Search Results (456)

Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data. Full article

Monitoring and protecting flowing watercourses is a complex and challenging task that requires the collaboration and coordination of various stakeholders such as governments, industries, farmers, consumers and environmental groups. The study of the dynamics of the concentration of polluting factors and especially the concentrations of heavy metals and highlighting a seasonal variation is a necessary element from this point of view. In this article, we present the results of our analyses carried out in two measurement campaigns executed in 10 monitoring points along the Danube River, between Braila city and Isaccea city in the pre-deltaic area, during the summer season and autumn season 2022. The importance of this area is given by the fact that the Danube Delta is part of the UNESCO heritage, and the monitoring of polluting factors is a necessity in the desire to protect this area. The data measured during the July and August 2022 campaign cover a wide range of chemical species: Phosphate, CCO, CBO₅, NH₄⁺, N-NO₂, N-NO₃⁻, N-Total, P-PO₄ ³⁻, SO₄²⁻, Cl⁻, phenols, as well as metals with a harmful effect: Al, As, Cd, Cr, Fe. The study includes an evaluation based on the statistical approach of the results to highlight the significant correlations and differences identified between the two data sets. Next, to highlight the obtained results, a numerical model was considered using HEC-RAS and ESRI ArcGIS applications in a two-dimensional unsteady flow model in order to obtain the non-homogenous concentrations’ distributions in the studied area. These two-dimensional models have been less studied in the specialized literature. In this way, interesting results could be obtained, and prediction methods regarding the dynamics of metal concentrations could be structured. The data obtained were used for the terrain model from the USGS service, and the flows of the Danube and its two tributaries were simulated using the data provided by the national services. In this work, we present the results obtained for the dynamics of the concentrations of the metals Al, As, Cd, Cr and Fe and the evaluation of the specific absorption coefficients for the explanation and correlation with the results of the measurements. Except for the numerical model presented, we would like to highlight the existence of some contributions of the main tributaries of the Danube in the study area. Such a systematic study has not been carried out due to conditions imposed by the border authorities. From this point of view, this study has an element of originality. The study is part of a more complex project in which the spatio-temporal distribution of the polluting factors in the water was evaluated, and the habitats in the study area were inventoried—especially those of community interest. In this way, we were able to expose the self-purification capacity of the Danube and highlight the existence of a concentration reduction gradient along the course of the river. The aspects related to the influence of the distribution of polluting factors on the state of health will be the subject of another article. Full article

Drill-bit metamorphism (DBM) is the process of thermal degradation of drilling fluid at the interface of the bit and rock due to the overheating of the bit. The heat generated by the drill when drilling into a rock formation promotes the generation of artificial hydrocarbon and non-hydrocarbon gas, changing the composition of the gas. The objective of this work is to recognize and evaluate artificial gases originating from DBM in wells targeting oil accumulations in pre-salt carbonates in the Santos Basin, Brazil. For the evaluation, chromatographic data from advanced mud gas equipment, drilling parameters, drill type, and lithology were used. The molar concentrations of gases and gas ratios (especially ethene/ethene+ethane and dryness) were analyzed, which identified the occurrence of DBM. DBM is most severe when wells penetrate igneous and carbonate rocks with diamond-impregnated drill bits. The rate of penetration, weight on bit, and rotation per minute were evaluated together with gas data but did not present good correlations to assist in identifying DBM. The depth intervals over which artificial gases formed during DBM are recognized should not be used to infer pay zones or predict the composition and properties of reservoir fluids because the gas composition is completely changed. Full article

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution. Full article

Background/Objectives: Physical activity is widely recognized for its beneficial effects on bone density during adolescence, which could lead to enhanced bone density in later life, thus acting as a health-promoting activity with long-lasting implications. However, not all studies are conclusive regarding the type, intensity, duration, and frequency of the most effective physical activities. This study focuses on combat sports athletes and examines the relationship between their somatic build and heel bone parameters using ultrasound (USG) and their vitamin D3 levels. Methods: The study included 40 male athletes specializing in various combat sports. The measurements of body height, body mass, skinfold thickness, and bone widths at multiple sites were performed to estimate the somatic build. The USG parameters of the heel bone and the blood levels of vitamin D3 were also recorded. Statistical significance was determined using one-way ANOVA, with differences among sports disciplines also examined. Results: The study found significant differences in the body composition and USG bone parameters among athletes from different combat sports (p ≤ 0.05). The calcaneus stiffness index (SI) and speed of sound (SOS) were significantly higher in athletes with normal vitamin D3 levels compared to those with below-normal levels (p = 0.0015 and p = 0.001, respectively). These findings suggest that vitamin D3 may influence bone stiffness and density. Conclusions: The study underscores the importance of maintaining adequate vitamin D3 levels to support bone mineralization in athletes, particularly those training indoors with limited exposure to sunlight. It also highlights the potential of using USG as a non-invasive method to assess bone health, aiding in the optimization of training programs to prevent injuries and improve performance. Full article

Accurate precipitation estimates are crucial for various hydrological and environmental applications. This study presents a comprehensive evaluation of three widely used satellite-based precipitation datasets (SPDs)—PERSIANN, CHIRPS, and MERRA—and a monthly reanalysis dataset—TERRA—that include data from across the contiguous United States (CONUS) and Hawaii, at daily, monthly, and yearly timescales. We present the performance of these SPDs using ground-based observations maintained by the USGS (United States Geological Survey). We employ evaluation metrics, such as the coefficient of determination (R²), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), to identify optimal SPDs. Our findings reveal that MERRA outperforms PERSIANN and CHIRPS on a daily scale, while CHIRPS is the best-performing dataset on a monthly scale. However, all datasets show limitations in accurately estimating absolute amount of precipitation totals. The spatial analysis highlights regional variations in the datasets’ performance, with MERRA consistently performing well across most regions, while CHIRPS and PERSIANN show strengths in specific areas and months. We also observe a consistent seasonal pattern in the performance of all datasets. This study contributes to the growing body of knowledge on satellite precipitation estimates and their applications, guiding the selection of suitable datasets based on the required temporal resolution and regional context. As such SPDs continue to evolve, ongoing evaluation and improvement efforts are crucial to enhance their reliability and support informed decision-making in various fields, including water resource management, agricultural planning, and climate studies. Full article

This study applied Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data and ZY1-02D hyperspectral data to map the structural distribution and hydrothermal alteration in the polymetallic ore district in southern Shangri-La City, Yunnan Province, China. The study area hosts several polymetallic deposits, including the Mahuaping tungsten–beryllium deposit, which has significant mineral exploration potential. The deposit type is mainly magmatic–hydrothermal, with average grades of 0.41% WO3 and 0.22% BeO, and substantial reserves, prominently controlled by faults. Based on this, this study employed ASTER data for the visual interpretation of structures through false-color composites combined with DEM data. Additionally, ASTER and ZY1-02D data were processed using the principal component analysis and spectral angle mapper methods to extract anomalies related to tungsten mineralization such as carbonate alteration, sericitization, chloritization, and hematization of the hydrothermal origin. The results indicated that the structural trends in the study area predominantly align in north–south and northeast directions, with alteration anomalies concentrated in the central and fold areas. Our analysis of typical deposits revealed their close association with north–south faults and east–west joints, as well as the enrichment level of alteration anomalies, identifying five high-potential target areas for mineral exploration. Further evaluation involved field validation through the spectral scanning of samples, field verification, and a comparison with known lithology. These assessments confirmed that the spectral curves matched those in the USGS database, the structural interpretations aligned with the field observations (84% accuracy from 25 sampling points, with 21 matching extracted alteration types), and the alteration results corresponded well with the lithological units, indicating high accuracy in alteration extraction. Finally, a comparative discussion highlighted that the results derived from ZY1-02D data were more applicable to the local area. The outcomes of this study can support subsequent mineral exploration efforts, enhancing the sustainability of important mineral resources. Full article

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth’s surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4–2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes. Full article

Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this study, the monitoring sites were established at two different locations to collect hydraulic data for the hydraulic calibration and validation of the model. In addition, water quality samples were collected at eight monitoring sites and analyzed in the lab for various parameters for calibration. This includes total suspended solids (TSS), soluble phosphorus, five-day biochemical oxygen demand (BOD₅), and dissolved oxygen (DO). The Personal Computer Storm Water Management Model (PCSWMM) 7.6 software was used to simulate all the pollutant loads using event mean concentrations (EMCs). The performance of the model for streamflow calibration at the two USGS gauging stations was satisfactory, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.51 to 0.54 and coefficients of determination (R²) ranging from 0.71 to 0.72. The model was also validated with the help of historical flow data with NSE values ranging from 0.5 to 0.79, and R² values ranging from 0.6 to 0.95. The hydraulic calibration also showed acceptable results with reasonable NSE and R² values. The water quality data recorded at the monitoring stations were then compared with the simulated water quality modeling results. The model reasonably simulated the water quality, which was evaluated through visual inspection using a scatter plot. Our analysis showed that the upstream tributaries, particularly from agricultural areas, were contributing more pollutants than the downstream tributaries. Overall, this study demonstrates that the PCSWMM, which was typically used for modeling urban watersheds, could also be used for modeling larger mixed land use watersheds with reasonable accuracy. Full article

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height. Full article

Accurate land surface temperature (LST) retrieval from satellite data is pivotal in environmental monitoring and scientific research. This study addresses the impact of variability in the effective wavelengths used for LST retrieval from the Thermal Infrared Sensor (TIRS) data of Landsat 8. We conduct a detailed analysis comparing the effective wavelengths reported by Yu et al. (2014) and those derived from data provided by the USGS. Our analysis reveals significant variability in the effective wavelengths for bands 10 and 11 of Landsat 8. By applying Planck’s Law and utilizing the K1 and K2 coefficients available in the metadata of Landsat 8 products, we derive the effective wavelengths for bands 10 and 11. We also rederive the effective wavelength by integrating the spectral response function of the TIRS1 sensor. Our findings indicate that the effective wavelength for band 10 is 10.814 μm, aligning with the USGS data, while the effective wavelength for band 11 is 12.013 μm. We discuss the implications of these corrected effective wavelengths on the accuracy of LST retrieval algorithms, particularly the single channel algorithm (SC) and the radiative transfer equation (RT) employed by Yu et al. The importance of using precise effective wavelengths in satellite-based temperature retrieval is emphasized, to ensure the reliability and consistency of results. This analysis underscores the critical role of accurate spectral calibration parameters in remote sensing studies and provides valuable insights in the field of land surface temperature retrieval from Landsat 8 TIRS data. Full article

Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model. We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy (R²), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT’s continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900–1250 nm and 1500–1780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR. Full article

Background and Objectives: We aimed to develop a predictive model for the outcome of bruxism treatments using ultrasonography (USG)-based machine learning (ML) techniques. This study is a quantitative research study (predictive modeling study) in which different treatment methods applied to bruxism patients are evaluated through artificial intelligence. Materials and Methods: The study population comprised 102 participants with bruxism in three treatment groups: Manual therapy, Manual therapy and Kinesio Tape or Botulinum Toxin-A injection. USG imaging was performed on the masseter muscle to calculate muscle thickness, and pain thresholds were evaluated using an algometer. A radiomics platform was utilized to handle imaging and clinical data, as well as to perform a subsequent radiomics statistical analysis. Results: The area under the curve (AUC) values of all machine learning methods ranged from 0.772 to 0.986 for the training data and from 0.394 to 0.848 for the test data. The Support Vector Machine (SVM) led to excellent discrimination between bruxism and normal patients from USG images. Radiomics characteristics in pre-treatment ultrasound scans of patients, showing coarse and nonuniform muscles, were associated with a greater chance of less effective pain reduction outcomes. Conclusions: This study has introduced a machine learning model using SVM analysis on ultrasound (USG) images for bruxism patients, which can detect masseter muscle changes on USG. Support Vector Machine regression analysis showed the combined ML models can also predict the outcome of the pain reduction. Full article

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage. Full article

Diabetes, especially type 2 diabetes (T2D), poses an unprecedented challenge to global public health. Hydration status also plays a fundamental role in human health, especially in people with T2D, which is often overlooked. This study aimed to explore the longitudinal associations between hydration status and the risk of T2D among the Chinese population. This study used data from the large community-based Kailuan cohort, which included adults who attended physical examinations from 2006 to 2007 and were followed until 2020. A total of 71,526 participants who eventually met the standards were divided into five hydration-status groups based on their levels of urine specific gravity (USG). Multivariable and time-dependent Cox proportional hazards models were employed to evaluate the associations of baseline and time-dependent hydration status with T2D incidence. Restricted cubic splines (RCS) analysis was used to examine the dose–response relationship between hydration status and the risk of T2D. Over a median 12.22-year follow-up time, 11,804 of the participants developed T2D. Compared with the optimal hydration-status group, participants with dehydration and severe dehydration had a significantly increased risk of diabetes, with adjusted hazard ratios (95% CI) of 1.30 (1.04–1.63) and 1.38 (1.10–1.74). Time-dependent analyses further confirmed the adverse effects of impending dehydration, dehydration, and severe dehydration on T2D incidence by 16%, 26%, and 33% compared with the reference group. Inadequate hydration is significantly associated with increased risks of T2D among Chinese adults. Our findings provided new epidemiological evidence and highlighted the potential role of adequate hydration status in the early prevention of T2D development. Full article