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Global Navigation Satellite Systems (GNSS) tropospheric tomography is a commonly used technique for the reconstruction of three-dimensional water vapour field, and a priori vertical constraint models are required for water vapour density (WVD) determination which plays a critical role in the quality of tomographic results. However, generalised exponential models were routinely used for vertical constraints and limited research was carried out in the GNSS tomography by taking epoch-by-epoch variations into consideration. In this study, an adaptive four-dimensional (4-D) WVD model for the vertical constraints in GNSS tropospheric tomography was developed based on both ERA5 and surface meteorological data in Hong Kong for each month during the period of 2015–2019, and the back-propagation neural network technique was used to develop the fitting model. Then, the WVD model was used to obtain the WVD of adjacent voxels in the vertical direction to alleviate the mis-representation of the generalised exponential model. The newly developed WVD model used in GNSS tropospheric tomography was validated using GNSS data from the Hong Kong region in the year 2020 and two tomographic epochs (00:00–00:30 UTC and 12:00–12:30 UTC) were evaluated each day. For each topographic epoch, the WVDs of the tomographic voxels including radiosonde profile are evaluated (10 voxels over 10 height layers) using radiosonde data as the reference and the WVDs of all tomographic voxels are evaluated (300 voxels over 10 height layers) using ERA5 data as the reference. Results showed that when radiosonde/ERA5 data were utilized as the references, corresponding monthly mean values of the root mean square errors (RMSEs) in the entire year reduced from 1.97/1.94 g/m3 of the traditional tomographic method to 1.56/1.36 g/m3 of the new method which showed approximately 21/30% improvements. These results suggest a better performance of the tomographic approach using the new WVD model for the vertical constraints proposed by this study by taking epoch-by-epoch information. [ABSTRACT FROM AUTHOR]

With the proliferation of low Earth orbit (LEO) satellites carrying GNSS receivers on‐board commercial operators such as Spire, Starlink, OneWeb, and Amazon, an abundance of high‐cadence tracking data could become available to the scientific community. While GNSS measurements from geodetic‐grade receivers on satellites like SWARM, CHAMP, GRACE, and GOCE have been extensively used for atmospheric density retrieval, limited research has explored the potential of less accurate data from commercial operators. This study focuses on two methods to estimate atmospheric densities from precision orbit determination (POD) products—precise positions and velocities—utilizing synthetic data sets. The first method, termed "POD accelerometry" treats the POD products as measurements to a reduced‐dynamic POD scheme with the goal of estimating densities using stochastic parameters. The second method known as the energy dissipation rate (EDR) approach derives densities from changes in orbital energy. The relative contributions of various error sources—dynamics model uncertainties, and POD noise—to the estimated densities are studied for a limited set of orbital regimes and space weather activity, and possible error mitigation strategies are suggested. The performance of the two methods and their sensitivities to these various error sources are compared for circular orbits in the altitude regime 300–800 km during solar minimum F10.7=72.5 $\left({F}_{10.7}=72.5\right)$. EDR and POD accelerometry have comparable performances for high drag, low POD noise environments, whereas the latter performs considerably better in low drag <10−6m/s2 $\left(< 1{0}^{-6}\ \mathrm{m}/{\mathrm{s}}^{\mathrm{2}}\right)$, high POD noise (>25 ${ >} 25$ cm) environments, with densities retrieved at higher cadences for the orbital regimes considered in this work during solar minimum. Plain Language Summary: Low Earth orbit (LEO) Satellites orbit the Earth within its tenuous upper atmosphere. The uncertainties in the modeling of the atmospheric density lead to considerable errors in the prediction of satellite orbits that can have drastic consequences in the increasingly overcrowded LEO regime. Therefore, real‐time estimates of the upper atmospheric density are very beneficial in improving the predictive capability of atmospheric models. This research focuses on using tracking data from satellites equipped with GNSS receivers to estimate atmospheric densities in LEO. Specifically, the study analyzes two methods that derive density estimates from precision orbit determination (POD) products. The research examines the impact of different error sources on the accuracy of density estimates during quiescent conditions in solar minimum. Strategies to mitigate these errors are also suggested. Overall, this research contributes to understanding the effectiveness of different methods for deriving atmospheric densities from POD products obtained from GNSS‐equipped satellites in LEO for the nominal space weather conditions considered here. It highlights the importance of considering various error sources and provides the reader with the trade‐offs in selecting a method to estimate atmospheric densities from tracking data in different operational conditions for circular satellite orbits during solar minimum. Key Points: Precision orbit determination (POD) accelerometry and energy dissipation rate (EDR) are evaluated for density retrieval in 300–800 km at solar minimum, assessing performance, and error sensitivityPOD accelerometry enables better handling of dynamical errors and excels in low‐drag, high‐noise environmentsEDR proves to be a viable option in high‐drag environments and low POD uncertainties [ABSTRACT FROM AUTHOR]

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Zenith tropospheric delay (ZTD) is an important atmospheric parameter in radio‐space‐geodetic techniques such as Global Navigation Satellite System (GNSS), which is pivotal for GNSS positioning, navigation and meteorology. The Vienna Mapping Function (VMF) data server is a widely utilized source for implementing ZTD, offering two types of models, that is, the empirical one and the discrete one with Grid‐wise and Site‐wise models. Therefore, to evaluate the accuracy of these models becomes the focus of this article. Specifically, this study investigates their performances in terms of calculation of ZTD, using the hourly values derived from the International GNSS Service data as references. The results show that the root mean square err (RMSE) of the Site‐wise, Grid‐wise and global pressure and temperature 3 model are 11.71/13.03/38.56 mm, respectively, indicating the discrete model performs generally better than the empirical model, and the Site‐wise model is the better of the two discrete models. From the perspective of spatial resolution, the performance of these three models in ZTD calculation shows obvious influences of latitude changes and elevation differences. From the temporal analysis, the accuracy of the discrete model shows differences over different UTC epochs, while the empirical model can only express the seasonal ZTD characteristics with the average RMSE at different epochs being similar, the specifically values are 39.67, 39.26, 39.38 and 39.18 mm at UTC 0:00, 6:00, 12:00 and 18:00, respectively. The histogram and boxplot well indicate the accuracy differences of the three models in different seasons. Additionally, the time series of three models at different latitudes were also explored in this research. These explorations are conducive to the selection of appropriate models for calculating ZTD based on specific requirements. Key Points: The discrete and empirical troposphere delay models of the VMF data server is comprehensibly evaluated using the global International GNSS Service (IGS) dataThe Site‐wise of discrete model exhibits the best accuracy and is recommended first, followed by the Grid‐wide and the empirical modelEach model shows different influences by the latitude, elevation, seasons and epochs [ABSTRACT FROM AUTHOR]

Visual Simultaneous Localization and Mapping (V-SLAM) is pivotal for precise positioning and mapping. However, visual data from crowd-sourced datasets often contains deficiencies that may lead to positioning errors. Despite existing optimization techniques, current algorithms do not adequately adapt to varied data in vehicle driving scenarios. To address this gap, this study introduces a novel SLAM framework (SLG-SLAM). This framework refines trajectories by integrating semantic information, laser point cloud, and global navigation satellite system (GNSS) data into V-SLAM. Initial trajectory estimates are made after filtering out dynamic targets and are subsequently refined with matched laser point clouds, then corrected for scale and direction using GNSS. The efficacy of this approach is assessed using four public datasets and one self-collected dataset, showing significant enhancements across all datasets. The proposed method reduces the mean absolute trajectory error by 43.50% on the KITTI dataset and 14.91% on the MVE dataset compared to the baseline. Unlike the baseline, which fails on three other datasets, the proposed method successfully performs localization and mapping. Additionally, compared to three other single-source methods (DynaSLAM, MCL, MVSLAM), the proposed method consistently outperforms, demonstrating its superior adaptability and effectiveness.

Abstract The identification and removal of outliers in time series are important problems in numerous fields. In this paper, a novel method (BCP-HI) is proposed to enhance the accuracy of outlier detection in GNSS coordinate time series by combining Bayesian change point (BCP) analysis and the Hampel identifier (HI). By using BCP, change points (cps) in the time series are lidentified, and so the time series is divided into subsegments that have properties of a normal distribution. In each of these separated segments, outliers are detected using HI. Each data element identified as an outlier is corrected by a median filter of window size (w) to obtain the corrected signal. The BCP-HI method was tested on both simulated and real GNSS coordinate time series. Outliers from three different synthetic test datasets with different sampling frequencies and outlier amplitudes were detected with approximately 98% accuracy after processing. After this process, Signal-to-Noise Ratio (SNR) increased from 0.0084 to 10.8714 dB and Root Mean Square (RMS) decreased from 24 to 23 mm. Similarly, for real GNSS data, approximately 98% accuracy was achieved, with an increase in SNR from 0.0003 to 4.4082 dB and a decrease in RMS from 7.6 to 6.6 mm observed. In addition, the output signals after BCP-HI were examined graphically using Lomb–Scargle periodograms and it was observed that clearer power spectrum distributions emerged. When the input and output signals were examined using the Kolmogorov–Smirnov (KS) test, they were found to be statistically similar. These results indicate that the BCP-HI algorithm effectively removes outliers, and enhances processing accuracy and reliability, and improves signal quality.

The identification and removal of outliers in time series are important problems in numerous fields. In this paper, a novel method (BCP-HI) is proposed to enhance the accuracy of outlier detection in GNSS coordinate time series by combining Bayesian change point (BCP) analysis and the Hampel identifier (HI). By using BCP, change points (cps) in the time series are lidentified, and so the time series is divided into subsegments that have properties of a normal distribution. In each of these separated segments, outliers are detected using HI. Each data element identified as an outlier is corrected by a median filter of window size (w) to obtain the corrected signal. The BCP-HI method was tested on both simulated and real GNSS coordinate time series. Outliers from three different synthetic test datasets with different sampling frequencies and outlier amplitudes were detected with approximately 98% accuracy after processing. After this process, Signal-to-Noise Ratio (SNR) increased from 0.0084 to 10.8714 dB and Root Mean Square (RMS) decreased from 24 to 23 mm. Similarly, for real GNSS data, approximately 98% accuracy was achieved, with an increase in SNR from 0.0003 to 4.4082 dB and a decrease in RMS from 7.6 to 6.6 mm observed. In addition, the output signals after BCP-HI were examined graphically using Lomb–Scargle periodograms and it was observed that clearer power spectrum distributions emerged. When the input and output signals were examined using the Kolmogorov–Smirnov (KS) test, they were found to be statistically similar. These results indicate that the BCP-HI algorithm effectively removes outliers, and enhances processing accuracy and reliability, and improves signal quality. [ABSTRACT FROM AUTHOR]

The Continuously Operating Reference Station (CORS) network has several stations that collect and record data from Global Navigation Satellite System (GNSS) constellations. These stations run continuously in an automated manner to collaborate and interact with different users for positioning services. In this study, the collected GNSS data at five CORS sites of the Iraqi Geodetic network was assessed to determine if there were any significant changes in the positions of these stations for a long time. This change gives a clear interpretation of the impact of different geophysical phenomena (e.g. tectonic plate motion) on the stability of positions within time. Therefore, the collected data at ZAXO, ISER, ISBA, ISKU and ISNA stations were analysed for seven years 2015 – 2022 (i.e. from 1st Jan 2015 to 31st Dec 2021). The daily position of each station can be computed from 24-hour recording data at a 30-second rate using GAPS or CSRS free software for Precise Point Positioning (PPP). Thus, 8806 observation daily files were used. The processed data was represented as a time series in Easting, Northing and Up coordinates for each station to analyse the trend of movement in these stations. It was found that the change in both Easting and Northing coordinates has a linear trend, which agrees with the general trend of tectonic motion in this part of the world. The mean yearly change was within 22-27 mm. On the other hand, the change in the Up coordinate with time had a fluctuated change over time "wave behaviour" or exhibited periodic variations.

In this paper, we kinematically analyze the movements of plates and blocks of the region of southeastern Turkey, where strong earthquakes occurred on February 6, 2023, based on a homogeneous database of displacement velocities of GNSS permanent monitoring stations. Along the East Anatolian fault zone from 2008 to 2018, the Arabian Plate was established to shift relative to the Anatolian Plate, which corresponds to a left shift (without a normal component) at a rate from 1 cm/yr in the eastern part to 0.8 cm/yr in the western part. Along the Chardak fault, displacements corresponding to the left shift occurred at a rate of less than 0.7 cm/year. The revealed kinematics is confirmed by focal mechanisms and cosesismic displacements of the studied earthquakes. The M7.5 earthquake that occurred directly on the Chardak fault is not an aftershock of the M7.8 earthquake, but is a relatively independent event. An analysis of the seismic regime shows that the stresses on the East Anatolian fault after the main M7.8 event are relieved by the first large latitudinal fault zone (the Chardak fault). The results of our study suggest that the counterclockwise rotation of the Anatolian and Arabian plates associated with the opening of the Red Sea Rift is most likely decisive for the general kinematics of the plates in the region. [ABSTRACT FROM AUTHOR]

Increasingly accurate, and spatio-temporally dense, measurements of Earth surface movements enable us to identify multiple deformation patterns and highlight the need to properly characterize the related source processes. This is particularly important in tectonically active areas, where deformation measurement is crucial for monitoring ongoing processes and assessing future hazard. Long Valley Caldera, California, USA, is a volcanic area where frequent episodes of unrest involve inflation and increased seismicity. Ground- and satellite-based instruments show that volcanic inflation renewed in 2011, and is continuing as of early 2021. Additionally, Long Valley Caldera is affected by the large, but spatially and temporally variable, amounts of precipitation falling on the adjacent Sierra Nevada Range. The density and long duration of deformation measurements at Long Valley Caldera provide an excellent collection of data to decompose time-series and separate multiple superimposed deformation sources. We analyze Global Navigation Satellite System (GNSS) time-series and apply variational Bayesian Independent Component Analysis (vbICA) decomposition method to isolate inflation-related signals from other processes. We show that hydrological forcing causes significant horizontal and vertical deformation at different temporal (seasonal and multiyear) and spatial (few to hundreds of km) scales that cannot be ignored while analyzing and modeling the tectonic signal. Focusing on the last inflation episode, we then improve on prior simplistic models of the inflation reservoir by including heterogeneous subsurface material properties and topography. Our results suggest the persistence and stability of the reservoir (prolate ellipsoid at about 8 km beneath the resurgent dome) and indicate a 40-50% reduction of the inflation rate after about 3 years from the inflation onset. The onset of the reduced inflation rate corresponded in time with the occurrence of a strong seismic swarm in the Caldera, but also to the temporal variation of climatic conditions in the area.

The main goal of this study is to develop and test an algorithm for determining the moment of inertia and angular momentum of a tectonic plate based on the processing of time series of daily solutions of continuous GNSS (Global Navigation Satellite System) stations. The proposed algorithm consists of four consecutive stages: reformatting data to the internal format; dividing the plate into cells and determining their masses; determining the rotation poles and distances from cells to the poles; calculating the plate’s moment of inertia and angular momentum. The algorithm uses freely available time series of daily solutions from continuous GNSS stations or any other data prepared in a similar format. The algorithm is tested for determining the moment of inertia and angular momentum of the Antarctic plate based on the processing of time series of daily solutions of continuous GNSS stations for the period 1995–2021. It is confirmed that the Antarctic Plate’s rotation poles, moment of inertia, and angular momentum are dynamic parameters. However, additional calculations and in-depth comprehensive analysis are required to determine the causes of such dynamics. As a result of comparing, the dynamics of changes in the Antarctic Plate’s rotation poles partially compensate for the unevenness of the Earth's rotation to keep the angular momentum of the Earth constant.

The total electron content (TEC) derived from the Global Navigation Satellite System (GNSS) has been widely used to study the ionosphere. The evaluation of the derivation method is usually based on the fitting residuals, which are the difference between the estimate and observation. However, there are few studies on the statistical characteristics of fitting residuals. The characteristics of fitting residuals and estimation errors are studied with international reference ionosphere (IRI-2016) and GNSS observations. First, the systematic estimation errors caused by mathematical modeling and related assumptions are discussed with IRI-2016. The errors are 1 TECU and 0.5 TECU for slant TEC (STEC) and vertical TEC (vTEC), respectively. The fitting residuals of STEC observations (Δ STEC r ) are studied by different noise models, including independent and identically distributed (IID) Gaussian noise, independent and non identical distributed (INID) Gaussian noise and INID Laplace noise. The statistical analysis shows that the distribution of Δ STEC r for the whole observations of all receivers is Gaussian when the noise is IID Gaussian, while it is Laplace for INID Gaussian or Laplace noise. The Δ STEC r reflects the statistical characteristic of noise. The estimation error of STEC (Δ STEC), which is the difference between the estimate ( STEC e ) and the model STEC (STEC) is discussed with IRI-2016. The distribution of Δ STEC is neither Gaussian nor Laplace, and STEC e is a biased estimate of STEC. The distribution of estimation error of vTEC (Δ vTEC) is neither Gaussian nor Laplace. Finally, the Δ STEC r is studied with GNSS observations. The distribution of Δ STEC r is more consistent with Laplace than Gaussian, indicating that the noise of the GNSS observations should not be modeled as IID Gaussian distribution. [ABSTRACT FROM AUTHOR]

This paper presents the study of crustal movements and their relation to earthquake activities within Egypt using the GNSS and seismicity measurements. Fourteen campaigns of GNSS measurements have been collected, processed, and adjusted. The collected data of about 50 permanent and campaign GNSS stations, which covered the whole geographic area of Egypt covering about 12 years, were used. The collected data are utilised using differential GPS with surrounded IGS stations by Bernese software V 5.0. The results of the deformation analysis indicate that northern Egypt is deformed more than the southern part. [ABSTRACT FROM AUTHOR]

This research deals with the problem of the accessibility of urban forests and parks for people with disabilities in wheelchairs. Through an analysis of spatial data, trails were categorized on the basis of the measurements of the independent movements of eight subjects on six trails. The critical longitudinal slopes were determined for independent movement in wheelchairs, complex independent movement with certain risk, and categories of trails that are not suitable for wheelchair movement. The results indicate that a slope of 5.50% was the tipping point, after which all respondents experienced some uncertainty. In addition, a gradient of more than 9.01% was almost impassable for people in wheelchairs, with respect to the gravel trails examined in this study. Modern measurement technology was used in the field, including the mobile apps GAIA GPS and GNSS GPS, and a total (geodetic) station; the subjects' heart rates were measured using a Garmin Fenix 6 Pro watch. In addition, people with disabilities were included in the implementation of the research through the Croatian Association of Paraplegics and Tetraplegics (HUPT). The results indicate the zones/trails that disabled people in wheelchairs can pass independently, the zones that can be accessed with a certain risk, and those that are not accessible, all according to the defined longitudinal slope zones using measurements of the movement of people in a wheelchair, their heart rate loads, and personal communication with the subjects regarding the insecurity they felt. [ABSTRACT FROM AUTHOR]

The detection of deformation in Global Navigation Satellite System (GNSS) time series associated with (a)seismic events down to a low magnitude is still a challenging issue. The presence of a considerable amount of noise in the data makes it difficult to reveal patterns of small ground deformation. Traditional analyses and methodologies are able to effectively retrieve the deformation associated with medium to large magnitude events. However, the automatic detection and characterization of such events is still a complex task, because traditionally employed methods often separate the time series analysis from the source characterization. Here we propose a first end‐to‐end framework to characterize seismic sources using geodetic data by means of deep learning, which can be an efficient alternative to the traditional workflow, possibly overcoming its performance. We exploit three different geodetic data representations in order to leverage the intrinsic spatio‐temporal structure of the GNSS noise and the target signal associated with (slow) earthquake deformation. We employ time series, images, and image time series to account for the temporal, spatial, and spatio‐temporal domain, respectively. Thereafter, we design and develop a specific deep learning model for each dataset. We analyze the performance of the tested models both on synthetic and real data from North Japan, showing that image time series of geodetic deformation can be an effective data representation to embed the spatio‐temporal evolution, with the associated deep learning method outperforming the other two. Therefore, jointly accounting for the spatial and temporal evolution may be the key to effectively detect and characterize fast or slow earthquakes. Plain Language Summary: The continuous monitoring of ground displacement with Global Navigation Satellite System allowed, at the beginning of the 2000s, the discovery of slow earthquakes—a transient slow slippage of tectonic faults that releases stress without generating seismic waves. Nevertheless, the detection of small events is still a challenge, because they are hidden in the noise. Most of the methods which are traditionally employed are able to extract the deformation down to a certain signal‐to‐noise level. However, one can ask if deep learning can be a more efficient and powerful alternative. To this end, we address the problem by using deep learning, as it stands as a powerful way to automatize and possibly overcome traditional methods. We use and compare three data representations, that is time series, images, and image time series of deformation, which account for the temporal, spatial, and spatio‐temporal variability, respectively. We train our methods on synthetic data, since real datasets are still not enough to be effectively employed with deep learning, and we test on synthetic and real data as well, claiming that image time series and its associated deep learning model may be more effective toward the study of the slow deformation. Key Points: We develop deep learning approaches on synthetics mimicking the spatio‐temporal structure of static deformation and realistic Global Navigation Satellite System (GNSS) noiseWe design three deep learning models and we train and test them against three GNSS data representationsTransformers and image time series of deformation can effectively characterize small deformation patterns associated with the seismic source [ABSTRACT FROM AUTHOR]

We introduce GUARDIAN, a near-real-time (NRT) ionospheric monitoring software for natural hazards warning. GUARDIAN’s ultimate goal is to use NRT total electronic content (TEC) time series to (1) allow users to explore ionospheric TEC perturbations due to natural and anthropogenic events on earth, (2) automatically detect those perturbations, and (3) characterize potential natural hazards. The main goal of GUARDIAN is to provide an augmentation to existing natural hazards early warning systems (EWS). This contribution focuses mainly on objective (1): collecting GNSS measurements in NRT, computing TEC time series, and displaying them on a public website (). We validate the time series obtained in NRT using well-established post-processing methods. Furthermore, we present an inverse modeling proof of concept to obtain tsunami wave parameters from TEC time series, contributing significantly to objective (3). Note that objectives (2) and (3) are only introduced here as parts of the general architecture, and are not currently operational. In its current implementation, the GUARDIAN system uses more than 70 GNSS ground stations distributed around the Pacific Ring of Fire, and monitoring four GNSS constellations (GPS, Galileo, BDS, and GLONASS). As of today, and to the best of our knowledge, GUARDIAN is the only software available and capable of providing multi-GNSS NRT TEC time series over the Pacific region to the general public and scientific community. [ABSTRACT FROM AUTHOR]

The main goal of this study is to develop and test an algorithm for determining the moment of inertia and angular momentum of a tectonic plate based on the processing of time series of daily solutions of continuous GNSS (Global Navigation Satellite System) stations. The proposed algorithm consists of four consecutive stages: reformatting data to the internal format; dividing the plate into cells and determining their masses; determining the rotation poles and distances from cells to the poles; calculating the plate's moment of inertia and angular momentum. The algorithm uses freely available time series of daily solutions from continuous GNSS stations or any other data prepared in a similar format. The algorithm is tested for determining the moment of inertia and angular momentum of the Antarctic plate based on the processing of time series of daily solutions of continuous GNSS stations for the period 1995-2021. It is confirmed that the Antarctic Plate's rotation poles, moment of inertia, and angular momentum are dynamic parameters. However, additional calculations and in-depth comprehensive analysis are required to determine the causes of such dynamics. As a result of comparing, the dynamics of changes in the Antarctic Plate's rotation poles partially compensate for the unevenness of the Earth's rotation to keep the angular momentum of the Earth constant. [ABSTRACT FROM AUTHOR]

Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the "turning", "working", and "abnormal working" trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the "turning" and "abnormal working" trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing. [ABSTRACT FROM AUTHOR]

The increasing importance of utilizing new technologies, such as GNSS (Global navigation satellite system) devices, total stations, drones, scanners, etc., in forestry has become evident with the development of these tools. Combined with precise GNSS devices, UAVs represent an efficient tool that facilitates field measurements and reduces time spent in the field while also providing highly accurate data. To accurately determine the application of spatial data for calculating the longitudinal slope of forest/logging roads in a research project, measurements based on UAVs were conducted and compared with GNSS data. GNSS data were considered the reference values for the studied area, assuming that satellite positions and internet signals were good. An accurate longitudinal slope was necessary to define fuel consumption on forest/logging roads and slopes throughout the project and to verify the calculation method that could be applied for such or similar terrain conditions and overgrowth. The main goal of this research phase was not to study fuel consumption but to obtain an accurate longitudinal profile of forest/skid roads on rocky terrain that is not overgrown or poorly grown. The study put forth two hypotheses, one of which was ultimately confirmed, while the other was refuted. The results show no significant differences when comparing the absolute elevation of the points in the profiles for defining the longitudinal slope of the road using the program for designing forest/skid roads. While applying such a method of data collection to create the main projects for the (re)construction of forest/skid roads, there are significant deviations in earthwork masses. There was a difference of over 22.64% in one of the types of earthwork excavation on the designed forest road, so the method implemented in the research does not give sufficiently accurate results.

Least-squares collocation (LSC) is a widely used method applied in physical geodesy to separate observations into a signal and noise part but has received only little attention when interpolating velocity fields. The advantage of the LSC is the possibility to filter and interpolate as well as extrapolate the observations. Here, we will present several extensions to the traditional LSC technique, which allows the combined interpolation of both horizontal velocity components (horizontal velocity (HV)-LSC), the separation of velocity observations on different tectonic plates, and the removal of stationarity by moving variance (the latter as HV-LSC-ex(tended) 2 ). Furthermore, the covariance analysis, which is required to find necessary input parameters for the LSC, is extended by finding a suitable variance and correlation length using both horizontal velocity components at the same time. The traditional LSC and all extensions are tested on a synthetic dataset to find the signal at known as well as newly defined points, with stations separated on four different plates with distinct plate velocities. The methodologies are evaluated by calculation of a misfit to the input data, and implementation of a leave-one-out cross-validation and a Jackknife resampling. The largest improvement in terms of reduced misfit and stability of the interpolation can be obtained when plate boundaries are considered. In addition, any small-scale changes can be filtered out using the moving-variance approach and a smoother velocity field is obtained. In comparison with interpolation using the Kriging method, the fit is better using the new HV-LSC-ex 2 technique. [ABSTRACT FROM AUTHOR]

Offshore data in the western Ionian Sea indicate that the NW–SE-trending dextral shear zone of the Alfeo-Etna Fault System turns to the N–S direction near the Ionian coastline, where the extensional Timpe Fault System is located. Morpho-structural data show that NW–SE-trending right-lateral strike-slip faults connect the Timpe Fault System with the upper slope of the volcano, where the eruptive activity mainly occurs along the N–S to NE–SW-trending fissures. Fault systems are related to the ~E–W-trending extension and they are seismically active having given rise to shallow and low-moderate magnitude earthquakes in the last 150 years. As a whole, morpho-structural, geodetic and seismological data, seismic profiles and bathymetric maps suggest that similar geometric and kinematic features characterize the shear zone both on the eastern flank of the volcano and in the Ionian offshore. The Alfeo-Etna Fault System probably represents a major kinematic boundary in the western Ionian Sea associated with the Africa–Europe plate convergence since it accommodates, by right-lateral kinematics, the differential motion of adjacent western Ionian compartments. Along this major tectonic alignment, crustal structures such as releasing bends, pull-apart basins and extensional horsetails occur both offshore and on-land, where they probably represent the pathway for magma uprising from depth. [ABSTRACT FROM AUTHOR]

Ocean microplastic concentrations are known to vary significantly by location, with especially high levels in the North Atlantic and North Pacific gyres. Most direct measurements come from plankton net trawling made in these regions; concentrations in other regions have been estimated by microplastic transport models that depend on large-scale ocean circulation patterns. However, global measurements of microplastic distribution and its temporal variability are lacking. A new method is presented for detecting and imaging the global distribution of ocean microplastics from space. The method uses spaceborne bistatic radar measurements of ocean surface roughness and relies on an assumed reduction in responsiveness to wind-driven roughening caused by surfactants that act as tracers for microplastics near the surface. Annual mean microplastic distributions estimated by the radars are generally consistent with model predictions. The spaceborne observations are also able to detect temporal changes that are not resolved by the models. For example, seasonal dependencies are observed at mid-latitudes in both Northern and Southern Hemispheres, with lower concentrations noted in the winter months. Time lapse images at finer spatial and temporal scales reveal episodic bursts of microplastic tracers in the outflow from major river discharges into the sea. This new method will provide better monitoring of ocean microplastics and will support future model development and validation. [ABSTRACT FROM AUTHOR]

This study introduces and verifies a relative navigation technique for two CubeSats in formation flying as part of the CANYVAL‐C mission. Because the mission requires precision and robustness subject to restricted computational complexity, the technique employs an Extended Kalman Filter (EKF) and raw GNSS data to achieve relative navigation. The relative navigation technique is composed of two parts: parameter‐tuning using an Adaptive Kalman Filter (AKF) in a ground station, and the application of the adaptively determined parameters to allow onboard EKF. Based on Hardware‐in‐the‐Loop Simulations (HILS), the relative positioning error of the EKF with adaptively determined parameters ranged from 10 to 20 cm on each axis (3σ) and satisfied the mission requirement of 1 m. The simulations confirm that the technique yields reliable relative navigation and a realistic error covariance without imposing a computational burden on the CubeSat, as well as reduces the time required for parameter tuning. [ABSTRACT FROM AUTHOR]

A comparative analysis of geodetic versus seismic moment-rate estimations makes it possible to distinguish between seismic and aseismic deformation, define the style of deformation, and also to reveal potential seismic gaps. This analysis has been performed for Egypt where the present-day tectonics and seismicity result from the long-lasting interaction between the Nubian, Eurasian, and Arabian plates. The data used comprises all available geological and tectonic information, an updated Poissonian earthquake catalog (2200 B.C.–2020 A.D.) including historical and instrumental datasets, a focal-mechanism solutions catalog (1951–2019), and crustal geodetic strains from Global Navigation Satellite System (GNSS) data. The studied region was divided into ten (EG-01 to EG-10) crustal seismic sources based mainly on seismicity, focal mechanisms, and geodetic strain characteristics. The delimited seismic sources cover the Gulf of Aqaba–Dead Sea Transform Fault system, the Gulf of Suez–Red Sea Rift, besides some potential seismic active regions along the Nile River and its delta. For each seismic source, the estimation of seismic and geodetic moment-rates has been performed. Although the obtained results cannot be considered to be definitive, among the delimited sources, four of them (EG-05, EG-06, EG-08, and EG-10) are characterized by low seismic-geodetic moment-rate ratios (<20%), reflecting a prevailing aseismic behavior. Intermediate moment-rate ratios (from 20% to 60%) have been obtained in four additional zones (EG-01, EG-04, EG-07, and EG-09), evidencing how the seismicity accounts for a minor to a moderate fraction of the total deformational budget. In the other two sources (EG-02 and EG-03), high seismic-geodetic moment-rates ratios (>60%) have been observed, reflecting a fully seismic deformation. [ABSTRACT FROM AUTHOR]

Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the “turning”, “working”, and “abnormal working” trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the “turning” and “abnormal working” trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing.

GPS measurements have proved extremely useful in quantifying strain accumulation rate and assessing seismic hazard in a region. Continuous GPS measurements provide estimates of secular motion used to understand the earthquake and other geodynamic processes. GNSS stations extending from the South of India to the Higher Himalayan region have been used to quantify the strain build-up rate in Central India and the Himalayan region to assess the seismic hazard potential in this realm. Velocity solution has been determined after the application of Markov noise estimated from GPS time series data. The recorded GPS data are processed along with the closest International GNSS stations data for estimation of daily basis precise positioning. The baseline method has been used for the estimation of the linear strain rate between the two stations. Whereas the principal strain axes, maximum shear strain, rotation rate, and crustal shortening rate has been calculated through the site velocity using an independent approach; least-square inversion approach-based triangulation method. The strain rate analysis estimated by the triangulation approach exhibits a mean value of extension rate of 26.08 nano-strain/yr towards N131°, the compression rate of -25:38 nano-strain/yr towards N41°, maximum shear strain rate of 51.47 nano-strain/yr, dilation of -37:57 nano-strain/yr and rotation rate of 0.7°/Ma towards anti-clockwise. The computed strain rate from the Baseline method and the Triangulation method reports an extensive compression rate that gradually increases from the Indo-Gangetic Plain in South to Higher Himalaya in North. The slip deficit rate between India and Eurasia Plate in Kumaun Garhwal Himalaya has been computed as 18 ± 1:5 mm/yr based on elastic dislocation theory. Thus, in this study, present-day surface deformation rate and interseismic strain accumulation rate in the Himalayan region and the Central Indian region have been estimated for seismic hazard analysis using continuous GPS measurements. [ABSTRACT FROM AUTHOR]

In construction sites, embankment slope failure has led to catastrophic events, necessitating advanced monitoring for disaster prevention. This study presents a novel Slope Displacement Inspection and Management Algorithm (SDIMA) that utilizes Global Navigation Satellite System (GNSS) data to detect significant slope displacements automatically. SDIMA analyzes GNSS data to discern displacements in various directions (North-South, East-West, Vertical) and separate actual change points from measurement errors by employing a robust mathematical model called Displacement Detector. It successfully detected change points with displacements greater than two times the samples' standard deviation, and thresholds were set for displacements ≥ 25 mm. A comparative analysis showed promising efficiency in North-South detection, while certain limitations were identified. The proposed SDIMA outperforms existing models, achieving a Mean Absolute Error (MAE) of 3.157 mm, Root Mean Square Error (RMSE) of 4.754 mm, Mean Absolute Scaled Error (MASE) of 0.294, and an average processing time of 0.578 min, making it a highly efficient and accurate expert system. The innovation of SDIMA lies in its ability to provide an automated decision support tool for site managers, laying the foundation for a low-cost anomaly detection system for slope failure disaster prevention. The algorithm's proficiency and its potential to integrate with existing practices herald a significant advancement in construction management and safety. [ABSTRACT FROM AUTHOR]

The article considers the modern deformation of Earth’s surface according to the results of the GNSS data analysis center of the MAO NAS of Ukraine in comparison with the latest lineament zones and faults, as well as, the Late Pliocene-Quaternary vertical movements of Earth’s crust. The areas of prevailing values of the compressiontension and the leftright rotation of Earth’s surface, the boundaries between which can be drawn in accordance with the latest lineament zones and faults, are identified. The areas of the prevailing stretching of Earth’s surface correspond to the zones of newest tectonic uplifts and the maximum amplitudes of the Pliocene-Quaternary movements and to the areas of the prevailing compression to the tectonic descents and minimum amplitudes. Four large modern geoblocks have been identified: the North-West and the North-East, which rotate clockwise, and the South-West and the South-East — counterclockwise. A qualitative mechanism for interconnecting the modern and newest movements of Earth’s crust is proposed.

Offshore data in the western Ionian Sea indicate that the NW–SE-trending dextral shear zone of the Alfeo-Etna Fault System turns to the N–S direction near the Ionian coastline, where the extensional Timpe Fault System is located. Morpho-structural data show that NW–SE-trending right-lateral strike-slip faults connect the Timpe Fault System with the upper slope of the volcano, where the eruptive activity mainly occurs along the N–S to NE–SW-trending fissures. Fault systems are related to the ~E–W-trending extension and they are seismically active having given rise to shallow and low-moderate magnitude earthquakes in the last 150 years. As a whole, morpho-structural, geodetic and seismological data, seismic profiles and bathymetric maps suggest that similar geometric and kinematic features characterize the shear zone both on the eastern flank of the volcano and in the Ionian offshore. The Alfeo-Etna Fault System probably represents a major kinematic boundary in the western Ionian Sea associated with the Africa–Europe plate convergence since it accommodates, by right-lateral kinematics, the differential motion of adjacent western Ionian compartments. Along this major tectonic alignment, crustal structures such as releasing bends, pull-apart basins and extensional horsetails occur both offshore and on-land, where they probably represent the pathway for magma uprising from depth.

Landslide processes are considered to be the major part of the natural hazards occurring on the northern part of the Bulgarian sea side. Their monitoring can be done with high precision using GNSS data. The objective of this study is to provide solid grounds for monitoring of the landslide processes using GNSS and SAR data. This goal will be achieved by implementation the following: 1) establishment a verified methodology for extracting high-quality information from SAR images aimed at continuous monitoring of landslide areas integrating interferometric images (IFI) and GNSS data and 2) creation of working prototype of an information system for monitoring and prevention of the effects of earth crust movements (landslides, falls, etc.) based of freely accessible data provided by ESA and national sources. One of the scientific tasks to be solved includes the development of methodological approaches for comparison of the results from combined processing of interferometric images from SAR, measurements at permanent GNSS stations of the national NIGGG network in the area of study and geodetic measurements of a newly established test network covering a specific area on the Northern Black Sea coast of Bulgaria with active landslide processes. [ABSTRACT FROM AUTHOR]

This study investigates the ionospheric total electron content (TEC) variations prior to the earthquake (MW = 6.9) of 24 May 2014 in Samothraki island of north Aegean Sea in Greece. TEC estimates were analyzed using data from GNSS (GPS+Glonass) permanent networks with the aim to detect possible ionospheric anomalies associate with the seismic event. The test period covers one week of data, 4 days before and two days after the event. Selected GNSS stations are scattered around seismic epicenter of distances from 16 up to 1375 km. TEC values estimated for every hour using PPP technique with Bernese GPS software. A comparison with global TEC estimates derived from CODE and JPL institute confirms the validation of results. It is found that a significant decrease 1-day prior to earthquake occurs at all of the selected stations. This result is not obvious when standard ionospheric model is performed for the estimation of TEC. Therefore, in such cases the use of dedicated GNSS processing data scenario is mandatory. A spatial analysis on TEC estimates with geometrical properties shows that the 1-day decrement is related with the EQ shock and may point the location area of the Earthquake. Finally, we conclude that the lithosphere-atmosphere-ionosphere coupling (LAIC) mechanism through acoustic or gravity waves has a key role for this phenomenology.

The transmission and storage of global navigation satellite system (GNSS) data places very high demands on mobile networks and centralised data processing systems. GNSS applications including community based navigation and fleet management require GNSS data to be transmitted from a vehicle to a centralised system and then processed by a map-matching algorithm to determine the location of a vehicle within a road segment. Various data compression techniques have been developed to reduce the volume of data transmitted. There is also an independent literature relating to map-matching algorithms. However, no previous research has integrated data compression with a map-matching algorithm that accepts compressed data as an input without the need for decompression. This paper develops a novel GNSS data reduction algorithm with deterministic error bounds, which was seamless integrated with a specifically designed map-matching algorithm. The approach significantly reduces the volume of GNSS data communicated and improves the performance of the map-matching algorithm. The data compression extracts critical points in the trajectory and velocity–time curve of a vehicle. During the process of selecting critical points, the error of restoring vehicle trajectories and velocity–time curves are used as parameters to control the number of critical points selected. By setting different error bound values prior to the execution of the algorithm, the accuracy and volume of reduced data is controlled precisely. The compressed GNSS data, particularly the critical points selected from the vehicle's trajectory is directly input to the map-matching algorithm without the need for decompression. An experiment indicated that the data reduction algorithm is very effective in reducing data volume. This research will be useful in many fields including community driven navigation and fleet management. [ABSTRACT FROM AUTHOR]

Rapid finite fault source determination is critical for reliable and robust tsunami early warnings. Near-field Global Navigation Satellite System (GNSS) observations have shown value to constrain the source inversion, but real-time GNSS stations are sparse along most of the active faults. Here we propose an automatic earthquake finite source inversion (AutoQuake Inversion) algorithm jointly using near-field (epicentral distance < 1000 km) GNSS data and mid-range (epicentral distance from 30° to 45°) teleseismic P displacement waveforms. Neither the near-field GNSS nor the mid-range teleseismic data clip or saturate during large earthquakes, while the fast-traveling P-waves are still essential to constrain the source in regions where very few or no GNSS stations are available. Real-time determination of the fault geometry remains to be the main challenge for rapid finite source inversion. We adopt a strategy to use the pre-defined geometry Slab2 for earthquakes within it or to forecast a focal mechanism based on near-by historical events for earthquakes without Slab2 prior. The algorithm has been implemented successfully in the prototype of JPL's GPS-Aided Tsunami Early-Detection system and tested for many real events recently. This article provides the framework of the algorithm, documents the retrospective and real-time results, and discusses remaining challenges for future improvements. [ABSTRACT FROM AUTHOR]

A comparative analysis of geodetic versus seismic moment-rate estimations makes it possible to distinguish between seismic and aseismic deformation, define the style of deformation, and also to reveal potential seismic gaps. This analysis has been performed for Egypt where the present-day tectonics and seismicity result from the long-lasting interaction between the Nubian, Eurasian, and Arabian plates. The data used comprises all available geological and tectonic information, an updated Poissonian earthquake catalog (2200 B.C.–2020 A.D.) including historical and instrumental datasets, a focal-mechanism solutions catalog (1951–2019), and crustal geodetic strains from Global Navigation Satellite System (GNSS) data. The studied region was divided into ten (EG-01 to EG-10) crustal seismic sources based mainly on seismicity, focal mechanisms, and geodetic strain characteristics. The delimited seismic sources cover the Gulf of Aqaba–Dead Sea Transform Fault system, the Gulf of Suez–Red Sea Rift, besides some potential seismic active regions along the Nile River and its delta. For each seismic source, the estimation of seismic and geodetic moment-rates has been performed. Although the obtained results cannot be considered to be definitive, among the delimited sources, four of them (EG-05, EG-06, EG-08, and EG-10) are characterized by low seismic-geodetic moment-rate ratios (60%) have been observed, reflecting a fully seismic deformation.

This study investigates the ionospheric total electron content (TEC) variations prior to the earthquake (MW = 6.9) of 24 May 2014 in Samothraki island of north Aegean Sea in Greece. TEC estimates were analyzed using data from GNSS (GPS+Glonass) permanent networks with the aim to detect possible ionospheric anomalies associate with the seismic event. The test period covers one week of data, 4 days before and two days after the event. Selected GNSS stations are scattered around seismic epicenter of distances from 16 up to 1375 km. TEC values estimated for every hour using PPP technique with Bernese GPS software. A comparison with global TEC estimates derived from CODE and JPL institute confirms the validation of results. It is found that a significant decrease 1-day prior to earthquake occurs at all of the selected stations. This result is not obvious when standard ionospheric model is performed for the estimation of TEC. Therefore, in such cases the use of dedicated GNSS processing data scenario is mandatory. A spatial analysis on TEC estimates with geometrical properties shows that the 1-day decrement is related with the EQ shock and may point the location area of the Earthquake. Finally, we conclude that the lithosphere-atmosphere-ionosphere coupling (LAIC) mechanism through acoustic or gravity waves has a key role for this phenomenology. [ABSTRACT FROM AUTHOR]

The main characteristics of an active landslide located before the Thracian Cliffs golf club, SW of the village of Bozhurets (Northern Bulgarian Black Sea Coast) are considered. The results of the field studies for the landslide activity for the period 2018-2020 are presented. Physical and mechanical parameters of samples from the landslide toe are determined. Extensive research has been carried out on the latest landslide activations. As part of the study, a control geodynamic network covering the landslide area was established. [ABSTRACT FROM AUTHOR]

In southwest Japan, great earthquakes have occurred on the plate interface along the Nankai trough with a recurrence time of approximately 100 years. Most previous studies estimated slip deficits on the seismogenic zone from interseismic Global Navigation Satellite System (GNSS) velocity data assuming slip‐response functions for an elastic medium. The observed surface velocities, however, include effects of viscoelastic relaxation in the asthenosphere caused by slip motion associated with seismic cycles. If the elastic responses cannot adequately approximate the deformation of the viscoelastic medium, the results of an inversion analysis could be biased. If the recurrence interval is greater than the effective relaxation time in the elastic‐viscoelastic layered structure, we were able to formulate an inverse problem for the estimation of slip‐deficit rates from GNSS velocity data with completely relaxed slip‐response functions for a later stage of the seismic cycle. Analyzing surface velocity data from GNSS daily coordinate data between March 2005 and February 2011 together with seafloor geodetic data, we estimated the slip‐deficit rate distribution by the strain data inversion method. There were significant differences between the results using elastic and completely relaxed responses. Although the result with elastic responses shows a high coupling zone in the coastal region, it was located trenchward when completely relaxed responses were used. We found that the peak slip‐deficit rate increases as the thickness of the lithosphere becomes thinner. Moreover, we succeeded in appropriately separating elastic‐viscoelastic deformation due to plate coupling from rigid block motion. Key Points: Slip‐deficit rates along the Nankai trough were estimated from GNSS data and seafloor geodetic data, using elastic‐viscoelastic structuresWhen viscoelastic relaxation is included, a high slip‐deficit rate zone was located more trenchward than those in pure elastic responseOur strain data inversion succeeded in decomposing observed displacement into the contributions from plate coupling and rigid block motions [ABSTRACT FROM AUTHOR]