Addressing the impact of sea-level rise in coastal areas requires sustainable management strategies based on informed decision-making, and thus a shoreline projection is important. This research explores methods to improve shoreline change projections by obtaining a long-term shoreline change trend, empirically from \acrfull{sds} observations, and implementing a coastal impact model to project the future shoreline. We defined a heuristic approach to obtain a clean-\enquote{primary}- shoreline-change signal from all raw \acrshort{sds} observations that are available at transect. Long-term trend analysis from the primary shoreline-change signals results in a more accurate long-term shoreline-change trend. The improvement is also observed in Truc Vert, a meso-tidal case previously well known for low \acrshort{sds} accuracy, where the primary series results in accurate long-term shoreline trend regimes. Our projections in open sandy transects show that long-term trends predominantly influence shoreline change patterns and regimes compared to the retreat due to sea level rise. Using input comparable to the input of an earlier study, projections in seven out of eight regions indicate less retreat, except in North Carolina where the regional sea-level rise projections are relatively high. Compared to a hybrid, physics- and process-based, model, our projections showed higher progradation and erosion in \enquote{hot spots}, revealing limitations in linear trend extrapolation for capturing non-linear shoreline behaviour. We can conclude that future shoreline-change projections can be improved by using more accurate long-term shoreline-change trends. The framework's applicability is best suited to open sandy beaches with minimal human intervention. This highlights the need for better coastal typology classification that formalizes the concept of coastal type and coastal squeeze in the backshore of the beach. The outlook for the development of the shoreline projection framework is to explore the non-linear behaviour of shoreline change. This can be captured by decomposing the primary shoreline-change series into different signals that better represent the shoreline-change behaviour.

Sand waves can have a significant impact on offshore activities, and with the global push for sustainability and more renewable energy sources, these negative impacts will become more prevalent. Sand waves in the North Sea have a height up to a couple of meters and a wavelength between 100 and 1000 meters. The migration or change in asymmetry of sand waves can cause free spans in export cables leading to buckling or vibration causing failure. The migration can also cause cables to be exposed on the sea bed and in danger from offshore activities like anchors and fishing boats.

This research focuses on utilizing satellite data to determine sand wave characteristics. Satellites like Sentinel-1 and Sentinel-2 which are used in this research have global availability of data over multiple years at a 10 meter resolution. Sentinel-1 has a Synthetic Aperture Radar (SAR) which creates images by sending out microwave signals and recording the strength and the time delay of the returning signals. Sentinel-2 has an optical instrument which creates images by recording the reflected light from the sun on the earth's surface. Sand waves were detected through the change in Sea Surface Roughness due to the current interaction with the sea bed as described in the Alpers-Hennings Model. The difference in resulting sand wave characteristics between SAR and optical images were determined.

Three initial areas of interest off the coast of the Netherlands were chosen based on their different characteristics. Hoek van Holland contains short irregular sand waves. Holland Kust Zuid contains long regular sand waves. The third location is Alkmaar, which contains no sand waves and was used to determine the results of the methodology when a location has no sand waves present. The first step was to determine the environmental conditions necessary for sand waves to be visible in satellite images. Image collections for an area of interest over a set period of time were filtered based on the environmental parameters. These parameters include mean glint angle and cloud cover for optical images, and wind and current speed for both optical and SAR images. Threshold values were determined through literature. The wind speed should be between 3 and 12 m/s, the current speed should be greater than 0.4 m/s, and the mean glint angle for the latitude of the North Sea is less than 56 degrees. The image collection for the year 2021 for the three different areas of interest was filtered based on these parameters. The final collection after filtering was then manually checked for the visibility of sand waves. This number was lower than the initial filtered collection which means that there are additional factors that need to be accounted for.

A methodology was created to determine the sand wave characteristics over an area by applying a Fast Fourier Transformation (FFT) and calculating the sand wavelength and wave angle from the resulting signal. This was first applied to the three different areas of interest within the North Sea, Hoek van Holland, Holland Kust Zuid, and Alkmaar. Using the resulting image collection at these locations the sand wave characteristics were calculated. Although there are no sand waves at Alkmaar, due to the methodology, sand wave characteristics are still calculated. Also, the methodology works better at Holland Kust Zuid with long regular sand waves compared to at Hoek van Holland where sand waves are shorter and irregular.

To determine at which scale the satellite images should be viewed at to obtain information on the sand wave characteristics, the data was split into different area sizes and a FFT performed. The resulting average values and spread of the sand wavelength and wave angle were compared at different area sizes for optical and SAR images, as well as Multi-Beam Sonar (MBES). This resulted in an area of 5 by 5 kilometers which allows for the correct signals to be read across different possible sand wavelengths. Also, the effect of different sources of noise on the FFT and calculation of the sand wave characteristics for optical and SAR images was determined. From this it was seen that ships within a satellite image containing sand waves introduce a very strong signal within the FFT. This prevents the sand wave characteristics from being calculated correctly. Wind farms within a satellite image affect SAR images, and not optical images. Suspended sediment transport and clouds, which only occur within optical images, have different effects. Optical images are not affected by sediment transport because the current direction will always be perpendicular to sand wave crests and therefore does not influence the calculation of sand wave characteristics. Clouds, similarly to ships, introduce a strong signal within the FFT which blocks the signal of the sand waves. Dark patches (like rain cells), which effect SAR images, block the visibility of sand waves within satellite images and therefore there no signal corresponding to sand waves within the FFT can be found.

Then, the area of interest was increased to the entire southern North Sea. Satellite images over the North Sea were downloaded in 5 by 5 km tiles for a single satellite pass over per year where the environmental parameters met the conditions necessary to view sand waves. As for the local scale cases, a FFT was applied to each tile and the sand wave characteristics were calculated including sand wavelength, wave angle, density, and spatial frequency. The differences in characteristics were compared from year to year. Typically, the difference is less than 200 meters in sand wavelength, however there are points where the difference is much larger. This is due to noise affecting the calculated value. Additionally, the data for wind speed and current speed for the date and time of the satellite image was determined. From this it is seen that optical images can contain sand waves at lower wind and current speeds, at the lower end of the boundary conditions. Sand waves are only visible in SAR images when the current speed is very high. Also, the results were compared to the water depth of the North Sea. At depths less than 40 meters which occurs on the Dutch Continental Shelf sand waves are almost always visible with the correct environmental parameters. At depths greater than 40 meters, which occurs on the West side of the sand wave field, sand waves are not visible consistently. The calculated sand wavelength over the North Sea was also compared to wavelength obtained from MBES. This resulted in low correlation values for both optical and SAR. As a result, noise present in satellite images should be taken into account.

By both applying the methodology to smaller scale cases and to the entire North Sea, it is possible to detect sand waves in different environments. Using the change in sea surface roughness due to sand waves it is possible to see sand waves in both optical and SAR satellite images. By applying the Fourier transformation the average sand wave characteristics over an area can be calculated. The methodology is limited by environmental parameters and noise that can be present in the satellite images. SAR and optical images require specific wind and current conditions, while optical images also requires specific mean glint angles and a low cloud cover. Different sources of noise also have a negative impact, adding signals to the FFT that do not correspond to the sand waves. Although the methodology proposed in this thesis is successful in determining the average sand wave characteristics over an area, more research for this topic would increase the possibilities. This includes using additional conditions for filtering image collections for sand waves, removing sources of noise, utilizing higher resolution satellite data, and testing the methodology on different sand wave fields that have different characteristics, such as symmetry. There are still many possibilities that can be explored in using satellite images to determine sand wave characteristics.

Since early history, humans have been attracted to coastal areas. This can be related to the economic benefits of these areas due to access of ocean navigation, coastal fisheries, tourism and recreation (Seas and Plans, 2011). Around 40% of the world’s population lives within 100 km of the coast (Seas and Plans, 2011). People are drawn to sandy beaches in particular because of its aesthetics and value for specific economic amenities (Luijendijk et al., 2018). Nevertheless, as these beaches are dynamic both in time and space, proper coastal management is required to prevent loss of land and secure future coastal life.

Up till now, studies into coastal erosion have been conducted locally, resulting in site specific observations. However, the promising results of using satellite imagery in the field of coastal engineering allowed studies to be performed at larger spatial scales. This can lead to the identification of areas with similar characteristics, resulting in methodological standardization of approaching a specific problem. A first step toward this new approach of studying shoreline evolution was taken by Luijendijk et al. (2018) who presented a global dataset of annual shoreline positions for sandy beaches over the period 1984-2016 using satellite derived shorelines
(SDS). However, the drivers (causes) of shoreline evolution on a global scale were still unknown, making it only suitable for identifying areas of structural shoreline change, but less suitable for deriving coastal management solutions. Therefore, the research objective in this study is to identify and characterize drivers of shoreline evolution on a global scale using SDS.

This study focused on dynamic sandy beaches, or hotspots, extracted using a method developed by Kras (2019). In this method, using a 2.5-kilometer moving window, transects showing structural shoreline changes and similar characteristics both in space and time were grouped. The small size of the moving window led to locally created hotspots, 95% of which had a spatial extent of less than 10 kilometers, allowing to study shoreline drivers with small to moderate spatial scales (∼10 kilometer). Therefore, the main focus in this study lies on seasonality as a natural driver of shoreline evolution and three anthropogenic drivers: reclamations, nourishments and littoral drift barriers. As seasonality shows inter-annual variability, the temporal resolution of the SDS is increased from annual to monthly.
Using time series decomposition methods, different parameters are extracted that can be used to link the drivers to the SDS. Besides temporal parameters, also parameters related to spatial characteristics are considered. These parameters can be split into identification parameters, used for identifying a driver, and informative parameters, providing knowledge on the behavior of the driver. These parameters were developed and tested using local case studies. Results from these local case studies showed that the identification parameters showed similar behavior along the case studies. This implies that the identification parameters correctly reflect a driver’s behavior. Next, identification of the drivers was verified on a larger scale, all transects within hotspots on West-European coastlines. Verification was done on hundreds of samples using literature or manual inspection of satellite images. Using precision scores, the fraction of true positives to the total identified cases, optimal settings were derived for identification of the drivers. These settings resulted in a pattern of driver identification and characterization along the West-European coastline that is supported by literature.

With the optimal settings for identification determined, the methods were deployed on a global scale. The global dataset consisted out of 3033 prograding and 2121 retreating hotspots containing over 58 thousand transects in total. For these hotspots, SDS were generated over the period 1984-2021 with a monthly temporal resolution. This resulted in a global dataset of more than 26 million monthly shoreline positions. Two other processes, in addition to a seasonal change in wave height, were found to be able to generate seasonal variations in coastline positions from this global dataset. At the Red Sea, even though the wave climate is low in energy (Langodan et al., 2017), the coast is characterized by seasonal behavior. However, in this basin, seasonal variations in water levels rather than the wave climate best described this pattern. In addition to varying wave height and water level, seasonal beach morphology can also be caused by a shift in wave direction. This was observed in southern and western parts of Australia. Furthermore, non-seasonal beaches were primarily seen in low-energetic wave regions where neither of these other two processes occurred, as is the case in the Mediterranean. In regions where seasonal shoreline fluctuations are caused by differences in wave energy, minimum shoreline positions were found at the start of the summer. However, the period in which minimal shoreline positions are observed may be observed at a different time of the year in regions where seasonal shoreline behavior is driven by water level variations or a shift in wave direction. The identification of reclamations pointed out that this driver was especially linked to shoreline evolution in the Middle East and East-Asia. Furthermore, while the amount of constructions of reclamations remained
constant on a global level over the period 1987-2017, in these two areas an increase was observed. Opposite behavior was found by the identification of nourishments, as this driver was identified more often in the period 2007-2017 compared to the two decades before that. Moreover, nourishments were mostly observed in Western countries, for example the USA and the Netherlands. Nevertheless, also in Non-Western countries, an increment over time in the amount of nourishments could be detected. This indicates that throughout the entire world the use of nourishments as a measure to prevent coastal erosion is increasing. Shoreline evolution linked to littoral drift barriers was mostly observed in North-America, Europe and Africa. Downdrift (erosive) hotspots were mostly observed in Africa while in North-America and Europe mostly updrift (accreting) hotspots were linked to littoral drift barriers. On a global level, a combination of an updrift and downdrift hotspots (a pair) was observed in only 2% of all hotspots.

The outcomes above can support local-scale studies by identifying the drivers of shoreline evolution, describing their characteristics and even create standardization by analyzing areas with similar behavior. Hence, it can be concluded that spatiotemporal parameters describing the behavior of a driver can be used to identify and characterize drivers on a global scale using SDS. Nevertheless, not all drivers of shoreline evolution were included in this study. Therefore, to include drivers with larger spatial scales, hotspots should be extracted by using a larger moving spatial window. Furthermore, by increasing the spatiotemporal resolution on which this extraction is based, accuracy of the spatial extent of the hotspots is expected to increase. The small proportion of pairs identified for littoral drift barriers can be partly explained by the erroneous spatial extent of some hotspots. Finally, drivers are identified independently from each other neglecting their interactions. Even though interactions might be complex, drivers should not be identified independently as this will rather
require local studies than support them. Still, even though refinement and further development of the methods is required, this research has shown that identifying the drivers of shoreline development on a global scale using SDS has great potential for sustainable coastal management in the face of future challenges

Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior for sustainable decision-making. Currently, these insights are gathered by process-driven models that are often time expensive, require local input and calibration, and are often limited by equations. However, satellite imagery proved to be a promising data source to derive historic shoreline behavior on a global scale. Machine Learning (ML) algorithms are suggested to create an extra in-depth understanding of these satellite images. The increase in the availability of both satellite data and ML algorithms opens possibilities for shoreline forecasting on a global scale. This research aims to improve forecasting of shorelines by creating a global model in which cross-time series information can be used. To encourage a meaningful exchange of time series information, a clustering forecasting approach is proposed.This research builds upon the yearly SDS data behind the Shoreline Monitor of Deltares. The SDS dataset captures shoreline evolution and behavior on a global scale by quantifying it in time series. The SDS dataset consists of transects every 500 m along the global coast and contains 33 years (1984-2016) of shoreline evolution. First, in order to cluster transects, the features for clustering were defined. Here, clustering features were divided into time series features and hydraulic and geomorphic features. Whether transect clustering could improve shoreline forecasting, based on these time series and hydraulic and geomorphic features, was explored. For this thesis, it was decided to focus on time series features for further clustering usage. Eventually, a shape-based transect representation was chosen as feature for input of the clustering algorithm. Around 350,000 globally distributed sandy transects were assigned to nine different main clusters, with a semi-unsupervised clustering approach. These nine clusters captured global sandy shoreline behavior ranging from extreme erosion to extreme accretion and proved to be a practical tool to quantify the (distribution of) shoreline behavior on different spatial scales. Subsequently, a second supervised step was developed to create sub-clusters to gain more insights into the trends of the nine clusters. With this supervised sub-clustering step, three sub-clusters were generated for each of the five largest clusters, allowing for the distinction between accelerating and decelerating behavior in the last decade of time series per cluster. Global, country-level and local case studies showed the potential of these sub-cluster refinements. Hereafter, the nine main clusters were separately used as input for four different forecasting algorithms. This resulted in predictions per cluster with a more reliable and more accurate seven-year forecast for >95 % of the transects, sometimes improving accuracy up to factor 15, compared to recently published work. Furthermore, a multi-method approach was created to determine the most reliable forecast, on a local scale by combining overall accuracy and information gained during clustering. Time series clustering enhances the forecasting of transects based on overall accuracy and local reliability. The multi-method approach for selecting the appropriate prediction on a local level, could be a practical tool for governments to apply. Besides, a vulnerability assessment explores the possible application of the predictions and underlines the need for global databases regarding the coastal zone. However, further research should consider incorporating hydraulic and geomorphic features to strengthen the clustering of transects. Hence, shoreline forecasting based on historical data should be considered as a valuable tool for sustainable decision-making regarding coastal zones.