Ground deformation caused by natural hazards or construction activities can pose risks to people, infrastructure and the environment. Interferometric Synthetic Aperture Radar (InSAR), a satellite-based remote sensing technique, offers promising capabilities for monitoring surface deformation with high spatial and temporal resolution. Despite its technical potential, the use of InSAR in civil and geo-engineering projects and for natural hazard assessment remains limited. This study investigates both the technical and non-technical factors influencing the integration of InSAR in practice. Through a comprehensive literature review and interviews with InSAR users and providers, this research identifies key benefits and obstacles for implementation and provides recommendations to improve the uptake of InSAR in civil engineering, geo-engineering and natural hazards projects. The findings highlight the need for interdisciplinary collaboration, clear communication, training and the development of application-specific tools and guidelines to facilitate wider adoption of InSAR in engineering practice.
Next, an operational framework is developed for using InSAR in accordance with geotechnical design standards, such as Eurocode 7, throughout all phases of infrastructure projects. The proposed framework is a practical tool that can be used by planners and engineers in the whole lifecycle of an infrastructure project. Finally, the study examines two practical InSAR applications. First, the use of InSAR in a shield tunnelling project in an urban environment is investigated. The applicability of InSAR prior, during, and after tunnel construction is evaluated. Special emphasis is placed on the influence of the InSAR phase ambiguities in relation to short-term settlements that may occur during tunnel construction. Second, the use of a persistent scatterer interferometry (PSI) map for alpine permafrost monitoring is examined. The results demonstrate that mean surface velocities are higher in permafrost zones than in zones without permafrost. However, it is not possible to monitor structures and infrastructure in permafrost areas using C-band data.
In summary, the study explores the challenges and potential of using Interferometric
Synthetic Aperture Radar (InSAR) for monitoring ground deformations in civil
engineering, geo-engineering, and natural hazard projects. Technical and nontechnical
obstacles are discussed and recommendations are provided for improving
InSAR’s adoption in practice.

Spaceborne sensors, particularly Synthetic Aperture Radar (SAR), provide valuable tools for monitoring agricultural resources, improving yield predictions, and ensuring sustainable farming practices. In this research, we explore several venues to advance the use of SAR observation time series for agricultural and vegetation monitoring applications.
The first part of this research evaluates the added value of Sentinel-1 InSAR coherence time series for land cover classification, using an agricultural region in São Paulo, Brazil, as a case study. This region is characterized by a mixture of crops, pastures, and sugarcane plantations, all managed asynchronously. The findings demonstrate that incorporating InSAR coherence alongside SAR backscatter improves classification accuracy, particularly during the dry season when the distinctions between vegetation and bare soil are more pronounced. The research employed machine learning approaches to analyze pixel-level and field-level classifications using different sampling schemes. It highlights how multi-looking strategies can be adjusted to improve the accuracy of the classification outcomes in agricultural settings. This research highlights the usefulness of coherence data for the detection of events such as harvesting, offering valuable insights for more dynamic agricultural monitoring. The sensitivity of the coherence to agricultural changes leads to the observed improvement in Land Use Land Cover (LULC) mapping.
Forward models, or observation operators, are essential for the interpretation of radar observations and for the development of assimilation frameworks. In particular, in this research, we are interested in forward modeling the relation between crop bio-geophysical parameters, such as Leaf Area Index (LAI), Above Ground Biomass (AGB), and soil moisture, the inputs to our data-driven model, and radar observables, the outputs.
In the second part of this research, we integrate an existing crop growth model, the Decision Support System for Agrotechnology Transfer (DSSAT), with machine learning techniques to train a forward model to predict SAR observables over silage maize fields in The Netherlands across multiple years. Using crop growth models circumvents the dependency on limitedly available field measurements. When we use training and validation data from the same growth season, we obtain accurate predictions, with a mean absolute error (MAE) of less than 1.23 dB. Some of the field-to-field variability is accounted for by including the mean backscatter intensity during a few acquisitions before crop emergence. The obtained performance suggests the potential of using this approach to generate observation operators for data assimilation frameworks or for anomaly detection, supporting large-scale agricultural monitoring. However, the results also highlight one of the main challenges: the resulting data-driven model fails to generalize when presented with input bio-geophysical parameters that fall outside the regions of the parameter space spanned by the training dataset, as can happen, for example, during a drought period.
In the final part, we develop a physics-guided machine learning approach to address the limitations of data-driven models: lack of generalizability, tendency to overfitting, and reliance on extensive training data sets. We introduce physical constraints in an artificial neural network (ANN) in two ways. First, by modifying the loss function, used to train the ANN, by including a penalty for unphysical behavior, in particular by penalizing negative values of the partial derivative of the predicted backscatter intensity with respect to the surface soil moisture, since we assume this should always be positive. Second, by mirroring the architecture of the widely used Water Cloud Model (WCM) in the network topology. The added physical term to the loss function improves the ANN performance in all cases considered, with an R2 increase of 3 percentage points (p.p). The WCM-inspired model performs slightly worse when trained and tested with data from the same year, but it generalizes better, producing significantly better results for unseen conditions. In addition, the WCM-inspired model also produces individual contributions to the observed intensity, such as the surface-scattering component and the vegetation backscatter component.

The Groningen gas field has been compacting since the start of gas extraction in the 1960s because of pressure depletion in the reservoir, causing subsidence in the Groningen region. Geodetic techniques, such as optical leveling and satellite-borne Interferometric Synthetic Aperture Radar (InSAR), provide displacement estimates for subsidence monitoring. InSAR displacement estimates provide observation points with a higher spatial density and temporal sampling than leveling. Whereas identifying leveling benchmarks with subsidence caused by the compaction reservoir is possible, the InSAR’s sensitivity to multiple subsidence sources (e.g., compacting reservoir, soil motion, and infrastructure instability) complicates the identification of subsidence-driving mechanisms in InSAR estimates. Combining physics-based subsurface models with InSAR estimates into data assimilation is an approach to estimating to what extent reservoir compaction and other subsurface processes contribute to the total subsidence.....

Over the past three decades, synthetic aperture radar (SAR) interferometry (InSAR) has become one of the most important Earth observation technologies in the world, and its use has become common in applications such as topographic mapping, monitoring earthquakes and volcanoes, as well as the built environment. Despite these advances, many technical and scientific challenges remain unsolved in the field, which prevent its use across diverse regions and biomes. One such type of region are wetlands and peatlands, which are notoriously challenging to monitor remotely due to poor signal quality and rapidly changing conditions between SAR acquisitions. This problem is particularly relevant in the Netherlands, because a significant portion of the country is composed of drained peat and clay soils which lie below sea level. These “soft soils” exhibit highly dynamic temporal behaviour that is closely linked to the phreatic groundwater system. In addition, they also exhibit a slow, irreversible subsidence caused by compaction and oxidation, the latter of which is a greenhouse gas (GHG) emitting process. It is this slow, irreversible subsidence component which scientists, governments, farmers and other stakeholders are trying to better understand, and evaluate the risks it poses. Previous efforts in monitoring the cultivated soft soil regions of the Netherlands by InSAR have been hampered by two main problems, which in this work are referred to as “cycle slips” and “loss-of-lock”. The former refers to consistent errors made in ambiguity resolution due to signals which exhibit such highly dynamic behaviour that standard algorithms cannot correctly interpret the wrapped phase data. The latter term refers to a permanent and irreparable loss of coherence in an interferometric SAR data stack. It is common in peatland regions for coherence levels to rise and fall seasonally, and in general, no coherent interferometric combination exists between the coherent periods. This condition means that the interferometric time series is severed during these incoherent periods, and only intermittent, disconnected temporal subsets of data are useable...

Assessing firn processes withinGreenland and Antarctica is important in recent decades, as melt–refreezing processes can result in accelerated meltwater runoff and land-ice discharge. Meanwhile, surface and depth hoar crystal formation have an impact on the surface warming and surface mass balance (SMB) of the ice sheets. Typically, these processes are monitored using in situ firn core measurements, or estimated using climate models. However, the in situ measurements are sparse due to the harsh conditions of the polar regions, while the climate models are based on simplified assumptions which introduce various uncertainties. The recent advancements of satellite remote sensing techniques provide the opportunity to monitor the firn processes over the ice sheets, due to a vast spatial coverage and a frequent revisit time.

This thesis explores the capability of satellite radiometers, scatterometers and altimeters to assess firn processes, including firn density variation and melt–refreezing processes. Conventionally, satellite radiometers and scatterometers are used in detecting melt events over the ice sheets, based on the principle that melt events change the dielectric constant within the firn layer, while the satellite altimeter is typically used for estimating surface elevation changes over ice sheets. This thesis, on the contrary, explores the feasibility of using radiometers and scatterometers to assess the dry-firn density; meanwhile, it assesses the potential of using the altimeters to observe the melt– refreezing events of firn. The rationale behind this thesis is that the long-term variations in satellite radiometer and scatterometer observations depend on the changing scattering properties due to variations in near-surface firn densities, which provides the opportunity for using satellite radiometer and scatterometer observations to estimate long-term changes in firn densities. Meanwhile, the shape of the waveform obtained by a satellite radar altimeter can be influenced by volume and surface scattering of firn. By assessing the variation of firn scattering properties using the waveform information, the occurrence and impact of melt–refreezing processes within the firn layer can be assessed. Therefore, more potentials lie in the application and interpretation of remote sensing data in the studies of the cryosphere. To study the aforementioned application…

Interferometric Synthetic Aperture Radar (InSAR) is a geodetic technique that is capable of monitoring surface displacements up to millimeter-level of precision. The end products from conventional InSAR processing are application-agnostic, which means that they are not optimized for any particular application. InSAR products could be more beneficial if tailored for a relevant application, particularly if expert users can tune the products according to their monitoring requirement. Here, we develop tools for application-aligned monitoring by means of the selection of application-relevant arcs between scatterers in InSAR. We are interested in the use of local (short) arcs between point scatterers, as these arcs are more likely to be better suited for monitoring localized differential deformation, and may provide observations of better quality due to the fact that they are less prone to atmospheric noise.

We first compare the time series of local arcs and conventional time series w.r.t.\ a common reference point based on their deformation behavior. The comparison reveals that the time series of local arcs are capable of providing additional information on deformation behavior over the conventional method. However, the quality of observations in local arcs in general is found to be more variable, and often even worse than those from the conventional method. Most likely, the reason for this is the absence of noise reduction in local arcs in comparison to the time series from the conventional method which optimizes the selection of the common reference point to reduce noise in the time series.

In addition, to optimize the arc selection for a given application, we propose an arc tuning strategy, where criteria can be set based on arc parameters, i.e., the length, the elevation difference (between point scatterers) and the azimuth of the arc. We also introduce the arc clustering method as an exploratory data analysis algorithm for general-purpose monitoring using local arcs. Both of these methods are demonstrated on test scenarios over the quay walls along the canal network of Amsterdam. The demonstration on arc tuning shows that arc setting criteria on arc geometry parameters are adequate to select arcs with certain orientations, and the selection can be further aided by estimating displacement parameters with multiple hypothesis testing. The results from the arc clustering show the potential of detect instability over a certain area using arcs without knowing the motion of the specific object.

This study contributes to monitoring deformation where the InSAR data can be optimally attuned based on a particular application. In order to convey information on selected arcs effectively, a visualization tool based on an interactive map is created in a jupyter notebook environment.

Interferometric Synthetic Aperture Radar (InSAR) stands as a widely adopted technique for monitoring displacements on the Earth's surface, providing millimeter-level precision. Several InSAR studies show the efficacy in retrospectively identifying hazardous situations, such as the failure of a structure. The next imperative step is to detect and identify anomalous points proactively. This necessitates robust and repeatable displacement estimates to avoid misinterpretation and instill confidence in the results.

Many InSAR studies use a batch estimation process requiring a robust algorithm to obtain reliable results. Here we propose a test recipe and introduce metrics to assess the robustness of the InSAR displacement estimates quantitatively, comparing the batch-estimated results of varying SAR acquisition inputs. Robustness characterizes the stability of displacement estimates in the face of disturbances and uncertainties, demonstrating resilience against changing conditions and input.
Our quantification of robustness involves three core metrics to assess InSAR displacement estimates.

Case studies conducted over the city center of Amsterdam and a coastal region at the North Sea reveal the useful insight provided by robustness testing in identifying ambiguities and fallacies in the applied algorithm. Notably, the main challenges arise from the estimation of atmospheric delay, which emerges as a sensitive step with ample room for enhancement. A robust atmospheric estimation appears very dependent on the use of a sufficiently large area of interest while the estimation is sensitive to first-order network changes.

Through the implementation of appropriate measures, an average metric improvement of a factor of four can be achieved, reducing the likelihood of a misinterpretation of the InSAR time series. This underscores the effectiveness of the proposed test recipe in improving existing InSAR software.

Coastal areas are sensitive ecosystems, which are important as natural habitat, recreational areas and for protection of the hinterland. Coastal monitoring is essential in the analysis and prediction of coastal development. Coastal monitoring has become more urgent due to climate change and its effects on sea level and frequency of storm events. Permanent laser scanning (PLS) provides a tool to acquire 3-dimensional point clouds of an area nearly continuously over extended periods of time. PLS bridges the gap between incidental, in-situ measurement campaigns with high spatial detail, and frequent monitoring via satellite data at lower spatial resolution. Methods to process the complex high-resolution permanent laser scanning data are needed to find and analyse the effects of geomorphological processes over extended periods of time and with high spatial detail. This dissertation deals with the development of methods for spatio-temporal data mining of large 4D data sets from permanent laser scanning for the application of coastal monitoring on sandy beaches.

Two data sets of hourly 3-dimensional point clouds, acquired over periods of six months and three years at two different locations on the Dutch coast were analysed, to identify and assess geomorphological dynamics at the sediment surface. Each data set contains up to 20 000 epochs capturing the dynamics of the sandy beaches in Kijkduin and Noordwijk.

In this thesis two methods are developed: the application of multiple hypothesis testing for the estimation of minimal detectable bias and the generation of a so-called inventory of trends, and the application of clustering algorithms for grouping elevation time series. The first method using multiple hypothesis testing provides a means to define the minimal detectable bias for an expected model behaviour of time series from permanent laser scanning. This method provides a new way to detect small but persistent and statistically significant changes in longer time series derived from 3-dimensional point clouds. Using multiple hypothesis testing allows to identify linear changes with slopes of 0.032 m/day and sudden changes in elevation of 0.031 m with a given discriminatory power of 80% and significance level of 5% in 24-hour time series.

In an additional step, multiple hypothesis testing is used to reduce the complex permanent laser scanning data set to an inventory of trends, which consists of linear pieces of time series, matching the predefined statistical models and corresponding parameters. This method is applied to find and analyse times and areas where specific processes such as storms, aeolian sand transport or bulldozer works occur. The inventory of trends is particularly effective for the detection of aeolian sand transport, which has been difficult to identify using other coastal observations because it causes small, gradual deformations at the sediment surface.

The second method uses clustering algorithms to identify areas which are subject to similar change patterns. These change patterns are then easily associated with underlying physical and anthropogenic processes, mostly tidal induced changes and bulldozer works.

In summary, the developed methods allow to effectively detect deformations on sandy beaches and establish their origins, such as storms, tides, anthropogenic activities or aeolian sand transport, with a resolution and detail that has not been achieved until now. These results allow further analysis and interpretation of geomorphological coastal processes. For instance, the analysis of bulldozer works in our study area leads to the conclusion, that not only buildings themselves, but also the associated human interventions on the sandy beach around each building have a significant impact on coastal morphology and possibly lead to increased erosion.

The timing and magnitude of global sea level rise remains difficult to predict, driven for a large part by the potential instability of ice shelves in Antarctica. Ice shelves, the floating extension of the Antarctic ice sheet, govern the mass loss of the ice sheet by providing resistance (buttressing) to the grounded ice — thereby modulating the ice flow to the ocean. The short-term collapse or long-term weakening of ice shelves can result in drastic increases of ice discharge and Antarctic mass loss. Understanding the processes that affect the weakening, retreat, and instability of ice shelves is therefore essential in order to improve sea level rise predictions.

Damaged areas on ice shelves, consisting of fractures, crevasses and/or rifts, are first indicators of its weakening. As ice shelves weaken, they can provide less buttressing to the ice sheet, causing accelerated ice flow, heightened internal stress, and increased strain rates. This creates a feedback loop, further promoting damage development and ice mass loss through increased discharge. Moreover, the propagation of crevasses or rifts through the ice shelf eventually leads to calving of (often large) ice bergs. Observable damage is therefore an important precursor to this mode of mass loss. Damage has been considered key for the collapse of the Larsen B ice shelf and the retreat of Pine Island Glacier and Thwaites Glacier. Despite its significance for future ice shelf stability, damage processes remain one of the least understood in marine ice sheet dynamics. This dissertation therefore aims to improve our understanding of damage impacts on ice shelf weakening and retreat from an observational perspective.ined...

A vital component in the management of seismic hazard is the study of past seismic events. Classically, this has been the domain of seismology, which studies the dynamic manifestations of the event to infer properties such as epicenter and moment magnitude. More recently it has become possible to perform similar analyses on the basis of the static consequences of a seismic event, as satellite borne Synthetic Aperture Radar (SAR) data allows us to compare the local surface geometries before and aftera seismic event. The locality of the deformation data promises reconstructions with greater detail and subject to fewer model uncertainties.
With current technology, it is not possible to use SAR to their full potential. The non-linearity of the static dislocation problem that links faulting mechanisms to observed deformations causes any inverse method to require many evaluations of the forward model. This poses limits on the permissible cost of solving the dislocation problem, restricting most approaches to simplified model assumptions such as material homogeneity and absence of topography. In situations where more accurate information is available, this presents a clear opportunity for improvement by accelerating the computational methods instead.
This thesis presents the Weakly-enforced Slip Method (WSM), a modification of the Finite Element Method (FEM), as a fast approach for solving static dislocation problems. While the computational cost of the WSM is similar to that of the FEM for single dislocations, the WSM is significantly faster when many different dislocation geometries are considered, owing to the reuse of computationally expensive components such as matrix factors. This property makes the method ideally suited for inverse settings, opening the way to incorporating all available in situ data in a forward model that is simultaneously flexible and cheaply evaluable. Moreover, we prove that the WSM retains the essential convergence properties of the FEM.
A limitation of the WSM is that it produces continuous displacement fields, which implies a large error local to the dislocation. We show that this error decreases rapidly with distance, and that in a typical scenario the majority of deformation data has a discretization error that is smaller than observational noise, particularly when a fault is buried. In the case of shallow or rupturing faults, neighbouring data needs to be discarded from the analysis to avoid disruption. With this measure in place, we show via Bayesian inference of synthesized datasets that the discretization errors of the WSM do not significantly affect the inverse problem.

Long-term settlement is unavoidable but by good prediction accompanied risks can be reduced. Nowadays linear isotach models belong to the state of the art but result in very small strain rates when small load increments or unloading is applied. For that reason this study has focused on the validation of those predictions using InSAR. Different causes of settlement have been studied and it was found that fluctuations in groundwater do not affect the final settlement. Another influencing factor is the presence of organic matter and its degradation. This reduction of organic matter is caused by many factors and is yet too complicated to quantify and include in predicting models. In spite of the idea that predicted strain rates by linear isotach models are too small, InSAR results show that the measured displacement rates were actually smaller than predicted some years after construction for road segments of the A2 and that a new proposed model could be used to match these displacement rates better. The C+S model by Vergote et al. (2021) is an isotach model which uses non-linear isotachs which would reduce the strain rates faster than linear isotach models and viscoplastic swell is included which also reduces the total strain rate. Results on incremental loading tests show that this model captures the behaviour in unloading stages better than the linear isotach models but for conventional incremental loading tests the two models do not differ much. For field scale embankment scenarios the results show that the C+S model with non-linear isotachs and viscoplastic swell included will predict a longer swell period with more swell, lower total strain rates after the swell period with a faster decay and in the and less residual settlement.

Undiscovered underground cavities might exist in the subsurface. A catastrophic ground failure event follows when such a cavity starts to migrate upwards and finally intersects with the surface, resulting in a sinkhole. Catastrophic collapse events are usually preceded by precursory subsidence. An upward migrating cavity causes the development of a (subsidence) trough at the surface. The trough deepens as the cavity nears the surface, changing its surface expression. With the technique called InSAR, displacements over a large area can be measured. Literature shows that precursory subsidence is measurable using InSAR. However, automatically detecting impending sinkholes from InSAR displacements has not yet been researched in depth. This study shows that the developed novel arc-based temporal strategy can early detect an impending sinkhole. The kinematic model is adequate to model the surface expression of an impending sinkhole. The results are used to implement an artificial sinkhole based on the kinematic model into a subset to test the developed strategies. The first strategy was based on spatio-temporal characteristics of a sinkhole. This strategy can locate the subsiding area and indicate a surface expression size range. However, during this study, a second strategy was developed to identify anomalous behavior quicker and more reliable than the spatio-temporal strategy. The second strategy is arc-based and marks point measurements behaving anomalously. The result demonstrates a potential automated early warning system based on the InSAR displacement time series. The developed strategies harbor the potential of monitoring for impending sinkholes on a large scale. This study is anticipated to be a starting point for more development in early warning systems for impending sinkholes. Future research could entail verifying the strategies in regions with collapsed sinkholes.

Satellite radar interferometry (InSAR) is a powerful technique for monitoring deformation phenomena. While deformation phenomena occur in a three-dimensional (3D) world, one of the limitations of the InSAR phase observations is that they are only sensitive to the projection of the 3D displacement vector onto the radar line-of-sight (LoS) direction. To uniquely estimate the three displacement components, we would require at least three sets of spatiotemporally coinciding independent (STCI) LoS observations, (i.e., scatterers on an object that is not subject to internal deformations, observed at the same time) available over the same Region of Uniform Motion (RUM). More importantly, the system of equations needs to have a full rank coefficient matrix. Unfortunately, in most practical situations at most two sets of STCI LoS observations are available, resulting in an underdetermined system with an infinite amount of possible solutions.

Within the InSAR literature we encounter different approaches to address the underdeterminancy problem, unfortunately often with either mathematical or semantic flaws. We concluded that the InSAR community has no uniform way of addressing the underdeterminancy problem. We developed a taxonomy for the different fallacious approaches that can help by evaluating InSAR results and reviewing InSAR papers.

Moreover, using the east-north-up (ENU) reference frame for decomposing the LoS observations provides results that are not tuned to the needs of the end-user of an InSAR product. Therefore, we developed an alternative solution to the underdetermined problem, in the form of a `strap-down' approach, which uses a local strap-down reference system that is fixed to the deformation phenomenon with transversal, longitudinal, and normal (TLN) components. For many practical cases, such as line-infrastructure, landslides, or subsidence bowls, analysis of the main driving forces supports the assumption that significant deformations in the longitudinal direction are unlikely.

We found that using the strap-down approach gives physically more relevant estimates. Moreover, it results in more relevant estimates since it properly includes all uncertainties. We can further conclude that the conventional way of communicating (PS)-InSAR results by means of a `dot distribution map' is sub-optimal when considering the quality of the estimates. For many cases, `vector arrow maps', or traditional geodetic vector-based visualizations, including error ellipses are a viable and more optimal alternative.

A satellite remote sensing technique, Interferometric Synthetic Aperture Radar (InSAR), is able to provide surface displacement information on a millimeter level. In this study, data from the TerraSAR-X satellite collected in the years 2009-2018 over the area of Amsterdam is used. Even though radar data is a subject to multi-step processing, there are still several problems observed that can make the interpretation of the time-series difficult for users who are not experts in the radar remote sensing field. In this study we focus on unwrapping errors, partial decorrelation, and incorrectly fitted models. The unwrapping errors are handled as outlier detection problem and the rest as a time-series segmentation task. In order to address these issues, a data-driven approach is proposed. We show a method to detect unwrapping errors based on spatially neighboring points. A GUI is developed to collect expert knowledge in a form of assessing the time-series correctness, marking unwrapping errors, and dividing time-series into separate segments. This information is later used in the evaluation of several outlier detection and segmentation algorithms. We propose a supervised learning approach based on neural networks in order to use expert knowledge. Due to not enough labelled data available, a simulation is developed and used for training of the networks. We present two different approaches, one based on multi-label classification and one on binary classification. For each of them fully-connected neural networks and convolutional neural networks are compared.

The Delft real-time GNSS single-frequency precise point positioning (RT-SF-PPP) algorithm is extended to include velocity and receiver clock drift as unknown states to be estimated from Global Navigation Satellite Systems (GNSS) measurements. Carrier-phase ambiguities are assumed constant over time. Two different variance models are used, one obtains variance as a function of satellite elevation, and the other obtains variance as a function of carrier-to-noise density ratio as estimated by the receiver. The elevation based variance model was used in the original RT-SF-PPP algorithm, and adapted to include Doppler measurements. The carrier-to-noise density ratio based variance model components are estimated from double difference (DD) observation combinations using measurements obtained from a shortbaseline experiment with two receivers setup over multiple days. Two velocity observables are used and related to velocity and clock drift through the extended functional model of the original algorithm: the receiver generated Doppler and a time-derivative of the carrier-phase observable: the time-differenced carrier-phase (TDCP). Algorithmic performance is evaluated by the horizontal RMSE, which represents accuracy as the variance plus bias squared, precision and reliability. This was validated using three different experiments: a stationary receiver on top of a roof, a buoy freely adrift in the North Sea, and a receiver mounted on a car driving a regional road. It was found that in terms of position in the static experiment and under calm water conditions during the drifting buoy experiment the horizontal RMSE was between 0.429 and 0.530 [m], and under rough water conditions and a road partly flanked by fences and trees between 0.682 and 0.812 [m]. Furthermore in terms of velocity it was found that the TDCP observable in combination with the carrier-to-noise density based variance model has a horizontal RMSE between 0.014 and 0.068 [m/s] over all experiments, and using the Doppler observable with either variance model a RMSE between 0.033 and 0.122 [m/s]. The algorithm was even found by means of external reliability to be capable of detecting faults at the boundary of 0.5 [m] for position and 0.1 [m/s] for velocity in the TDCP observable case.

Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite Systems (GNSS) are widely used to monitor the dynamic behavior of the Earth. InSAR is a geodetic technique that estimates millimeter-level relative displacement time-series in an opportunistic network of a multitude of coherent points on the Earth’s surface, in a local datum. GNSS uses ground-based instrumentation to acquire time-series data over a limited number of specific and well-defined points in a known geodetic datum.The Integrated Geodetic Reference Station (IGRS) is designed to combine these (and other) techniques into one common instrument, establishing an integrated benchmark, i.e., a GNSS antenna and two radar corner reflectors, ensuring an identical kinematic behavior. This enables a geodetic datum connection, effectively enabling the InSAR results to be represented in a common geodetic datum, instead of a free network.However, the efficacy of the IGRS has not yet been proven, i.e., a thorough analysis of the first empirical results of an IGRS network has not yet been performed.Here we show that by using three years of data from a spatio-temporal network of 29 IGRS stations in an area of 60×60 km, and 742 independent Synthetic Aperture Radar (SAR) acquisitions, we reach a high level of agreement, demonstrating that IGRS can be used to connect InSAR information products to a well-defined geodetic datum.By using an Overall Model Test with a significance level of 5% we found that 96% of the double-difference arcs in time and space, sustain the null hypothesis that both the InSAR and the GNSS results stem from the same distribution. We found that the main reason for rejecting the null hypothesis for the remaining 4% of the double-difference arcs is that the results of both the InSAR and the GNSS are affected by a leakage of signal from the functional to the stochastic model. For the InSAR observations there is inadequately modeled atmosphere leaking into the stochastic model, while for the GNSS it appears that the precision estimate of the periodically moving stations is worse than the non-periodically moving stations, which suggests that the stochastic model is influenced by the the functional model.In the end, this study proved the efficacy of the IGRS to connect different geodetic datums, and this enables the InSAR results to be integrated in a well-defined geodetic datum.

Subsidence can be observed at various locations in the Netherlands. While it can be due to both shallow and deep subsurface processes, the shallow subsidence is mainly a result from compaction, oxidation and/or groundwater drainage. Moreover, for grasslands on organic soils, in particular pastures on drained peat soils, the vertical position of the ground level is subject to significant temporal variability. Temporal scales of the vertical motion are expected to vary between days and centuries, and spatial scales between millimeters and kilometers. Unfortunately, performing precise, reliable, and representative geodetic measurements of shallow subsidence processes is very difficult for soils, as fixed benchmarks do not exist. Here we propose an insitu measurement procedure for grass-covered soils using laser scanning, both terrestrial and airborne, using a vertically fixed reference platform. We show that for terrestrial laser scanning (TLS) it is possible to detect changes in the vertical position of soils with a systematic error of up to 2.6 mm and a standard deviation of 0.4 mm. Given a predefined level of significance of 훼 =0.05 (confidence level of 95%) and a detectability power of 훾 =90%, we achieve a minimal detectable vertical deformation (MDD) of 21.1 mm in our study area. We show that the results are influenced by the grass density and length, the incidence angle of the laser beam, as well as other settings of the laser scanner. We find that the parameter settings of the method for estimating soil surface, and subsequently the subsidence, has an influence on the results and related statistics. For airborne laser scanning (ALS), using a precisely leveled reference platform, we find that the quality of elevation estimates is still limited, requiring further considerations on the design of data acquisition surveys and the reference platforms. Our results demonstrate the ability of laser scanning technology for investigating shallow subsurface motion of grass-covered soils relative to a benchmark on a local scale. Based on the quality assessment, the detection of vertical ground level change is better understood in terms of time and probability. In future research, the factors affecting terrestrial laser scanning technology to accurately identify soils affected by vegetation, environment, and device condition, should be further studied.

Subsidence of the ground surface, caused by hydrocarbon production, compaction of soft ground layers or other subsidence causes, is a timely topic in the Netherlands. Geodetic measurements of the surface can help us improve our knowledge of the subsurface; this process is called geomechanical inversion. Improved knowledge on the subsurface is needed for example to improve deformation predictions and to safeguard subsurface and surface infrastructure. Related works in this domain use derivatives of geodetic measurements as input for their inversion methodologies, but not the measurements themselves. Performing geomechanical inversion with derivatives of geodetic measurements introduces correlations in the covariance matrix of the data, making error propagation into the geomechanical estimates more complex. Defining a direct relationship between measurements and geomechanical estimates and subsequently inverting this relationship, makes the error propagation less complex. This thesis presents a new methodology that can be used to estimate reservoir geomechanical parameters through direct inversion using measurements from optical leveling campaigns. In the context of this thesis, a direct inversion is an inversion of a linear relationship between data and measurements. In this thesis, we propose and test a workflow for the estimation of a simplified set of geomechanical parameters. Part of the workflow is an extensive testing procedure of the geodetic data. A Geertsma nucleus-of-strain model is used to express a source parameter term in function of optical leveling measurements. This source parameter term is a lumped term and consists of a volume term, a pressure term, and several elastic rock parameter terms. This system is inverted using a Tikhonov regularization with a spatial penalty term. The methodology is applied to optical leveling data from a case study (the Norg and Roden gas fields in the northern Netherlands) and shows promising results. The RMS between modeled and measured subsidence for the most promising parameterization is 3.0 mm. The proposed methodology leads to geomechanical estimates with formal quality description, that could improve geomechanical models and subsequently leads to a better understanding of the subsurface and better subsidence predictions. The geomechanical parameter that is estimated is lumped and without additional information, it is impossible to differentiate between individual compaction parameter terms. Feeding the problem more information might also relax the need for regularization but can lead to the introduction of bias. We believe that the framework proposed in this work can be a good starting point for further research that uses geodetic measurements directly as input for a geomechanical inversion.

Subsidence is affecting different parts of the Netherlands. The strongest subsidence is observed in the province of Groningen due to ongoing natural gas extraction. Subsidence is also observed at several locations in the province of South Holland, where the processes of peat oxidation, soil compaction and the withdrawal of groundwater are at the root of the problem. In South Limburg, the after-effects of coal mining are seen in the surface deformation, which is characterised by ground heave due to rising mine water, and the potential risks for sinkhole and local subsidence due to near-surface mining. Not only does subsidence cause damage to the natural and built environment, but it also increases the vulnerability towards flooding. Considering the rise in sea level, subsidence forms a pressing issue for low-lying countries, such as the Netherlands. The combined efforts of interferometric synthetic-aperture radar (InSAR) data, global positioning system (GPS) points and gravity measurements have led to the recent Bodemdalingskaart (2018), which represents the nationwide subsidence in three statistically inferred products. These products are the the total surface deformation, the deformation caused by shallow subsurface processes, and the deformation caused by deep subsurface processes. This has triggered the discussion on how the deformation from 'shallow' and 'deep' processes can or should be better understood in a physical sense. The aim of this research is to 'enrich' Sentinel-1 persistent scatterer interferometry (PSI) points with contextual information, or 'attribute-enrichment', in order to better understand the origin of the observed deformation. Classifications from the Dutch 'basisregistraties' (base registries) are assigned to the PSI points, which are stored in a spatial database. The classifications include information on the Dutch soil types and geomorphology. The classifications from Bodemkaart are used in an analysis of nationwide extent, focused on the deformation behaviour of different soils and their groundwater levels. The nationwide average deformation of all track results suggest subsiding trends of about -1.24 mm/yr for marine clay soils and -0.48 mm/yr for peat soils, whereas opposite trends of uplift are observed of, on average, +0.77 mm/yr for river clay soils and +0.54 mm/yr for sand. The soil and geomorphology datasets are also used in combination with the geographic classifications of the built environment to study the distinction between deep and shallow-induced deformation in Groningen and South Holland. Here, the 2 x 2 km grid cell representation of deformation makes it possible to characterise areas with overall low PSI point density. In the localised deformation cases, the behaviour of classes from different attributes can be directly compared, e.g. road polygons intersecting with soil type polygons. For South Limburg, risk-assessment-based classifications are used to enrich the PSI points in the study the coal mining after-effects in the identified risk areas. The risk-deformation results suggest an overall slowing trend in the ground heave of the potential impact areas. The results from the case studies highlight the efficiency of attribute-enriched PSI datasets for the interpretation of deformation, based on both direct and indirect physical classifications.