Assessing alongshore variability of intertidal channels at Noordwijk by means of LiDAR measurements

Abstract

In order to understand the long term behavior of intertidal ridge-runnel systems high resolution spatial and temporal data covering extended periods of time is needed. Such datasets are still rare but are becoming more available through the usage of remote sensing technologies such as Argus and Terrestrial Laser Scanning. A three month period (Feb – April 2020) with data obtained by laser scanning at the beach of Noordwijk, the Netherlands, has been analysed to evaluate the longshore variability of rip channels. During this period varying weather conditions occurred. The timeseries consist out of snapshots taken around the occurrence of the lowest tidal water level each day, containing elevation data of an 1km alongshore section of the beach. Each scan in this timeseries is processed in order to remove noise, objects and correct for time dependent rotations. The spatial gaps in the scans present in the data as the result of flowing or standing water are interpolated using a 2D grid interpolation method. The analysis is done using two different methods: a 1D-method using longshore elevation transacts, and a more advanced 4D-Object-by-Change based upon elevation changes over time. The 1D-method detected 44 rip channels that existed for multiple days, with an average alongshore migration rate of 1.55 m/day. The average spacing between rip channels was found to be 131 meters. The 4D-OBC detected less rip channel when compared to the 1D-method but did capture other morphological features such a runnels. A comparison of the alongshore migration rates and the dominant wave and wind directions that during this period showed a correlation between wind and wave direction and the direction of the alongshore migration direction, however some exceptions are visible. Comparing this research to similar research and literature did show differences in the migration rates and rip spacing. These are explained by the different nature of both datasets (2D vs 3D) and methodology used in the detection of rip channels.