Volume 12, Issue 1

Elevation differences have been determined through conventional ground surveying techniques for over a century. Since the mid-80s GPS, GLONASS and other satellite systems have modernized the means by which elevation differences are observed. In this article, we assessed the quality of GEIOD12B through long-occupation GNSS static surveys. A set of NGS benchmarks was occupied for at least one hour using dual-frequency GNSS receivers. Collected measurements were processed using a single CORS station at most 24 kilometers from the benchmarks. Geoid undulation values were driven by subtracting measured ellipsoidal heights from the orthometric heights posted on the NGS website. To assess the quality of GEOID12B, we compared our computed vertical shifts at the benchmarks with those estimated from GEOID12B published by NGS. In addition, Kriging model was used to interpolate local maps for the geoid undulations from the benchmark heights. The maps were compared with corresponding parts of GEOID12B. No biases were detected in the results and only shifts due to random errors were found. Discrepancies in the range of ten centimetres were noticed between our geoid undulation and the values available from NGS.

The gravity satellite mission GOCE made its final observations in the fall of 2013. Since the re-entry to the Earth’s atmosphere, the full cycle of the GOCE data has been published by ESA. At first, we evaluated all the GOCE-based global geoid models over Finland using terrestrial gravity and GNSS-levelling data. The most suitable model was selected as a global background model for the Finnish quasi-geoid calculations. Next, we combined the chosen model with terrestrial gravity data of Finland and surrounding areas. Quasi-geoid models with different modifications were calculated using the GOCE DIR5 model up to spherical harmonic degree and order (d/o) 240 and 300, and the high resolution EIGEN-6C4 (includes the complete GOCE data) model up to degree and order 1000 and 2190. The calculated quasi-geoid models were validated to the measurements on site with two independent GPS-levelling datasets. The best quasi-geoid models with GOCE gave standard deviations of 2.6 cm (FIN_DIR5 d/o 240) and 2.3 cm (FIN_DIR5 d/o 300) in Finland. For the high resolution model FIN_EIGEN-6C4, the results were 1.8 cm (d/o 1000) and 1.7 cm (d/o 2190). In addition, the results were compared with the latest geoid models available in Finland (FIN2005N00, NKG2004, NKG2015, EGG2008). The sub-2-centimetre (and near 2 cm, when using the GOCE-based models) accuracy is an improvement over the previous and current Finnish geoid models.

Owing to the appearance of many global geopotential models, it is necessary to determine the most appropriate model for use in Egyptian territory. In this study, we aim to investigate three global models, namely EGM2008, EIGEN-6c4, and GECO. We use five mathematical transformation techniques, i.e., polynomial expression, exponential regression, least-squares collocation, multilayer feed forward neural network, and radial basis neural networks to make the conversion from regional geometrical geoid to global geoid models and vice versa. From a statistical comparison study based on quality indexes between previous transformation techniques, we confirm that the multilayer feed forward neural network with two neurons is the most accurate of the examined transformation technique, and based on the mean tide condition, EGM2008 represents the most suitable global geopotential model for use in Egyptian territory to date. The final product gained from this study was the corrector surface that was used to facilitate the transformation process between regional geometrical geoid model and the global geoid model.

The common approach for code-based point positioning using GNSS involves linearizing the observation equations about an estimated position and solving the equations iteratively in a least squares fashion. The solution provides estimates for the receiver coordinates and clock error. In this paper, a method based on distance geometry and Kalman filtering is presented. Distance geometry is used to provide a closed form solution for the receiver clock bias which is then used to correct the pseudorange observations before proceeding to locate the receiver coordinates. This two step method guarantees a solution for when a minimum of four satellites are available and facilitates direct utilization of a simple Kalman filter without any need for linearization. Results indicate that the method presented can provide improved estimates under poor satellite coverage as compared to the conventional iterative methods while performing similar to the conventional methods when there is good coverage.

Besides the geodetic dual-frequency GNSS receivers-systems (receiver and antenna), there are also low-cost single-frequency GPS receiver-systems. The multipath effect is a limiting factor of accuracy for both geodetic dual-frequency and low-cost single-frequency GPS receivers. And the multipath effect is for the short baselines dominating error (typical for the monitoring in Engineering Geodesy). So accuracy and reliability of GPS measurement for monitoring can be improved by reducing the multipath signal. In this paper, the self-constructed L1-optimized choke ring ground plane (CR-GP) is applied to reduce the multipath signal. Its design will be described and its performance will be investigated. The results show that the introduced low-cost single-frequency GPS receiver-system, which contains the Ublox LEA-6T single-frequency GPS receiver and Trimble Bullet III antenna with a self-constructed L1-optimized CR-GP, can reach standard deviations of 3 mm in east, 5 mm in north and 9 mm in height in the test field which has many reflectors. This accuracy is comparable with the geodetic dual-frequency GNSS receiver-system. The improvement of the standard deviation of the measurement using the CR-GP is about 50 % and 35 % compared to the used antenna without shielding and with flat ground plane respectively.

In this study, we propose a regional ionospheric model (RIM) based on both of the GPS-only and the combined GPS/BeiDou observations for single-frequency precise point positioning (SF-PPP) users in Europe. GPS/BeiDou observations from 16 reference stations are processed in the zero-difference mode. A least-squares algorithm is developed to determine the vertical total electron content (VTEC) bi-linear function parameters for a 15-minute time interval. The Kriging interpolation method is used to estimate the VTEC values at a 1 ° × 1 ° grid. The resulting RIMs are validated for PPP applications using GNSS observations from another set of stations. The SF-PPP accuracy and convergence time obtained through the proposed RIMs are computed and compared with those obtained through the international GNSS service global ionospheric maps (IGS-GIM). The results show that the RIMs speed up the convergence time and enhance the overall positioning accuracy in comparison with the IGS-GIM model, particularly the combined GPS/BeiDou-based model.

In geodetic deformation analysis observations are used to identify form and size changes of a geodetic network, representing objects on the earth’s surface. The network points are monitored, often continuously, because of suspected deformations. A deformation may affect many points during many epochs. The problem is that the best description of the deformation is, in general, unknown. To find it, different hypothesised deformation models have to be tested systematically for agreement with the observations. The tests have to be capable of stating with a certain probability the size of detectable deformations, and to be datum invariant. A statistical criterion is needed to find the best deformation model. Existing methods do not fulfil these requirements. Here we propose a method that formulates the different hypotheses as sets of constraints on the parameters of a least-squares adjustment model. The constraints can relate to subsets of epochs and to subsets of points, thus combining time series analysis and congruence model analysis. The constraints are formulated as nonstochastic observations in an adjustment model of observation equations. This gives an easy way to test the constraints and to get a quality description. The proposed method aims at providing a good discriminating method to find the best description of a deformation. The method is expected to improve the quality of geodetic deformation analysis. We demonstrate the method with an elaborate example.

Gyro theodolites – briefly gyros – allow the orientation transfer from a reference line to another line without need for a connecting geodetic network. They are routinely used for orientation control of networks in tunnelling projects. This is currently the only way to stabilize accuracy and reliability in tunnelling networks and is indispensable with respect of today’s requirements for excavation and breakthrough accuracy. In order to archive a reliable assessment of correctness, the measuring method has to be planned in a way that systematic influences in the determination of an underground directional angle are minimized. For this purpose, the principle of differential measurements is used for an azimuth determination both in terms of time as well as in space. All required corrections and reductions must be considered correctly to obtain the directional angle of interest with a measurement uncertainty less than 1 mgon (=∧3.3″$\stackrel{\wedge }{=}3.{3^{\prime\prime }}$). Some accuracy considerations obtained from own experiences are discussed to state whether the specified standard deviations of the used gyros according to DIN 18723-7 can be confirmed.

Point-cloud coordinate information derived from terrestrial Light Detection And Ranging (LiDAR) is important for several applications in surveying and civil engineering. Plane fitting and segmentation of target-surfaces is an important step in several applications such as in the monitoring of structures. Reliable parametric modeling and segmentation relies on the underlying quality of the point-cloud. Therefore, understanding how point-cloud errors affect fitting of planes and segmentation is important. Point-cloud intensity, which accompanies the point-cloud data, often goes hand-in-hand with point-cloud noise. This study uses industrial particle boards painted with eight different colors (black, white, grey, red, green, blue, brown, and yellow) and two different sheens (flat and semi-gloss) to explore how noise and plane residuals vary with scanning geometry (i.e., distance and incidence angle) and target-color. Results show that darker colors, such as black and brown, can produce point clouds that are several times noisier than bright targets, such as white. In addition, semi-gloss targets manage to reduce noise in dark targets by about 2–3 times. The study of plane residuals with scanning geometry reveals that, in many of the cases tested, residuals decrease with increasing incidence angles, which can assist in understanding the distribution of plane residuals in a dataset. Finally, a scheme is developed to derive survey guidelines based on the data collected in this experiment. Three examples demonstrate that users should consider instrument specification, required precision of plane residuals, required point-spacing, target-color, and target-sheen, when selecting scanning locations. Outcomes of this study can aid users to select appropriate instrumentation and improve planning of terrestrial LiDAR data-acquisition.