This paper is the outcome of the 1st Summer School on Geomorphology, Ecology, and Marine Biology in insular environments, focused on the advanced and multidisciplinary methods for the scientific investigation of marine coastal areas. It was held at Tremiti Islands, a significant laboratory for geomorphological, biological, and ecological studies because of its dynamic interaction between geodiversity and biodiversity, which makes it an ideal place for scientific research and geotourism. Landscape information was collected during field trips, while practical activities were finalized to sampling sediments and data collection of hard bottom assemblages. The map is the result of a multidisciplinary analysis incorporating geomorphological field observations and advanced methods applicable for ecological and environmental research, supported by scientific diving. This work represents a useful tool for the dissemination of environmental knowledge of the area and for understanding the relationships between landscape and natural heritage through modern and environmentally aware tourism.

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.

No abstract