Feasibility of Objective Seabed Mapping Techniques in a Coastal Tidal Environment (Wadden Sea, Germany)

The growing interest in monitoring the marine environment has strongly encouraged governmental agencies and research institutes to undertake seabed mapping programs and stimulated scientific interest in innovative mapping methods and tools. In this study, object-based image analysis was used to map a very shallow tidal inlet, characterized by high sediment variability and intense morphodynamic processes. The aim was to test the feasibility of reproducible mapping approaches within extended mapping programs of complex coastal areas. The study is based on full-coverage, high-resolution bathymetry and reflectivity, calibrated by means of sediment samples. Seafloor segmentation and classification were based on a cluster analysis performed on reflectivity, slope, and ruggedness. Statistics of clusters were extracted and analysed to identify the optimal number of clusters and evaluate the suitability of the clustering process to differentiate different seabed types. Clusters and samples data were joined to create a training and validation dataset for characterizing the seabed and carrying out an accuracy assessment. Misclassifications were explored and referred to three main reasons: (i) The not-perfect correspondence between sediment boundaries of classification systems and boundaries derived from the clustering process; (ii) the geomorphological features of the seabed; and (iii) the position accuracy of samples. The study contributes to testing of the feasibility of objective methods and highlights the importance of joining acoustic, lithological, and geomorphological analysis. It highlights issues and the need to critically analyse the mapping results and improve the accuracy of collected data.

Automated summary

The study utilized object-based image analysis to map a shallow tidal inlet, aiming to test the feasibility of reproducible mapping approaches in complex coastal areas. By focusing on seafloor segmentation and classification, the research aimed to improve mapping accuracy through the analysis of clusters and sediment samples.