Chlorophyll (Chl) and Total Suspended Matter (TSM) are both important water quality parameters since they influence the amount of oxygen & amount of light penetrating the water. Oxygen and light are vital in marine ecosystems. The Dutch governmental organisation, Rijkswaterstaat (RWS), has been monitoring the water quality of the Dutch part of the North Sea for the last 35 years. A research vessel takes off to sample parameters such as chlorophyll and TSM every few weeks at fixed locations. Recently, Sentinel-3 satellites started to provide satellite-based information on air or water quality. It is expected that products from the Ocean Land Colour Instrument (OLCI) sensor, on board of Sentinel-3, can greatly improve both the geographical and temporal coverage of these parameters. For the Dutch coastal waters this is challenging, because they predominantly consist of complex coastal waters. This study focusses on the validation of water quality parameters (Chl and TSM) available from Sentinel-3A OLCI observations. To verify the processing line of the OLCI data products it was desired to evaluate the following variables as well: 1) the aerosol optical thickness used in the atmospheric aerosol correction process, which is an important step for deriving the water-leaving reflectance, and 2) the water-leaving reflectance itself used as the main signal for deriving Chl and TSM. OLCI water quality data products were compared to Rijkswaterstaat in-situ measurements for the months May until September of 2017. Furthermore, OLCI’s Chl and TSM were compared with climatologies of MERIS data. The OLCI water-leaving reflectance and aerosol optical thickness data products were compared with observations from the Belgian AERONET-OC station Thornton. To evaluate the spatial distribution of OLCI's aerosol optical thickness comparisons with nearly coincident MODIS-AQUA observations were made. To evaluate the spatial variability of OLCI's data products boxplots were created of Chl, TSM and the aerosol optical thickness. The water quality products of OLCI consist of a Chl product determined by the OC4Me algorithm and a Chl & TSM product derived from a neural network. OLCI Chl obtained from the OC4Me algorithm showed an overestimation of a factor 2 compared to the in-situ measurements. The Chl results of the neural network compared well with the in-situ measurements showing a correlation coefficient of 0.77. OLCI TSM showed an unrealistic underestimation of a factor 4 compared to in-situ measurements. Boxplots showed that the largest spatial variability is found at stations <50 km from the coast for the three water quality products. This unrealistic underestimation of scattering TSM would imply an underestimation of the water-leaving reflectance in all the bands. Comparing OLCI's water-leaving reflectance with AERONET's showed underestimations in the blue and green bands only. OLCI’s water-leaving reflectance of the red and near-infra-red (NIR) bands correlated well with the AERONET-OC measurements. The aerosol optical thickness data product showed unrealistic overestimations of OLCI compared to AERONET-OC, but had a correlation coefficient of 0.58 when comparing it to MODIS aerosol optical thickness product. The spatial variability of OLCI's aerosol optical thickness is very high with differences of more than 40% per kilometre. In general, all products seem to have unrealistic values around clouds and in coastal areas, especially the aerosol optical thickness product. The pixels in those regions are different from other pixels. These results imply that further research into the software implementation of the radiative transfer models, lookup tables, vicarious calibrations and Neural Networks is needed to understand how retrievals of Chl and TSM concentrations are influenced. Such a fundamental understanding is ultimately also of interest for end users and all parties providing products and services for marine applications.

Coastal dunes habitats are in the transition zone of marine and terrestrial ecosystems and therefore have high biodiversity in terms of animal and vegetation species. These species provide important ecosystem services to the human kind. Due to the development and operation of Maasvlakte 2, an increase of nitrogen emission is expected in the province Zuid-Holland of the Netherlands. When this is taken up by the vegetation and soil in the dunes, encroachment by shrubs will dominate. This will lead to a decrease in species diversity of dune habitats in protected areas of the Natura 2000 network, which is an unfavourable situation conforming to the Nature Protection Law (Wet Natuurbescherming). The main loss is expected to be in grey dunes and humid dune slacks of the Natura 2000 areas nearby Maasvlakte 2. This research focuses on the classification of grey dunes, white dunes, humid dune slacks, woody vegetation, water and their sub-classes in the Natura 2000 areas Solleveld & Kapittelduinen, Voornes Duin and Goeree & Kwade Hoek. Remote sensing in combination with machine learning have been proven to be useful in the monitoring of dune habitats. Open access satellite data provide high temporal and geographic coverage, but are often limited in their spatial resolution. Contrarily, high spatial resolution products are limited in their spectral resolution. In this thesis, the effects of spatial resolution, spectral features and feature combinations on the classification of dune habitats using aerial images and a Landsat-7 image (2012) are analysed. The classifications are performed with a Random Forest classifier, using both a pixel-based and region-based approach. In contrast to the traditional pixel-based method, the region-based method incorporates spectral and statistical information of neighbouring pixels. The best performance is achieved with the region-based approach on a 4m resolution aerial image with an accuracy of 87%. The 10m region-based classification is the second best with an accuracy of 83%. The misclassified pixels are mostly classified as the other sub-class of the same main-class. The region-based method should not be applied to satellite images with low spatial resolution as it causes more loss of detail. More spectral bands contribute to a better classification performance, but they contribute less than the spectral bands that were already used. Also, Landsat-7 images were used to create yearly habitat maps for 2012 and 2019 to subsequently analyse the habitat changes between those years, although the accuracies of the Landsat-7 maps are an issue. The main changes in habitat are found in the woody vegetation (shrubs, trees) and humid dune slacks. The woody vegetation decreased in area, causing an increase in grey dunes area in Voornes Duin and Goeree & Kwade Hoek, whereas the opposite has happened in Solleveld & Kapittelduinen. Loss of area in humid dune slacks is detected in all three regions of interest. The local dune manager confirmed that the detected changes in woody vegetation are mostly correct and caused by dune management activities. The perceived changes in humid dune slacks are less reliable. Finally, a modified training dataset is used to classify Sentinel-2 images in 2019. These results compared reasonably well with the few locations we visited during our fieldwork campaign.