HTS) and/or reverse transcription-polymerase chain reaction (RT-PCR) assays. Viral contigs identified via HTS were further validated through RT-PCR or PCR assays followed by Sanger sequencing. In total, 17 distinct viruses were detected, including 15 RNA and two DNA viruses, with several representing putatively novel findings. The most prevalent virus was beet curly top virus (BCTV), followed by tomato yellow leaf curl virus (TYLCV), potato yellow dwarf virus/constricta yellow dwarf virus (PYDV/CYDV), and pepper mild mottle virus (PMMoV). Virus incidence varied by season and location, with some surveys showing infection rates exceeding 80%. This study provides the first in-depth characterization of the pepper virome in Oklahoma and valuable insights into the prevalence and distribution of pepper-infecting viruses. These findings will support the development of informed, targeted strategies for virus detection and management in pepper production systems. Full article

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article

Habitat alteration due to agricultural expansion and heavy livestock grazing is a major threat for open natural ecosystems (ONEs). Within the Indian Thar Desert, such land-use transformations are altering native grassland habitats, with consequential effects on insect communities that perform vital ecological functions and support higher trophic levels. Between 2020 and 2022, we surveyed a 641 km² area, using belt transect and visual detection methods, to quantify insect densities at the order level across different seasons. Linear mixed-effect (LME) models revealed that the orthopteran insect densities, primarily grasshoppers, were significantly higher in grasslands compared to agriculture and barren lands and were lower in the presence of livestock grazing. Orthopteran densities were higher and showed strong seasonal dependencies, likely driven by rainfall-mediated vegetation growth during monsoons. Intense grazing and agricultural expansion reduced vegetation biomass and resource availability, which affected the insect populations negatively. These research findings underscore the urgent need to implement ecologically sensitive land management practices, including sustainable grazing regimes and grassland conservation, to maintain insect biodiversity and the broader ecological network. Given the role of insects in ecosystem functioning and their importance to conservation dependent species of, such as the critically endangered Great Indian Bustard (Ardeotis nigriceps), these findings underscore the ecological significance of preserving native grassland habitats in the Thar Desert landscape. Full article

Large-scale vegetation loss induced by surface coal mining constitutes a critical driver of regional ecological degradation. However, the applicability of existing change detection methodologies based on remote sensing within complex mining areas under diverse climatic conditions remains systematically unverified. To address this gap and reveal nationwide disturbance patterns, this study systematically evaluates the performance of two algorithms—Continuous Change Detection and Classification (CCDC) and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr)—in identifying vegetation loss across three major climatic zones of China (the humid, semi-humid, and semi-arid zones). Based on the optimal algorithm, the vegetation loss year and loss magnitude across all of China’s surface coal mining areas from 1990 to 2020 were accurately identified, enabling the reconstruction of the comprehensive, nationwide spatio-temporal pattern of mining-induced vegetation loss over the past 30 years. The results show that: (1) CCDC demonstrated superior stability and significantly higher accuracy (OA = 0.82) than LandTrendr (OA = 0.31) in identifying loss years across all zones. (2) The cumulative vegetation loss area reached 1429.68 km², with semi-arid zones accounting for 86.76%. Temporal analysis revealed a continuous expansion of the loss area from 2003 to 2013, followed by a distinct inflection point and decline during 2014–2016 attributable to policy-driven regulations. (3) Further analysis revealed significant variations in the average magnitude of loss across different climatic zones, namely semi-arid (0.11), semi-humid (0.21), and humid (0.25). These findings underscore the imperative for region-specific restoration strategies to ensure effective conservation outcomes. This study provides a systematic quantification and analysis of long-term, nationwide evolution patterns and regional differentiation characteristics of vegetation loss induced by surface coal mining in China, offering critical support for sustainable development decision-making in balancing energy development and ecological conservation. Full article

Xylella fastidiosa was first detected in Portugal in 2019 in Lavandula dentata. In response, the national plant health authorities promptly established a Demarcated Zone in the affected area and implemented a series of eradication and control measures, including the systematic removal and destruction of infected and host plants. This study analyzes the economic and operational impacts of these eradication efforts in the northern region of Portugal, with a focus on Demarcated Zones such as the Porto Metropolitan Area, Sabrosa, Alijó, Baião, Mirandela, Mirandela II, and Bougado between 2019 and June 2023. During this period, about 412,500 plants were uprooted. The majority were Pteridium aquilinum (bracken fern), with 360,324 individuals (87.3%), reflecting its wide distribution and the large area affected. Olea europaea (olive tree) was the second most common species removed, with 7024 plants (1.7%), highlighting its economic relevance. Other notable species included Quercus robur (3511; 0.85%), Pelargonium graveolens (3509; 0.85%), and Rosa spp. (1106; 0.27%). Overall, destruction costs were estimated at about EUR 1.04 million, with replanting costs of roughly EUR 6.81 million. In parallel, prospection activities—conducted to detect early signs of infection and monitor disease spread—generated expenses of roughly EUR 5.94 million. While prospecting represents a significant financial investment, the results show that it is considerably more cost-effective than large-scale eradication. Prospection enables early detection and containment, preventing the widespread destruction of vegetation and minimizing disruption to agricultural production, biodiversity, and local communities. Importantly, our findings reveal a sharp decline in confirmed cases in the initial outbreak area—the Porto Demarcated Zone—from 124 cases in 2019 to just 5 in 2023, indicating the effectiveness of the eradication and monitoring measures implemented. However, the presence of 20 active Demarcated Zones across the country as of 2023 highlights the continued risk of spread and the need for sustained vigilance. The complexity of managing Xylella fastidiosa across ecologically and logistically diverse territories justifies the high costs associated with surveillance and targeted interventions. This study reinforces the strategic value of prospection as a proactive and sustainable tool for plant health management. Effective surveillance requires the integration of advanced methodologies aligned with the phenological stages of host plants and the biological cycle of vectors. Targeting high-risk locations, optimizing sample numbers, ensuring diagnostic accuracy, and maintaining continuous training for field teams are critical for improving efficiency and reducing costs. Ultimately, the findings underscore the need to refine and adapt monitoring and eradication strategies to contain the pathogen, safeguard agricultural systems, and prevent Xylella fastidiosa from becoming endemic in Portugal. Full article

Coal mining disturbances in semi-arid grasslands affect land surface phenology (LSP), impacting ecosystem functions, restoration target setting, and carbon sequestration; however, the magnitude and spatial extent of these disturbances and their detectability across vegetation indices (VIs), remain insufficiently constrained. We developed and applied a streamlined quantitative framework to delineate the extent and intensity of mining-induced phenological disturbance and to compare the sensitivity and stability of commonly used VIs. Using Harmonized Landsat Sentinel (HLS) surface reflectance data over the Yimin mine, we reconstructed multitemporal VI trajectories and derived phenological metrics; directional phenology gradients were used to delineate disturbance, and VI responsiveness was evaluated via mean difference (MD) and standard deviation (SD) between affected and control areas. Research findings indicate that the impact of mining extends to an area approximately four times the size of the mining site, with the start of season (SOS) in affected areas occurring about 10 days later than in unaffected areas. Responses varied markedly among VIs, with the Modified Soil-Adjusted Vegetation Index (MSAVI) exhibiting the highest spectral stability under disturbance. This framework yields an information-rich quantification of phenological impacts attributable to mining and provides operational guidance for index selection and the prioritization of restoration and environmental management in semi-arid mining landscapes. Full article

We here delve into the intricate and evolving concepts of brain death and consciousness, particularly at the end of life. We examine the historical and technological advancements that have influenced our understanding of death, such as mechanical ventilation and resuscitation techniques. These developments have challenged traditional definitions of death, leading to the concept of brain death, defined as the irreversible loss of all brain functions, including the brainstem. We emphasize that consciousness exists on a continuum, ranging from full alertness to deep coma and complete cessation of brain activity. It explores various disorders of consciousness, including coma, vegetative state, minimally conscious state, and locked-in syndrome, each with distinct characteristics and levels of awareness. Neuroimaging techniques, such as EEG, fMRI, and DTI, are highlighted for their crucial role in diagnosing and understanding disorders of consciousness. These techniques help to detect covert consciousness, assess brain activity, and predict recovery potential. The phenomenon of the “wave of death,” which includes a paradoxical surge in brain activity at the point of death, is also discussed. We address the challenges in defining and understanding both death and consciousness, calling for biologically grounded, ethically defensible, and culturally sensitive definitions. We advocate for standardized neuroimaging protocols, longitudinal studies, and the integration of artificial intelligence to improve diagnosis and treatment. In conclusion, the document underscores the importance of an integrated, evidence-based approach to understanding the gray zones between life and death, recognizing that consciousness and death are dynamic processes with both biological and experiential dimensions. Full article

Accurate tree health monitoring in orchards is essential for optimal orchard production. This study investigates the efficacy of a deep learning-based object detection single-step method for detecting tree health on multispectral UAV imagery. A modified Mask R-CNN framework is employed with four different backbones—ResNet-50, ResNet-101, ResNeXt-101, and Swin Transformer—on three image combinations: (1) RGB images, (2) 5-band multispectral images comprising RGB, Red-Edge, and Near-Infrared (NIR) bands, and (3) three principal components (3PCs) computed from the reflectance of the five spectral bands and twelve associated vegetation index images. The Mask R-CNN, having a ResNeXt-101 backbone, and applied to the 5-band multispectral images, consistently outperforms other configurations, with an F1-score of 85.68% and a mean Intersection over Union (mIoU) of 92.85%. To address the class imbalance, class weighting and focal loss were integrated into the model, yielding improvements in the detection of the minority class, i.e., the unhealthy trees. The tested method has the advantage of allowing the detection of unhealthy trees over UAV images using a single-step approach. Full article

Background: Airborne transmission of bacteria, viruses, and fungal spores poses a major threat in enclosed settings, particularly nursing homes where residents are highly vulnerable. Compressive Heating Air Sterilization Technology (CHAST) applies compressive heating to inactivate microorganisms without reliance on filtration or chemicals. Methods: CHAST efficacy was evaluated in laboratory and deployed for a feasibility and performance validation study of air sterilization in a nursing home environment. Laboratory studies tested prototypes (300–5000 CFM; 220–247 °C) against aerosolized surrogates including Bacillus globigii (Bg), B. stearothermophilus (Bst), B. thuringiensis (Bt), Escherichia coli, and MS2 bacteriophage. Viral inactivation thresholds were further assessed by exposing MS2 to progressively lower treatment temperatures (64.5–143 °C). Feasibility and performance validation evaluation involved continuous operation of two CHAST units in a nursing home, with pre- and post-treatment air samples analyzed for bacterial and fungal burden. Results: Laboratory testing demonstrated consistent microbial inactivation, with most prototypes achieving > 6-log (99.9999%) reductions across bacterial spores, vegetative bacteria, and viruses. A 5000 CFM prototype achieved > 7-log (99.99999%) elimination of B. globigii. MS2 was completely inactivated at 240 °C, with modeling suggesting a threshold for total viral elimination near 170 °C. In the feasibility study, baseline sampling revealed bacterial (35 CFU/m³) and fungal (17 CFU/m³) contamination, dominated by Bacillus, Staphylococcus, Cladosporium, and Penicillium. After 72 h of CHAST operation, discharge air contained no detectable viable organisms, and fungal spore counts showed a 93% reduction relative to baseline return air. Units maintained stable operation (464 °F ± 2 °F; 329–335 CFM) throughout deployment. Conclusion: CHAST reproducibly and scalably inactivated airborne bacteria, viruses, and fungi under laboratory and feasibility field studies, supporting its potential as a chemical-free strategy to improve infection control and indoor air quality in healthcare facilities. Full article

Arabinogalactan proteins (AGPs: hydroxyproline-rich glycoproteins) are ubiquitous in plants and play various functions in cases of development and reproduction. In Arabidopsis thaliana some AGPs can work as markers for gametophytic cell differentiation (among others embryological structures they mark generative cell wall and/or plasma membrane, and also sperm cells). However, apart from Arabidopsis, this labeling of generative cell and sperm cells in pollen grains has only been observed in a few flowering plant species belonging to dicotyledons. No such studies are available in monocotyledons. The main aim of our study was to see whether AGPs would be present at the generative cell–vegetative cell interface in different monocotyledons (representatives of Asparagaceae, Amarylidaceae and Liliaceae), and we also wanted to test whether they would be the same AGPs as in dicotyledons. For the study, we selected Gagea lutea (L.) Ker Gawl., Ornithogalum nutans L. and Galanthus nivalis L. species that differ in shape and size of generative cells. Antibodies against arabinogalactan proteins AGPs were used, including JIM8, JIM13, JIM14, MAC207, LM2, LM14, JIM15 and JIM4. The localization of the examined compounds was determined using immunohistochemistry techniques. The key finding was that AGPs (detected with JIM8 and JIM13 antibodies) consistently mark the boundary between the generative cell and the surrounding vegetative cytoplasm, suggesting their association with the generative cell–vegetative cell interface in all species studied. Identifying such molecular markers in male gametophyte may enhance the understanding of gametophytic cell fate, sperm cell identity and the molecular mechanisms underlying fertilization. Such labeling may also be useful in studies on pollen development, species comparisons, or responses to environmental stresses. Full article