Abstract
A huge amount of seabed acoustic reflectivity data has been acquired from the east to the west side of the southern Adriatic Sea (Mediterranean Sea) in the last 18 years by CNR-ISMAR. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of them has never been carried out. Here, we aimed at coherently interpreting acoustic data images of the seafloor to produce a benthic habitat map of the southern Adriatic Sea showing the spatial distribution of substrates and biological communities within the basin. The methodology here applied consists of a semi-automated classification of acoustic reflectivity, bathymetry and bathymetric derivatives images through object-based image analysis (OBIA) performed by using the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each cruise dataset separately, then classified and validated through comparison with bottom samples, images, and prior knowledge of the study areas.
Highlights
Acoustic reflectivity data are a proxy to the type and grain size of the seabed
Remote Sensing-OBIA (RSOBIA) segmentation was run setting the Minimum Object Size at 1000 for all datasets: this value corresponds approximately to a circle of 18 pixels (180 m) radius which we found suitable for the scale at which we wanted to produce the benthic habitat map
Following the methodology proposed by Lacharité et al [23], we firstly we found suitable for the scale at which we wanted to produce the benthic habitat map included only the primary acoustic data layers to minimize
Summary
Acoustic reflectivity data are a proxy to the type and grain size of the seabed. They allow distinction between different substrates [1,2] and identification of some morphologies (e.g., small-scale structures) and benthic habitats (seagrass meadows, coralligenous formations, cold-water corals and others) [3]. Combined with the geomorphological study of the seafloor, the analysis of acoustic reflectivity data plays a key role and constitutes one of the most widespread approaches for benthic habitat mapping [4,5]. Different methodologies can be applied to classify seabed reflectivity data, going from experts’ interpretation to automatic or semi-automatic classifications, successively validated through bottom samples and images. Large datasets are more handled through automatic or semi-automatic classifications that are quantitative, repeatable, comparable, more objective and less time-consuming than a manual interpretation [4,5,6,7]
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