Nowadays, many people and organizations depend on Global Navigation Satellite Systems (GNSS. Monitoring of GNSS is important to ensure the quality of GNSS measurements and products. The availability of the signals-in-space (SiS) is an essential part of the monitoring of GNSS, but it is not clear how availability is defined and standards for monitoring are lacking. The main research question for this thesis is therefore: Which are the key performance indicators related to availability that unambiguously describe sensor station and system performance in time, how can these be computed in an operational manner, and how can they be presented in a condensed form to the stakeholders? This includes an objective of defining unambiguous performance parameters for sensor station and system, and address the considerations related to the definition. A prototype software tool is created to study the algorithms and compute the key performance indicators. Availability is in the basis a binary operation: a signal is available or unavailable. When this is applied on daily measurements, daily statistics can be computed. A signal is considered available if the code, carrier phase and C/N0 measurements are present, and meet certain standards. A signal is said to be expected if the satellite is expected to transmit that signal, the receiver is configured to receive that signal, and the signal is not blocked by objects in the signal’s path to the sensor station. For this it’s needed to define and compute an elevation mask for each station. The sensor station and system performance parameters are computed from a network of sensor stations, using files in the Receiver Independent Exchange format (RINEX). Four key performance indicators are defined for the sensor station performance. The Daily Station Availability describes the part of the day that the station is operational, the Daily Station Total Availability gives how well the station receives, and the Effective Mean Elevation quantifies the elevation mask and thus the location of the sensor station. These three parameters are summarized into the Overall Station Quality parameter, which gives and overall performance class to the sensor station. For the system performance a satellite is considered available if all signals are received by a sensor station of the monitoring network and the health status is healthy. The satellite is considered unavailable if the signals are received by none of the monitoring stations, while expected by at least two stations, or the health status is unhealthy. The Daily GNSS Availability parameter gives the percentage of the day that the satellite was available and the Daily Available number of Satellites tells how many satellites were available during the day.  The parameters are computed for a period of 100 days. Results are presented using color codes and by showing only detailed information in case of anomalies or specific investigations. The proposed key performance indicators showed to be very useful at pointing out good performances or anomalies. While SiS availability gives much insight in the performance, a monitoring tool can be improved when combined with other performance aspects.  

Geodesists use multiple methods to monitor surface deformation. This gives the opportunity to integrate complementary data for better interpretation of deformation processes. The integration of data has to deal with: (1) spatio-temporal differences in sampling methodologies, meaning that observed objects are not the same and thus observed processes may stem from different sources; and (2) physical differences in the methodologies leading to different kinds of coordinate reference systems with possibly different datums. This makes integration of data challenging.In this thesis, we address one of the fundamental roots to this integration problem, by developing co-located reference points for multiple geodetic monitoring methods. This will ensure that, for multiple geodetic monitoring methods, one common deformation process is observed. This eliminates interpolation between observations. I show the main requirements for an Integrating Geodetic Reference Station (IGRS), present a design for a fully functional IGRS and describe its performance. Furthermore, the protocols for deployment and operational use are given. Benchmarks for InSAR, GNSS, levelling, LiDAR, and gravimetry are present on the IGRS. From field tests, it is found that presented radar reflectors have a 1-sigma measurement precision of <0.5 mm in the Line-of-Sight of the radar. This includes both the structural stability as the influence due to clutter. Furthermore, it is shown that the GNSS antenna has mm precise performance, similar to standard monitoring GNSS set-ups.The developed IGRS can be deployed to create a local datum connection between datasets. Ideally IGRSs, either the design presented in this thesis or similar, are deployed where integration, validation and calibration of data, especially InSAR data, is needed.

The purpose of this thesis is to do quality assessment of GNSS/IMU derived NAP heights for The Netherlands (NAP) using Fugro RILA technique and the RDNAPTRANS2018 published by Rijkswaterstaat. The use of Global Navigation Satellite System (GNSS) is growing rapidly in order to determine the position (both horizontal and vertical). However, the GNSS is only able to give the geometric height which is the position on the ellipsoid, which have a drawback that the surface of constant ellipsoidal height are not equipotential surface, and hence these heights need to be transformed into traditional height systems such as the Normaal Amsterdam Peil (NAP) used in The Netherlands. In order to derive these NAP heights from the GNSS heights, a (quasi-) geoid model along with corrector surface model is used to convert from one height to another. In this study the newly computed local quasi geoid model NLGEO2018 rather than the NLGEO2008 is adapted using RDNAPTRANS2018 along with Fugro Rail Infrastructure aLignment Acquisition (RILA) technique which makes use of GNSS/IMU to obtain ellipsoidal height. It was found upon using the older transformation procedure RDNAPTRANS2008, which makes use of NLGEO2008, a mismatch in the order of 17mm between the NAP and GNSS-derived NAP from RILA. This error is not only due to the geoid itself but also the systematic errors in the different height system. However, this new geoid NLGEO2018 along with the new transformation procedure of RDNAPTRANS2018 allows a more accurate conversion of ellipsoidal to normal heights and this study focuses on this quality assessment of the new GNSS/IMU derived NAP heights and investigate how well NAP heights be obtained using RILA technique based on the new geoid. It was found with the use of the new geoid and the new transformation model RDNAPTRANS2018, the mean height difference was reduced from 12mm to 3.6mm showing a better fit of GNSS heights to NAP heights. An error budget was also calculated, to understand the reliability of these RILA measurements with the NAP heights. This error budget includes all the uncertainties from all error sources, namely RILA derived height, geoid height and the levelled height, followed by hypothesis testing. The highest uncertainty was from GNSS/IMU measurements from RILA system followed by the levelling and then the gravimetric quasi-geoid. Finally from this analysis, areas where the terrestrial surveying can be avoided was found. It was found that near the stations, tunnels, high vegetation had the highest uncertainty due to poor GNSS reception. It was also found that level crossing also showed a constant systematic offset between the two heights potentially due to the materials of the level crossings.

The introduction of artificial reflectors in the areas to be imaged by a radar sensor facilitates interferometric analysis over regions of weak coherence between acquisitions. Traditionally, corner reflectors have been utilized; whose strong and stable scattering characteristics make them suitable for these purposes. Such reflectors also provide flexibility in exerting control over the network of points to be analyzed. However, the use of these reflectors is beset with the following challenges: settlement under self-weight, inefficient drainage retaining excess rain/snow, one setup per track etc. Based on the experiment carried out with several reflector devices at Wassenaar, The Netherlands, here we show that specific next-generation artificial reflectors can address some of the challenges posed by conventional corner reflectors while allowing the integration of measurements from several deformation monitoring techniques. As part of the experiment, a deliberate 7±0.05 mm vertical movement is imparted to radar transponder MUTE-1 which is estimated from Sentinel-1 radar images within 0.01 to 0.69 mm deviation while the line of sight motion and radar cross section change realized as a result of imparting deliberate tilt to DBFm device is estimated within 0.47 mm and 1.7 dBm2 respectively. The displacement results from SAR images for MUTE radar transponders are validated using a ground survey campaign. Displacement results in the horizontal direction (East-West projection) suggests that the location WASS01 housing devices MUTE-1 and DBFT tilts in the western directions (estimates of maximum tilt vary from 0.9 to 1.95 mm) while WASS02 location on which MUTE-2 is installed exhibits an eastward tilt (about 2 mm in magnitude). These tilt motions result from the susceptibility of concrete foundation design to swelling and shrinkage of soil. By analyzing meteorological data in conjunction with SAR results from installed devices, we find that the impact of heavy rain remains limited to the conventional reflector type, suggesting that the downward-pointing design of DBF CRs drains the water preventing its accumulation in the apex. Comparative stability analysis demonstrates that next-generation reflector devices (MUTE-2, DBFT and DBFX) can be utilized to determine motions in the vertical and horizontal direction and detect changes in orientation of the device over a vegetation area with low backscattering characteristics with sub-millimeter precision. We anticipate this study to be a small step towards a more sophisticated ground segment for SAR satellites consisting of reflectors that can adequately provide knowledge about deformation characteristics of the area under investigation.

The TU Delft has acquired a UAV LiDAR system and can be used to acquire an 3D point cloud of the terrain below the system. It is of interest how such a flight mission would be best performed and what the corresponding quality would be. Current free software provided limited data quality estimation and mission planning support for the DJI Matrice 300 RTK and Yellowscan Mapper+ UAV LiDAR system. For these reasons an open source flight planner tool has been created. This tool does require the DJI Pilot 2 application and can for this reason only be used with DJI UAVs. It is optimized for the Yellowscan Mapper+ LiDAR module. To analyze the quality of the data, two types of point cloud quality methods have been performed: comparison of targets in the point cloud to GNSS measurements, referred to the target analysis, and based on comparisons to itself for different acquisition times on the same location, referred to as the overlap analysis. For the target analysis an automatic, LiDAR intensity based method was developed, for determining target coordinates. Furthermore, target coordinates where detected manually based on image projected RGB data in the point cloud. By comparing these target coordinates to the reference GNSS target measurements, the combined GNSS and point cloud error can be estimated separate from the target fitting errors. It was found that the combined point cloud and GNSS error is likely larger than the fitting errors in up direction up to 70m flying height. This might allow for study of the point cloud error in up direction, with this method. The presented overlap method can be used when no other reference data is available. This method divides the data in horizontal grid cells. The data in each grid cell is divided in time groups. For each time group a PCA plane is fitted and used to estimate the height in the horizontal center of the grid cell. By comparing heights between different time groups in the same grid cell, the height precision can be studied. With this method two types of overlaps are found. Within flight strips and between flight strips. The overlaps within flight strips seem to have a strong relation with the considered time difference length. This is likely caused by a combination of IMU and scan geometry errors. The overlaps between flight strips do not seem to have such a relation. This is likely caused by a combination of strip adjustment errors and possible GNSS errors. The found estimated standard deviations, up to a flying height of 70m, are generally below 17mm. It was found that flights above 70m seemed to perform significantly worse. Furthermore, grass resulted in larger estimated standard deviations than expected for low flights. This is likely caused by the ability of the scanner to measure the 3D shape of the grass leaves for lower flying heights and not for larger flying heights.