Search Results (1,569)

The study investigates the spatial variability of the cone index (CI) and its implications for vehicle mobility across two contrasting regions in Poland: the Suwalki Gap and Garwolin County. Background motivation stems from the need to assess off-road trafficability for agricultural, forestry, and other vehicles operating on soils whose strength varies seasonally and spatially. Using 230 penetrometric measurements collected with an electronic Penetrologger equipped with a soil moisture sensor, CI values were recorded to a depth of 80 cm and supported with soil–agricultural maps and Sentinel-2 land-cover data. Results demonstrate clear relationships between CI, soil moisture, land cover, soil type, and depth. Wetlands exhibited consistently low CI (<1 MPa), while agricultural, artificial, and forested areas showed increasing resistance with depth, surpassing 2 MPa in deeper layers. Seasonal differences were pronounced: summer drying increased surface CI, whereas autumn profiles were generally softer but more uniform. Regression analysis confirmed a strong negative correlation between soil moisture and CI, particularly below 20 cm. Comparative assessment with vehicle cone index thresholds indicates that most terrains are suitable for heavy vehicles, except saturated wetlands, which pose significant trafficability constraints. The findings emphasize the importance of depth-specific CI assessment, the strong influence of local soil disturbances, and the need for high-density measurements to support real-time mobility modelling for agricultural and crisis-management applications. Full article

Blue–green spaces serve as the core carriers of urban ecosystems, and their conservation and optimization have emerged as pivotal issues in territorial spatial planning and ecological governance. Taking Guilin, a national innovation demonstration zone for China’s Sustainable Development Agenda, as the study area, a deep learning-based DBDTAF-Net classification model is constructed using 2020 Sentinel-2 remote sensing imagery and AW3D30 Digital Surface Model (DSM) data. The model achieves a mean Intersection-over-Union (mIoU) of 86.05% on the test set and an IoU of 94.67% for rocky desertification areas. Based on the classification results, 21 derived indicators (including landscape patterns of BGSs) and six meteorological and topographic factors, alongside three core ecosystem service indicators—Aboveground Biomass (AGB), Net Primary Productivity (NPP), and soil conservation—are extracted to characterize their spatial patterns. The XGBoost-SHAP framework is employed to quantify the driving effects and threshold responses of BGS patterns on ecosystem services. The results indicate that (1) BGSs in Guilin display a spatial pattern of “green-dominated, blue-supplemented, generally contiguous yet locally fragmented,” and all three ecosystem services exhibit significant spatial clustering. (2) Landscape pattern factors of green spaces constitute the dominant influencing factors, with contribution rates ranging from 22.3% to 28.6%. Specifically, green space_COHESION demonstrates a stable linear positive effect. A green space ratio below 45% suppresses AGB, whereas exceeding 45% shifts to a positive effect and represents an efficient enhancement interval for NPP while exerting a continuously positive influence on soil conservation. A cultivated land proportion below 30% leads to a strongly increasing inhibitory effect on AGB and soil conservation, whereas its inhibition on NPP weakens beyond 20%. A construction land proportion exceeding 10% significantly suppresses NPP, and the inhibitory effect stabilizes above 20%. Green space patch density below 0.8 shows a pronounced negative effect, which diminishes above 0.8. Blue space factors exert relatively weak effects. (3) The ecosystem service supply capacity varies across functional zones in Guilin, with the ecological barrier zone performing the best, the modern agricultural zone performing moderately, and the six central urban districts of the Shanshui Metropolis Area exhibiting the lowest levels. This study provides a technical framework for high-precision extraction of urban BGSs and quantitative analysis of factors influencing ecosystem services, offers decision support for ecological conservation and restoration in Guilin, and furthermore proposes insights for the coordinated development of rational land resource utilization and ecosystem service enhancement in other karst cities. Full article

Spatial information on soil clay content addresses critical needs in precision agriculture and soil health assessment worldwide. This study utilizes a mapping workflow for topsoil clay (0–20 cm) across croplands in southern Russia using Sentinel-2 bare soil mosaics and regional covariates representing soil-forming factors. We generated 26 multi-temporal mosaics via per-pixel and per-date approaches and then tested five scenarios with regional covariates in combination with three variable selection techniques: variance inflation factor (VIF), recursive feature elimination (RFE), and modified greedy feature selection (MGFS). We found that among 26 temporal mosaics, the best single mosaic (scenario 1) explained 25% of the clay variation (RMSE = 10.03%, R² = 0.25, RPD = 1.16). Using only regional covariates selected after VIF and RFE approaches (scenario 2) yielded comparable results (RMSE = 9.77%, R² = 0.27, RPD = 1.18). Combination of the best bare soil mosaic and all regional covariates without the variable selection method (scenario 3) improved the predictions (RMSE = 9.45%, R² = 0.33, RPD = 1.24), and with the VIF/RFE application (scenario 4), the model showed slightly worse accuracy (RMSE = 9.65%, R² = 0.31, RPD = 1.21). MGFS implementation (scenario № 5) boosted the model performance and resulted in the best predictions (RMSE = 8.73%, R² = 0.42, RPD = 1.34). NIR bands from the bare soil mosaic, and terrain attributes with Landsat and MODIS variables from the regional covariate set were the key variables. We demonstrate that combining multi-temporal Sentinel-2 mosaics with regional covariates, when paired with an appropriate selection strategy, yields superior clay predictions. Full article

Accurate crop yield prediction increasingly relies on diverse data streams, including satellite observations, meteorological reanalysis, soil composition, and topographic information. However, despite advances in machine learning, many existing approaches remain crop- or region-specific and require substantial bespoke data engineering, limiting scalability and reproducibility. This study introduces UniCrop, a generalisable, configuration-driven data engineering pipeline that standardises the acquisition, harmonisation, and feature construction of multi-source agro-environmental data. Rather than proposing a new predictive model, UniCrop addresses a key bottleneck in agricultural machine learning: the lack of reproducible and scalable data preparation workflows. For any given location, crop type, and temporal window, the pipeline automatically retrieves, harmonises, and engineers over 160 environmental variables from heterogeneous sources (Sentinel-1/2, MODIS, ERA5-Land, NASA POWER, SoilGrids, and SRTM), reducing them to a compact, analysis-ready feature set using a structured feature selection process based on minimum redundancy maximum relevance (mRMR). The effectiveness of the pipeline is demonstrated through a case study, where the generated datasets enable robust baseline modelling across multiple machine-learning algorithms. Using a selected subset of 15 features, four baseline models (LightGBM, Random Forest, Support Vector Regression, and ElasticNet) were evaluated under rigorous cross-validation. LightGBM achieved the best single-model performance (RMSE = 465.1 kg/ha, $R^2=0.6576$), while a constrained ensemble provided a marginal improvement (RMSE = 463.2 kg/ha, $R^2=0.6604$). SHAP-based analysis further confirms that the selected features capture agronomically meaningful relationships across data modalities. UniCrop contributes a scalable and transparent data engineering pipeline that enables consistent, reproducible, and transferable dataset construction for crop yield prediction. By decoupling data specification from implementation and supporting flexible configuration across crops, regions, and temporal contexts, the framework provides a practical foundation for large-scale agricultural analytics. Full article

Rare earth elements are vital, non-renewable strategic resources, and their exploitation has significant impacts on regional ecological security and sustainable development. To address the issue of insufficient accuracy in land use classification in rare earth mining areas, this study takes the Lingbei rare earth mining area in Dingnan County, Jiangxi Province, as a case study. Multi-source remote sensing data, including Sentinel-2 imagery, Sentinel-1 SAR data, nighttime light data, and DEM data, were integrated to construct a feature set combining spectral, textural, and topographic information. On this basis, this study developed a feature optimization framework that combines recursive feature elimination (RFE), mean decrease accuracy (MDA), and K-fold cross-validation (CV), termed RFE-MDA-CV. We designed nine feature combination schemes and compared them with the optimal feature subset. Their performance was systematically evaluated across four classifiers: RF, SVM, CART, and GBDT. The results were as follows: (1) the optimized feature set combined with the RF classifier consistently achieved the highest classification performance, with a mean OA of approximately 93.2% and a kappa coefficient of about 0.916, outperforming CART and SVM by around 4-5 percentage points; (2) land use remained generally stable between 2016 and 2023, but frequent conversions occurred between forest land, cropland, and impervious surfaces, mainly driven by urban expansion and mining activities; and (3) cross-regional experiments demonstrated that the proposed feature optimization framework has good applicability and transferability in mining areas with similar geomorphological and metallogenic conditions. Overall, the proposed RFE-MDA-CV method can be effectively implemented on the Google Earth Engine platform, significantly improving the accuracy and robustness of land use classification in rare earth mining areas, while providing reliable technical support for ecological monitoring and land resource management. Full article

Influenza sentinel surveillance in Libya was formally established in 2022 by the Libyan National Center for Disease Control (NCDC). Between 2022 and 2024, a total of 1864 nasopharyngeal specimens were collected from patients presenting with influenza-like illness and tested using the GeneXpert for influenza A virus, influenza B virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV). Influenza A virus was detected in 21.1% (393/1864) of samples and influenza B virus was detected in 5.4% of samples (100/1864). SARS-CoV-2 and RSV were identified in 11.6% (216/1864) and 4.1% (77/1864) of specimens, respectively. A subset of 22 influenza A-positive samples was selected based on sample availability and sufficient remaining volume after the initial test for confirmatory testing and further molecular characterization. Real-time RT-PCR subtyping identified 11 A(H1N1)pdm09 and four A(H3N2) viruses. Whole-genome sequencing was successfully performed for 11 isolates, followed by phylogenetic analysis. Genetic characterization revealed that all A(H1N1)pdm09 viruses belonged to clade 6B.1A.5a.2a (5a.2a), while A(H3N2) viruses clustered within clade 3C.2a1b.2a.2a.3a.1 (2a.3a.1) were based on hemagglutinin gene mutations. No neuraminidase mutations associated with antiviral resistance were detected. This study represents the first molecular and phylogenetic characterization of circulating human influenza viruses in Libya, with sequence data submitted to the Global Initiative on Sharing All Influenza Data (GISAID) to establish baseline genetic data for influenza viruses in Libya. Full article

Net primary productivity (NPP) is an important indicator of ecosystem carbon accumulation capacity and vegetation productivity potential, and its accurate estimation is of great significance for agricultural management and regional carbon cycle research. To address the problem that the temporal continuity of single-source optical remote sensing data is easily affected by cloud cover, this study used Sentinel-2 imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as data sources and constructed an NDVI time series with high spatial and temporal resolution for the study area based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) method. On this basis, the Simple Ratio (SR) index was incorporated to supplement canopy information, and the key parameters of the Carnegie–Ames–Stanford Approach (CASA) model were differentially optimized for different crop types, thereby enabling remote sensing-based estimation of crop NPP. The results showed that the fused NDVI effectively compensated for observation gaps caused by cloud interference, and its temporal variation was generally consistent with the crop growth process. In addition, the Fraction of Photosynthetically Active Radiation (FPAR) improved with the fused NDVI, which effectively characterized phenological differences among crops. Compared with the unoptimized model, the improved model significantly improved NPP estimation accuracy for both maize and rice. Specifically, for maize, the coefficient of determination ($R^2$) increased from 0.75 to 0.88, and the mean absolute percentage error (MAPE) decreased from 67.00% to 34.68%. For rice, the MAPE decreased from 78.51% to 23.43%, while the mean absolute error (MAE) decreased from 345.1 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$ to 95.6 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$. These results indicate that constructing a highly continuous vegetation index time series through spatiotemporal fusion, together with optimizing the CASA model by incorporating the SR index and crop-specific parameterization, can effectively improve the stability and accuracy of NPP estimation for agricultural crops. Full article

Though intraperitoneal (IP) inoculation is not the natural pathway of Aleutian Mink Disease Virus (AMDV) infection in mink, it is frequently used experimentally. To investigate AMDV pathogenesis, we compared the effects of IP, aerosol (AE), and natural infection in mink. Forty-six sapphire mink were divided into groups: negative controls, IP and AE AMDV-inoculated mink, and sentinels exposed to IP-inoculated mink for two-week periods. Mink in the control, IP, and AE groups were euthanized 2, 5, or 10 weeks post-inoculation. The mink were tested for AMDV antibodies and by PCR on serum samples throughout the study, and by PCR and histology in organs after euthanasia. The sentinel mink were introduced to determine when the risk of natural transmission was highest. AMDV was detected in the sentinels exposed during weeks 3–6, indicating that AMDV transmission risk is highest early in infection, before antibody-positive animals can be detected on the farm. Infection in the AE group progressed more gradually than in the IP group, which developed more pronounced lesions and higher viral loads in the liver. Compared to IP inoculation, the aerosol model provides a superior experimental approach for studying natural infection and transmission of AMDV. Full article

Rice plant height is a key indicator of crop growth and phenology, yet continuous daily estimation remains challenging under limited field observations. This study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements. Daily plant height was estimated as a posterior-weighted ensemble of multiple LUT-derived heights, together with uncertainty reflecting ambiguity among plausible growth trajectories. Applied to rice paddies in Ryugasaki City, Japan, using Harmonized Landsat–Sentinel-2 data from the 2025 growing season, the method achieved $R^2=0.85$ and RMSE = 7.08 cm on the validation dataset, outperforming simple baseline approaches. The estimated daily height time series also enabled evaluation of the timing at which plant height reached 70 cm, revealing clear spatial variability among fields and an associated uncertainty of approximately 10 days. Although this threshold was discussed with reference to previous studies on L-band SAR sensitivity, the present study relied solely on optical observations. Overall, the proposed framework provides a data-efficient and explainable approach for daily, spatially explicit rice growth monitoring, while current limitations include the single-region, single-year LUT construction and the simplified statistical assumptions used in the Bayesian weighting framework. Full article

The Eurasian lynx (Lynx lynx) is an apex predator and an important sentinel for trophically transmitted helminths acquired via predation on wild ungulates. On 2 March 2022, an adult male lynx that was road-killed in the Apuseni Mountains (Surducel hunting ground, Bihor County) was collected, frozen for biosafety, and a necropsy was performed. Taeniid cestodes were detected, with a total intestinal burden of nine adult specimens. Genetic analyses confirmed Taenia lynciscapreoli, and the obtained sequences were deposited in GenBank (PV843597, PV855065, PV844409). Phylogenetic inference based on cox1 assigned the Romanian isolate within the European cluster, distinct from the Chinese isolate, while showing genetic proximity to Taenia sp. (MW846305) that have been reported from a lynx in China. This study represents the first molecular identification of T. lynciscapreoli in the Eurasian lynx in Romania and, to our knowledge, the first record from Southeastern Europe. Full article

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, this study proposes a quantitative spatiotemporal monitoring framework based on time-series Interferometric Synthetic Aperture Radar (InSAR) coherence to detect early-stage geotechnical stabilization. Using Sentinel-1 imagery of the Balongtu coal mine, a sliding-window detection algorithm was developed to capture the physical transition of surface electromagnetic scattering mechanisms from active disturbance to stable consolidation. The main findings are as follows: (1) Statistical analysis identified a critical geophysical coherence threshold of 0.15, which effectively and objectively distinguishes active dumping disturbance zones from structurally stable areas. (2) The spatiotemporal evolution dynamics of the completed dump areas from 2017 to 2023 were successfully characterized, revealing that 87.6% of the open-pit areas achieved physical stabilization within three years post-mining, with a spatial distribution highly consistent with the objective operational rule of “mining first, dumping later”. (3) Accuracy assessment using 700 spatiotemporally balanced validation points—derived through strict visual interpretation of high-resolution optical imagery—demonstrated high algorithm reliability, achieving overall accuracies (OA) of 87.57% and 90.43% at half-yearly and annual monitoring intervals, respectively. By decoupling physical surface stabilization from optical greenness, this study provides a timely abiotic precursor indicator, offering scientific, quantitative decision support for precision ecological zoning and accelerated land turnover approval in mining areas. Full article

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

Turbid Class II inland waters such as Taihu Lake exhibit a “spectral uplift” effect driven by suspended particulate matter (SPM) scattering and colored dissolved organic matter (CDOM) absorption, which can obscure chlorophyll-a (Chl-a) signals in the visible–red-edge region and challenge retrieval under small-sample, collinear feature settings. Using multispectral observations from the BC-1A satellite (carrying the Lightweight Hyperspectral Remote Sensing Imager, LHRSI) and synchronous satellite–ground in situ measurements acquired over Taihu Lake in late autumn, this study proposes Chl-a-oriented PCA–RF (COP-RF), a leakage-safe inversion framework integrating correlation screening, principal component analysis (PCA), and random forest (RF) regression. Candidate band-combination features are generated, and PCA is applied for orthogonal compression to mitigate collinearity before RF learning. A stratified five-fold cross-validation based on Chl-a quantile bins is adopted, with screening, standardization, and PCA fitted only on training folds. COP-RF achieves stable performance under the current dataset ($R^2=0.671$, RMSE $=1.80\mathsf{\mu }$g/L, MAE $=1.25\mathsf{\mu }$g/L). Spatial inversion shows higher Chl-a near shores and bays and lower values in the lake center, consistent with Sentinel-2 hotspot ranks. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article