As extensive efforts from consumer drone manufacturers resulted in inexpensive aircrafts that can capture high quality video imagery, drones are increasingly considered to be beneficial for scientific purposes. In the recent past, video imagery has been used to analyze waves in terms of several hydrodynamic parameters and to indicate matching coastal features. Whereas these measurements have been acquired using static cameras mounted on large poles situated at beaches, this report exploits a recently developed method using Unmanned Aerial Vehicles (UAVs) as a means to map coastal morphology. After recording aerial imagery in combination with several Ground Control Points (GCPs), several time series of georectified coastal images are compiled. Subsequently, for all of the points in a predefined grid, pixel intensities are stored throughout the time of the recording. Consequently, hydrodynamic data like wave celerity and phase are estimated, which in turn are used to invert water depths for every location in a predetermined area of interest using an algorithm called cBathy. Using reference measurements with an accuracy in the order of five centimeters, this report benchmarks the bathymetry as computed using the drone imagery by calculating the root mean squared error, the root mean squared error divided by the water depth and the mean error of three different sub areas within the area of interest. After calibrating parameters as used by the cBathy algorithm, it is shown that the best computation yielded a bathymetry with a root mean squared error of 0.37 meters for a total area of approximately 2500 square meters. It is also shown that the other datasets yield errors of approximately twice the error of the best dataset. It is shown that the total error can to a large extent be attributed to errors in the rectification part of the algorithm. The large errors and the large discrepancy between the errors make the method currently unsuitable for coastal monitoring purposes. Hence, before the UAV bathymetry mapping method is to replace traditional methods, more research should focus on standardizing the process and thereby decrease the variance between the errors of different datasets.

The Netherlands is a country primarily developed on coastal zones. Coastal zones are very important, since they are a source of valuable living and non-living resources for its population, and they also act as a natural protection against floods. Notably, the natural dynamics driven by the sea and the man-made actions on the land change them continuously. Consequently, the need for systematic coastal protection and management arises, for which coastal monitoring is of paramount necessity. The emergence of remote sensing in this field has been proven to have many advantages, especially due to the large coverage that it offers in a time- and cost-efficient way. Thus, the subject of this study is one of these remote sensing techniques, specifically the UAV Laser Scanning. UAV Laser Scanning has been introduced in the field of coastal monitoring quite recently, yet it has already proven to be a useful tool, outperforming the other methods used for the same purpose in a number of aspects. Its high accuracy and resolution coupled with flexibility and time efficiency make it a very promising technique. It works on the same principle as all the other laser scanning techniques. Its system components are a platform (drone), a laser scanner, a GNSS receiver and an Inertial Measurement Unit (IMU). The aerial system used in this study Alpha AL3-32, by Phoenix LiDAR Systems, in combination with a DJI Spreading Wings S1000 Octocopter. One of the main aims of this study was to investigate the error budget of that system and its impact on the positioning accuracy. The sources of error in such a system are its three main components as well as possible misalignments between these components, that cause bore-sight and lever-arm errors, coordinate system transformations and temporal interpolation issues. Herein, an error model was used for the determination of the quantitative impact of them on the positioning accuracy. Using the uncertainties of the instruments that Alpha AL3-32 consists of, the a priori positioning accuracy of the system was estimated equal to 2 cm. In addition, its dependency on the flight parameters was proven. The a posteriori positioning accuracy was derived by investigating a small flat dataset of the UAV Laser Scanning point cloud and was estimated to be 2 cm as well. From the same dataset, the precision was also computed as being equal to 4 cm. When it comes to the products of this technique, one of the main methods to analyse the morphological changes in coastal zones is by comparing Digital Terrain Models (DTMs) from different epochs. Thus, the generation of accurate DTMs was the other important objective of this study. The standard procedure includes preparation of the dataset, noise removal and above ground analysis. After the ground-only dataset was derived, it was converted into a grid using interpolation, namely the Moving Average or Moving Planes method. The grid size of the DTM can go down to 5 cm. In addition, the RMSE between the UAV Laser Scanning only-ground point cloud and the derived DTM was computed equal to 4 cm, while the DTM accuracy in height was estimated equal to 8 cm by using an empirical formula based on the point density and the slope of the terrain. Considering the 3D coordinate accuracy that can be achieved with such a system as well as the DTM quality, UAV Laser Scanning was found to be an appropriate technique for coastal monitoring, presenting important advantages over other competing methods. It can reduce the time and cost of surveys at the coastal zones and at the same time maintain high resolution with its high point density.