Unequal deformation of the soil can cause deformation or damage to buildings, like tilted facades or cracks in walls. This research investigates how deformation of a building can be analyzed using Light Detection And Ranging (LiDAR) data. Cyclomedia captures LiDAR data yearly in the Netherlands making it possible to analyze either one or multiple epochs of data. If deformation is monitored, failure can be predicted, and repairs can be performed in time.

A subsiding area is found using the Dutch surface motion map. The data points of a single building are fetched from the LiDAR point cloud to analyze the following types of deformation: a difference in the torsion and tilt angle of the facade between two epochs of data; the tilt angle of the facade for a single epoch of data; and local deformation patterns on the facade. The data is segmented before computing the angles and analyzing the local deformation patterns. During segmentation, points that do not correspond to the façade (like a sunshade or windows) are removed. After segmentation, the facade is modeled by fitting a plane using Principal Component Analysis (PCA). The plane parameters (A, B, C, D) are used to determine the torsion and tilt angles. If the torsion or tilt angles are large (above a degree), it is expected the facade has deformed. Local deformation patterns can be analyzed by using a raster containing the distance of the segmented points with an accuracy of 8 centimeters. Besides the point clouds, Cyclomedia also captures 360° panoramic images (Cycloramas). These Cycloramas can be used to explain patterns that are visible in the rasters. For example, objects in front of the building like a bench or a sunshade.

This research uses Random Sample Consensus (RANSAC) to segment the data. From the results, it can be concluded RANSAC is not very predictable because random points are taken as input. So, points corresponding to the facade can be removed instead of points corresponding to windows or doors. Therefore, it is recommended to use another segmentation method for future research instead of RANSAC. Machine learning could be a good alternative to remove objects like windows and other unwanted points from the data.

There is a growing demand for detailed and accurate landslide maps and inventories worldwide. The mapping of landslides is essential for emergency response, disaster mitigation and a better understanding of landslides. Currently, it is still difficult to detect the timing and extent of landslides accurately through satellite imagery. Most often optical satellite imagery is used, but this is limited since it requires daylight as well as cloud-free conditions in order to observe anything on the Earth's surface. Synthetic Aperture Radar (SAR) amplitude imagery shows great potential since it can overcome these disadvantages, the speckle and geometric effects make it difficult to detect landslides. Both SAR amplitude images without temporal averaging and with temporal averaging, within a time window of one month, were tested. This study introduces the use of SAR amplitude images by two Deep Learning models, a U-Net and a Pix2Pix conditional Generative Adversarial Network, to detect and map landslides. These two models are trained and tested on several regions in South-East Asia where landslides have occurred recently.
The two models were not able to detect and map the landslides based on the SAR amplitude imagery, as shown by the mean Intersection over Union values which are smaller than 0.01 and the inaccurately predicted images.
Speckle reduction by temporal averaging of pre-and post-event images and consequently taking the log-based amplitude ratio, Aratio, is recommended when using SAR amplitude imagery. Additionally, reduction of geometric effects by averaging images of descending and ascending order together is recommended.
Despite the application of these measures, the two Deep Learning models used in this study don't have a successful outcome. In fact, a positive Aratio returns landslide scars better than the models. The landslide detection becomes more effective when temporal averaging is done over more time, although this is impractical for timely landslide detection. In general optical imagery is recommended over SAR amplitude imagery, if available within three months after the landslide event. Otherwise, SAR amplitude imagery can be used, by using the aforementioned averaging and a threshold method on positive Aratio.

The stress on global food security is expected to increase. Hence, crop and drought monitoring will become increasingly important in the future. Synthetic Aperture Radars (SAR) are able to penetrate clouds and thus reliably provide data. Despite the extensive literature that can be found on the use of high spatio-temporal resolution SAR data, a study investigating the influence of drought on Sentinel-1 data over agricultural crops has yet to be conducted. This research aims to bridge this knowledge gap by utilizing Sentinel-1 data. The Sentinel-1 data is acquired and processed in Google Earth Engine and afterwards, data analysis is performed using Python. This results in parcel level SAR (VV, VH and VH/VV) data. This research is focused on maize, sugar beet, potato, onion and barley parcels in study areas in the Netherlands during the 2017, 2018 and 2019 agricultural summer season, of which 2018 and 2019 were impacted heavily by drought. 
This research demonstrates that phenological changes are reflected in Sentinel-1 data with increasing backscatter intensities during leaf development and stem elongation phases. Subsequently, saturation occurs which halts the rapid increase of backscatter. During harvest, the VH/VV ratio decreases rapidly. Time series of barley behave differently due to its unique vertical structure.The results show that VV and VH backscatter values are 2.5, 2 and 1 dB lower during the 2018 drought compared to 2017 for maize, sugar beet and potato parcels, respectively. Furthermore, the seasonal VH/VV ratio cycle for maize, onion and barley is shorter in a drought year and shortest in 2018. The VH/VV ratio cycle in 2018 was 30, 10 and 20 days shorter compared to 2017 for maize, onion and barley, respectively. Lastly, significantly lower VH/VV ratio values are observed during the vegetative stages in 2019. The percentage of individual parcels that show responses similar to aggregated responses ranges from 68% to 100%. Moreover, the results show that the overpass time has a large influence on drought response. Morning passes show significant increase in the magnitude of the VV and VH backscatter drop during the drought periods, especially for sugar beet and potato.The regional variability was assessed by comparing parcel backscatter from the northern part of the Vechtstromen water board, the Scheldestromen water board and the Flevopolder. Generally, drought impact is found to be most extreme in Vechtstromen. However, onions in 2018 were impacted most in Scheldestromen according to yield data. This clearly translated into lower VH backscatter and VH/VV ratio values during and after the drought period. Also, regional differences in maize time series caused by irrigation are observed. The results show that areas in which irrigation was allowed with ground and open water had a longer VH/VV ratio cycle in 2018, compared to areas in which irrigation was allowed only with groundwater.Overall, the usage of Sentinel-1 data for drought monitoring purposes shows tremendous potential. This gives a promising outlook on the use of dense C-band SAR data for the detection of crop drought stress.

In this study an analysis of the efficacy of using satellite data, in the form of InSAR measurements, as an extension of the volcanic monitoring network on Saba and St. Eustatius is performed. For this research, data from three different satellites that operate at three different wavelengths are available: ALOS-2 (L-band SAR), Sentinel-1 (C-band SAR) and PAZ (X-band SAR). The data are analysed through the formation of interferograms that are obtained using the Delft Object-oriented Radar Interferometric Software (DORIS) and Persistent Scatterer Interferometry (PSI) performed following the Delft Persistent Scatterer Interferometry (DePSI) algorithm.
The interferograms and PSI results differ strongly per satellite and are affected by the combined impact of several factors. In this study the impact of the misalignment of the master image used in the generation of the interferograms with respect to the Digital Elevation Model (DEM) is discussed, as well as the impact of the incidence angle, the spatial resolution, the temporal resolution, the perpendicular baseline, the number of available images and the wavelength.
The interferograms of ALOS-2 are of a good quality, however the low temporal resolution makes studying fast surface deformation difficult. However, they could be used to study surface changes in retrospect or to study slower processes, such as the pressurisation of a magma chamber, causing gradual surface deformation. The
low spatial resolution makes the interferograms of Sentinel-1 difficult to interpret and the interferograms for the PAZ data currently show too large amounts of decorrelation to study surface deformations.
The PSI analysis produces reliable results for Sentinel-1. The estimated linear deformation for the Persistent Scatterers (PS) shows constant values over both islands, which are centred around 0 mm/y and have low standard deviations. Therefore it is assumed, based on the data and prior knowledge about the area, that there is currently no deformation on either of the islands. The PSI analyses for the other two satellites do not provide reliable results, because the number of available images in the stacks is too low (only 10-12 images compared to the 116-123 available images for Sentinel-1). The PAZ data might be used in the future, when more images are available, however the low temporal resolution of the ALOS-2 data means that an appropriate stack cannot be acquired within the design lifetime of the satellite.
The ALOS-2 interferograms and the PSI analysis for Sentinel-1 could thus at present be a useful addition to the ground-based monitoring network. When a larger stack of data for PAZ is available, the PSI analysis could potentially be conducted again in order to determine its use as a volcanic monitoring tool.

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.

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.

Repeat-pass acquisitions with coherent Synthetic Aperture Radar (SAR) systems, preserving both phase and amplitude, are more readily available than ever (Bruzzone, 2016). Phase measurements from SAR systems have seen widespread use in the Interferometric Synthetic Aperture (InSAR) technique to measure deformations and elevations since the late 1980’s (Hanssen, 2002). Since the late 1990’s an increase in radar-based change detection is observed, mainly relying on amplitude measurements (Ajadi et al., 2016; Dekker, 1998). The unpredictive multiplicative noise-like speckle, inherent to coherent SAR,makes change detection in SAR imagery difficult (Bamler, 2015). However, the advantages in the all-weather mapping capabilities and object penetrating properties of SAR make it a suitable remote sensing technique for certain applications, such as natural disaster damage assessment (Bruzzone and Prieto, 2000). Broadly speaking, change detection in SAR-based images usually consists of applying an operator on two spatially filtered SAR images to create a difference image (DI), which is then analysed for change points by thresholding and/or clustering (e.g. Alphonse and Biju, 2015). However, such an approach completely neglects the long-term stability of a pixel. When taking the temporal evolution of a pixel into account, the steep increase in data volume puts an emphasis on finding an optimal (’best practice’) approach to the multitemporal change point detection problem. Here it is shown that change point detection methods that properly take the temporal evolution of a pixel into account can provide good segmentation results in multi-temporal SAR data stacks, even in unfiltered stacks that preserve the complete spatial resolution and without considering the spatial context in a pixel’s direct neighbourhood. Moreover, it is found thatmore sophisticated change point detection algorithms don’t
necessarily yield superior segmentation results for various discontinuity functions. This means algorithm selection has to be application driven. The results demonstrate that the suitability of algebraic methods in heterogeneous areas is limited, whereas proper time series analysis yields fairly consistent results over different land covers within the same image. Often, little effort is spend on finding an optimal approach; neglecting data selection and storage, a sensitivity analysis and/or the post-processing analysis procedure, all of which are shown or known to increase the success rate, efficiency and understanding of the segmentation results. It is anticipated that change point detection in SAR imagery will shift away from the classical bi-temporal methodology and multi temporal approaches will become the norm, be it by decomposing multi-temporal stacks or time series analysis.

The current procedure of the 11 Air Manoeuvre Brigade (11 AMB) is based on a manual non integrated and slow method that uses only outdated and only topographical data. In the procedure are all 5 aspects of a potential landing zone taken into account, the so called 5S: Slope, Shoots, Surface, Size and Security.
This research led to an automated and fast method that uses more actual 3D data (WorldDEM) and delivers better results (validated on the Edesche Heide) than using the current method. The automated procedure has been restricted to two of the five aspects (Slope and Shoots). The aspects Size and Security are discussed in the research but not integrated in the automated method.
Using the new method the Pathfinders can be better prepared for a mission, can perform their tasks faster and can perform their tasks saver.