Identifying topographic changes at the beach using multiple years of permanent laser scanning

Abstract

Sandy beach-dune systems make up a large part of coastal areas world wide. Their function as an eco-system as well as a protective barrier for human and natural habitat is under increased threat due to climate change. A thorough understanding of change processes at the sediment surface is essential to facilitate prediction of future development and management strategies to maintain their function. Especially slow and small scale processes happening over several days up to weeks at cm level, such as aeolian sand transport are difficult to identify and analyse. Permanent laser scanning (PLS) is a useful tool in the study and analysis of coastal processes as it captures a data representation of the evolution of the sediment surface over extended periods of time (up to several years) with high detail (at cm-dm level). The PLS data set considered for this study, consists of hourly acquired 3D point clouds representing the surface evolution of a section of the Dutch coast during three years. However, it is challenging to extract concrete information on specific change processes from the large and complex PLS data set. We use multiple hypothesis testing in order to reduce the PLS data set to a so-called inventory of trends, consisting of 12.8 million partial time series with associated rate of change and elevation. The inventory of trends proofs to be a suitable tool to identify natural processes such as storms and aeolian sand transport in our test area in the aeolian zone of a sandy beach-dune system on the Dutch coast. We identify these processes and provide a tool to derive summarising data from the complex PLS data set. We find that all partial time series identified as most likely representing aeolian sand transport, result in 1354 m${}^3$ of sand deposition in our study area over the course of three years. We also show a comparison with transects from JarKus data and find a correlation between anthropogenic activities and erosion in our test area with a correlation coefficient of 0.3.