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.