Coasts are constantly under the pressure of hydrodynamic conditions such as waves, tides, and storms. In the Netherlands, sand nourishments are executed every few years in order to maintain the country’s sandy beaches for purposes of safety, recreation, and ecology. In order to determine where to carry out these nourishments, the whole sandy coastline of the Netherlands is measured annually by Rijkswaterstaat. This annual survey, called JAarlijkse KUStmetingen (JARKUS), monitors where (increasing) erosive trends appear or persist and, due to its timespan (dating back to 1843), is a valuable dataset to understand the evolution of the Dutch coast. However, this measurement survey is restricted to its annual frequency and is costly. The use of optical satellite imagery for measuring land cover types and geographic features is rapidly becoming more popular due to their high temporal frequency and relatively low costs (due to the public availability of some satellite missions, such as NASA’s Landsat and ESA’s Sentinel-2 missions). This research studied the possibilities of using optical satellite imagery for measuring beach width dynamics in addition to the existing measurement campaigns.

In this study, we derived the Satellite-Derived Beach Width (SDBW) as the cross-shore distance between the Satellite-Derived Shoreline (SDS) and the Satellite-Derived Vegetation line (SDV). We adopted a widely used and validated method for SDS detection and adapted this method to establish the SDV detection method. The SDS and SDV are derived from optical satellites by deriving vectors from the border between two contrasting land cover types that are identified by differences in (sun)light reflectance values. The SDS and is derived from the contrast between water and land, the SDV from the contrast between and sand/sediment and vegetation. The SDBW data was measured from both composite and individual satellite images. The different techniques are suited for different applications, and both have their advantages and disadvantages. A composite image is an image that is composed of a sequence of individual satellite images available within a set window. E.g., a composite is the average image of all those images. Recent research showed that, at the cost of temporal resolution, composite images are suited for analysis of long-term (structural) shoreline trends since they mitigate certain factors influencing image quality (such as clouds and cloud shadows, waves and tides, and satellite instrument errors). Individual images are better suited for analyses of short-term dynamics since they provide instantaneous measurement data. However, they are hampered more by the factors mentioned above, and hence need to be screened before use...

Sea Level Rise is anticipated to significantly affect human development in the coastal zone by the end of the Twenty-First Century. The rising waters bring greater risk of floods, submergence of low lying land, salinization of freshwater supplies, and the erosion of sandy beaches. Quantifying these impacts spurns action by those hoping to adapt to changing environmental conditions, but sea level rise acts at such large scales that this quantification proves difficult. Dynamic Interactive Vulnerability Analysis (DIVA) provides this capacity by modelling the growth of society parallel to environmental forcing from IPCC scenarios and human adaptation measures in the coastal zone. This thesis attempts to use new information and technology to expand existing tools which quantify the impacts of beach erosion due to sea level rise and human adaptation using beach nourishment on a global scale. This echoes earlier work by (Hinkel et al., 2013). To accomplish this task, a methodology is developed to conduct and aggregate large amounts of information into the DIVA coastal database, and a secondary global risk model develops refined nourishment adaptation cost information.
Data collection over the large scales covered by the DIVA model is difficult, and existing approximations within the model are categorically applied to poorly resolved datasets and archived information. Recent developments in remote sensing created a new database which indicates the location of beaches at a global scale. The utility of this information is the primary motivation for this study. Similarly, adaptation costs in DIVA are not well resolved, and available expert judgment is leveraged to allow more insight into these practices.
These modifications have complex individual interactions with human coastal development, and simple superposition is not an effective tool to predict their combined impact. The locations most significantly impacted by the modifications are typically island states previously categorized without beaches. Adaptation favors wealthy locations with a large population density. The costs of lost land and forced migration in these areas outweigh adaptation costs by substantial margins in some cases, and nourishment .
Recreation of the Hinkel et al. study is not possible with the current DIVA architecture, and the lack of reasoned relation to this work limits interpretation. As a large scale model using aggregated parameters, DIVA is limited by its ignorance of local-level processes. Further limitations include a lack of resolved information for secondary erosive forcing from tidal basins, whose impacts are included, yet not specifically addressed.
DIVA does address the question of vulnerability at a national level, and can inform decision makers on the costs and protections provided through adaptation. It can highlight the growth and development of adaptation measures in response to sea level rise and weigh them against the cost of inaction. In doing so, DIVA highlights areas where pretection is economically feasible and viable for the mitigation of sea-level-rise-induced beach erosion.