Search Results (8,746)

Slaughterhouse detection of lesions compatible with bovine tuberculosis represents a key passive surveillance component in Santa Catarina, Brazil, yet subsequent trace-back investigations often fail to identify infected farms. This study developed a quantitative framework to prioritize epidemiological investigations by estimating the probability of infection associated with each farm connected to PCR-confirmed cases. Using official movement records and historical diagnostic data, we reconstructed the lifetime contact networks of slaughtered cattle presenting confirmed Mycobacterium bovis lesions (n = 502). For each sentinel animal–farm interaction (n = 1452), infection probability was estimated through a non-homogeneous Poisson process incorporating exposure duration and the time-weighted average herd size as determinants of infectious pressure. After evaluating stochastic variability through Monte Carlo simulation, a deterministic model using the mean infectious-pressure parameter was applied to classify farms into high-, medium-, and low-risk categories. Model performance was assessed using validated field diagnostic outcomes within a three-year temporal window. High-risk farms represented most validated contacts (58%) and demonstrated a relative risk of 3.48 compared with lower-risk category. These findings indicate that a standardized risk-based classification can substantially improve the prioritization of trace-back investigations, offering a practical decision-support tool to enhance bovine tuberculosis surveillance and contribute to eradication strategies in Santa Catarina. Full article

Evaluating Net Primary Productivity (NPP) variations driven by climatic variability and mining activities is fundamental for understanding ecological dynamics in high-intensity open-pit mining areas. Focusing on high-intensity open-pit mining areas of Qian’an, China, from 2016 to 2022, by integrating Sentinel-2, ERA-5 Land reanalysis dataset and Dynamic World V1, we employed an improved Carnegie–Ames–Stanford Approach (CASA) framework alongside the Thornthwaite Memorial algorithm to quantify actual NPP (ANPP) and potential NPP (PNPP). Additionally, the Relative Contribution Index (RCI) was utilized to explicitly isolate mining-driven NPP (MNPP) variations. The results revealed a significant downward trajectory in ANPP within the high-intensity open-pit mining area, with a cumulative reduction of 5.3 × 10⁸ gC a⁻¹. This productivity loss exhibited significant spatial heterogeneity, with the most severe degradation concentrated in core mining districts, including Malanzhuang, Caiyuan, Yangdianzi, and Muchangkou. ANPP, MNPP, and PNPP maintained relative stability overall but displayed significant interannual fluctuations during 2019–2022. RCI analysis indicated MNPP dominated ANPP in 62.67% of the study area, with mining impacts intensifying in 62.83% of the region. Driver mechanisms identified precipitation as the dominant climatic factor enhancing ANPP, whereas mining activities constituted the primary driver of ANPP reduction. Mining accounted for 61.33% of ANPP changes, significantly exceeding climatic variability’s 38.67% contribution. In conclusion, these findings provide a scientific foundation for developing ecological carbon sink systems and optimizing ecological restoration strategies. Full article

Snow density plays a crucial role in water resource estimation, runoff forecasting, and early warning of natural disasters such as avalanches and blizzards. This study uses optical satellite multispectral reflectance data to retrieve snow density, providing a novel perspective for snow density retrieval research. Supported by auxiliary data including CanSWE in situ measurements, Sentinel-2 satellite data, and ERA5-Land reanalysis data, this study constructs a hybrid model (Snow_ACMix) that integrates the strengths of the multi-head attention mechanism and convolutional neural networks, realizing direct snow density retrieval from multispectral satellite reflectance data for the first time. This research was primarily conducted in Canada and Alaska. For the Canadian region, the model achieves a mean absolute error (MAE) of 0.034 g/cm³, a root mean square error (RMSE) of 0.051 g/cm³, and a coefficient of determination (R²) of 0.547. For the Alaska region, the model yields an MAE of 0.020 g/cm³, an RMSE of 0.029 g/cm³, and an R² of 0.803. Feature and module ablation experiments are carried out, and one-shot transfer learning is adopted to perform snow density retrieval in the Alaska region. The spatial transfer prediction results show an MAE of 0.027 g/cm³, an RMSE of 0.038 g/cm³, and an R² of 0.747, which verify the model’s excellent spatial generalization ability and superior performance in data-scarce regions. The advantages and limitations of the Snow_ACMix model are investigated through comparative validation across different land cover types, regions, time periods, and against ERA5 data. The Snow_ACMix model achieves favorable retrieval performance in mountainous areas, and its practical application capability is verified by snow density retrieval in the Silver Star Mountain region. However, the model still has limitations: it is vulnerable to the effects of wet snow, resulting in large fluctuations in retrieval results in wet snow regions. Full article

Time-series InSAR is an important means for early identification and monitoring of landslides. However, in complex mountainous areas, it still faces challenges such as significant geometric distortions and complicated deformation mechanisms. To address these issues, this paper proposes a landslide identification and prediction framework that integrates ascending and descending orbits InSAR observations with physics-guided deep learning. Taking Yangbi County, Yunnan Province, as a case study, we combined ascending and descending Sentinel-1A data and employed the SBAS-InSAR method to identify potential landslides, detecting a total of 41 hazardous sites. The cumulative displacement time series of typical landslides were further extracted along the slope aspect to more realistically reflect landslide movement characteristics. On this basis, wavelet decomposition was introduced to separate the displacement series into trend and periodic components. Gray relational analysis was then used to select influencing factors such as precipitation and temperature, and a stepwise prediction model based on LSTM (WT-LSTM) was constructed. The results indicate that the model achieves significantly higher prediction accuracy at characteristic points of the representative landslide (RMSE = 1.16–2.19 mm) compared to standalone LSTM and SVR models. These findings demonstrate its effectiveness and potential applicability in landslide deformation monitoring and prediction in complex mountainous areas, while also providing a useful reference for landslide risk early warning. Full article

Understanding the interplay between tree structure and seasonal dynamics, particularly cross-species, is crucial for managing urban forest ecosystems. However, balancing fine-scale inventory of trees with large-area mapping of forest ecosystems is a challenge. This endeavor integrates multi-temporal Sentinel-2 satellite remote sensing (RS) imagery with high-density handheld light detection and ranging (LiDAR) point clouds to launch exploratory analyses of cross-species phenological–structural relationships (CSPSRs) in urban park trees. We derived plot-level phenological metrics (e.g., start of growing season, SOS) and quantified fine-scale three-dimensional (3D) tree structural attributes (e.g., tree height and trunk curvature), respectively. Then, we investigated how the 3D structural attributes of urban park trees covary with their phenological traits. The results revealed the underlying CSPSRs, e.g., a weak but significant negative correlation between SOS and tree height in the study area. The derived CSPSRs demonstrate that tree structure is a key predictor of its phenology, even across species. Overall, the integrated RS approach can provide a robust framework for associating the structure and phenology of trees, offering valuable insights for the ecological management of urban forests. Full article

Tropical forests are major contributors to the global carbon budget but are affected by disturbances such as hurricanes, which cause extensive yet spatially variable tree damage and mortality. High-resolution maps of forest aboveground biomass (AGB) and its temporal change aid in quantifying disturbance impacts, assessing resilience, and supporting forest management. This study presents wall-to-wall, high-resolution mapping of pre- and post-hurricane AGB and AGB change across Puerto Rico. The maps represent forest AGB measured 0–2 years before and after two major hurricanes (Irma and Maria), as well as longer-term conditions up to four years post-disturbance. AGB was modeled using Random Forest (RF) algorithms that integrated Forest Inventory and Analysis (FIA) plot data with canopy height and cover derived from discrete-return LiDAR, multi-temporal satellite imagery, and additional geospatial predictors. Model performance was evaluated using a 10% holdout dataset. Predicted versus observed regressions yielded, at 10 m and 90 m spatial resolutions, respectively, r = 0.75 and 0.79 with model residual mean standard deviation (RMSD) = 87.7 and 39.2 Mg ha⁻¹ for pre-hurricane AGB, and r = 0.77 and 0.74 with RMSD = 69.7 and 58.1 Mg ha⁻¹ for post-hurricane AGB. AGB change models at 10 m and 90 m resolutions yielded r = 0.58 and 0.73 with RMSD = 17.0 and 18.7 Mg ha⁻¹, respectively. Ten-fold cross-validation produced stronger correlations and reduced RMSD values. Frequency distributions of mapped pixels of forest AGB and AGB change, in comparison with previously published maps and island-wide field-based estimates, indicate that, although hurricane-driven biomass reductions of up to 20% were recorded in field data, patterns consistent with longer-term recovery from historical deforestation are evident within four years after the hurricanes. The 10 m maps capture fine-scale heterogeneity in canopy damage and regrowth, whereas the 90 m maps emphasize broader regional patterns. This integrated framework provides a transferable approach for monitoring forest structure and biomass dynamics in disturbance-prone tropical ecosystems. Full article

Pitahaya (Hylocereus spp.) production is increasingly affected by climatic factors, as well as by phytopathogens and abiotic stress, leading to delays in agronomic interventions and reduced productivity. The objective was to design, implement, and validate a multimodal system (CARYPAR) that enables early disease detection and agile decision-making, characterized by low latency and reduced dependence on cloud connectivity. The methodology integrates climate reanalysis from NASA POWER, biophysical remote sensing variables derived from Sentinel-1/2, and proximal computer vision captured via mobile devices using a late fusion architecture and an optimized convolutional neural network, EfficientNet-V2B0, which discriminates between optimal and pathological conditions in vegetative tissues and fruit. The results of the experimental validation carried out in 160 georeferenced units achieved an overall accuracy of 80.0% and an F1 score of 0.8645 for Bad Fruit. The McNemar test and the operational agreement with agro-industrial experts yielded a Cohen’s Kappa index of κ = 0.6831, with an inference latency reduced to 22.00 ms. It is concluded that the multimodal integration of satellite bio-environmental data with edge computer vision achieves substantial agreement with agronomic expert judgment under heterogeneous field conditions (Cohen’s κ = 0.6831), supporting its role as a decision-support tool rather than a replacement for expert assessment. Therefore, its adoption can enhance real-time irrigation management and crop protection, while contributing to traceability and sustainable resource management in agricultural regions with limited connectivity. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

The Nile River has played a central role in Egypt’s historical and cultural development, shaping ancient civilizations and settlement patterns. However, its course has changed dynamically over millennia, leaving behind buried channels and geomorphological features that are critical for reconstructing past hydrological landscapes. This study utilized Sentinel-2 satellite imagery within Google Earth Engine to develop a remote sensing method for analyzing spectral and temporal variations in vegetation as indicators of paleofluvial landforms and past river activity. The approach, applied to create ten seasonal representations, enhanced the detection of moisture-driven vegetation patterns. Here, the Moisture-Gradient Enhanced Vegetation Index (MGEVI) was developed to identify stable vegetated landforms and differentiate persistent moisture conditions from seasonal variations. Through this method, former river channels, river islands, and channel belts were identified, revealing patterns of past river activities. The results suggest a late anabranching phase of the Nile, characterized by the gradual stabilization of fluvial features in response to evolving hydrological conditions. A comparison between fluvial features identified through remote sensing and those mapped from TanDEM-X radar elevation data and historical maps revealed strong agreement, affirming the reliability of the remote sensing approach developed by this study. Evidence from sediment core analyses, stratigraphic correlation, and high-precision RTK field surveys further corroborated the existence of ancient, buried channels and islands within the study area. The study highlights the utility of multi-temporal satellite imagery analysis for reconstructing hydrological evolution and assessing past settlement suitability. Specifically, an inferred paleochannel near the Dendera Temple Complex suggests a possible hydrological connection between a former course of the Nile River and this archaeological site. These findings underscore the potential of remote sensing for large-scale geoarchaeological studies, offering scalable methodologies for identifying ancient river networks and supporting cultural heritage conservation in arid regions. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

Monitoring avocado (Persea americana Mill., cv. Semil-34) in tropical mountain landscapes of Cambita, San Cristóbal, Dominican Republic is inherently complex due to the pronounced topographical and climatic heterogeneity that modulates the crop’s ecophysiological responses, specifically vegetative vigor, carbon allocation, and the synchronization of reproductive flushes. This study integrates 5-year (2020–2025) Sentinel-2 time series, ERA5-Land climatic variables (air temperature, total precipitation, and radiation), and geomorphometric covariates to explain variability in yield and fruit quality. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Red Edge (NDRE), and Normalized Difference Moisture Index (NDMI), were analyzed using Partial Least Squares Regression (PLSR) to characterize phenological dynamics and rank dominant predictors. The results revealed coherent spectral phenological trajectories; however, a significant inverse relationship was detected between canopy vigor and yield during reproductive phases. High vegetation index values were significantly and negatively associated with lower production (r = −0.58, p < 0.0021), reflecting a potential source–sink imbalance. Topography functioned as a structural filter, regulating root drainage and productive stability across the landscape. While yield variability was partially explainable (R² = 0.38), internal fruit quality, measured as dry matter content, exhibited comparatively high environmental stability. A central contribution of this research lies in identifying the “vigor paradox” in cv. Semil-34 and the suggestion that topography may exert a stronger influence than direct spectral signals under tropical hillside conditions. These findings provide an exploratory framework for anticipating yield and fruit quality through satellite remote sensing or UAVs, supporting site-specific management decisions in mountain agricultural systems. Full article

This review provides a comprehensive bibliometric analysis of the literature on the integration of remote sensing data and machine learning or deep learning algorithms in precision agriculture. The analysis covers 1056 publications, included in the Web of Science Core Collection, and identifies the temporal patterns of research, the most frequently used algorithms, the prominent remote sensing technologies, and the geographical distribution of research output. Increased research output during the period of 2013–2025 is attributed to the availability of high-level computing, satellites, and UAV imagery. The earlier studies in machine learning primarily involved the use of the Random Forest and Support Vector Machine algorithms, whereas in the past few years, deep learning, and especially Convolutional Neural Networks, have become more dominant. The most widely used data sources in remote sensing are the imagery from UAVs and the Sentinel satellite missions. The evaluation revealed that most of the geographical research activity was centered in the United States and China, but there is a trend of increasing research activity in most of the other developed countries. Research in Africa and South America remains particularly underdeveloped. Considering the rapid development of research, data fusion of optical and radar satellite imagery, UAV imagery, weather and soil datasets are expected to further improve the representation of agricultural systems. Full article

Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer’s accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin. Full article

Background: Sebaceous carcinoma (SC) is a rare malignant cutaneous malignancy. Methods: A multicenter retrospective study of 213 German patients with SC diagnosed between 2008 and 2024 was conducted. Data were extracted from the Baden-Württemberg Cancer Registry. Cases were separated into ocular and extraocular SC. Their demographic, clinical, and treatment-related characteristics were compared and influences on overall survival (OS) analyzed. Results: Most patients were elderly (median age: 79 years), with a male-to-female ratio of 2:1. Extraocular SC was more common in men, while ocular SC was more frequent in women. Most tumors were diagnosed at stage I, and microscopically controlled excision was the primary treatment modality (81.4%). Sentinel lymph node biopsy (2.3%), lymph node dissection (1.9%) and systemic therapy (1.4%) were only documented in a minority of cases. Survival analysis (median follow-up 3.2 years) revealed a median OS of 61.4 months in the entire cohort. No significant survival difference was observed between ocular and extraocular SC (64.8 vs. 53.7 months; p = 0.490), and multivariable analysis confirmed no prognostic impact of tumor localization (HR 1.4, 95% CI 0.85–2.4). Age was the only independent predictor of outcome, with strongly increased risk in patients aged 70–79 years (HR 4.4, 95% CI 1.01–19.2) and ≥80 years (HR 16.1, 95% CI 3.91–66.1). Prior malignancies, including MTS-like tumors and hematological neoplasms, were not independently associated with overall survival. Conclusions: In this multicenter cohort, sebaceous carcinoma showed no survival difference between ocular and extraocular disease, with age emerging as the main independent prognostic factor. Prior malignancies and tumor characteristics, including histologic grade, were not independently associated with outcome. Microscopically controlled excision appears to be an effective treatment option. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) remains a leading cause of emergency department (ED) presentation, hospitalisation, and preventable healthcare utilisation worldwide. Although guidelines advocate coordinated, preventative, and community-based management, care within ED settings often remains reactive and crisis-driven. Nurses occupy a central role in COPD management; however, the experiential dimensions of nursing practice and its contribution to improving patient outcomes are insufficiently understood. Objectives: To explore the lived experiences of patients, nurses and medical officers regarding COPD presentations to the ED, with particular focus on the nursing role in assessment, coordination, education, and identification of unmet and comorbid care needs. Methods: A qualitative phenomenological approach was undertaken across three regional Australian EDs. Purposive sampling recruited patients presenting with acute exacerbations of COPD and nursing and medical officers involved in their care. Semi-structured interviews were conducted and transcribed verbatim. Data were analysed using Braun and Clarke’s thematic analysis framework, supported by reflexive discussion and audit trails to enhance rigour. Results: Six interrelated themes were identified: (1) nursing within a “crisis first” model of care; (2) holistic assessment and translation of complexity; (3) education and care coordination as preventative nursing work; (4) relational care and therapeutic connection; (5) nurses as sentinels for undiagnosed comorbidities, particularly obstructive sleep apnoea; and (6) system pressures constraining optimal nursing practice. Participants consistently described nurses as the clinicians who stabilised acute episodes, interpreted contextual risks, coordinated services, and provided relational and educational support, yet whose preventative contributions were limited by time and organisational demands. Conclusions: ED nurses function as critical integrators between acute stabilisation and chronic disease management for patients with COPD. Formalising nurse-led assessment, education, coordination, and sleep-disordered breathing screening may reduce avoidable ED presentations and enhance patient-centred outcomes. Investment in structured nursing models represents a key opportunity for improving COPD care delivery. Full article