In this work, multibeam echosounder (MBES) and dual frequency sidescan sonar (SSS) data are combined to map the shallow (5–100 m) benthic habitats of the National Marine Park of Zakynthos (NMPZ), Greece, a Marine Protected Area (MPA). NMPZ hosts extensive prairies of the protected Mediterranean phanerogams Posidonia oceanica and Cymodocea nodosa, as well as reefs and sandbanks. Seafloor characterization is achieved using the multi-frequency acoustic backscatter of: (a) the two simultaneous frequencies of the SSS (100 and 400 kHz) and (b) the MBES (180 kHz), as well as the MBES bathymetry. Overall, these high-resolution datasets cover an area of 84 km² with ground coverage varying from 50% to 100%. Image texture, terrain and backscatter angular response analyses are applied to the above, to extract a range of statistical features. Those have different spatial densities and so they are combined through an object-based approach based on the full-coverage 100-kHz SSS mosaic. Supervised classification is applied to data models composed of operationally meaningful combinations between the above features, reflecting single-sonar or multi-sonar mapping scenarios. Classification results are validated against a detailed expert interpretation habitat map making use of extensive ground-truth data. The relative gain of one system or one feature extraction method or another are thoroughly examined. The frequency-dependent separation of benthic habitats showcases the potentials of multi-frequency backscatter and bathymetry from different sonars, improving evidence-based interpretations of shallow benthic habitats.

In the present paper, a methodological scheme, bringing together common Acoustic Seabed Classification (ASC) systems and a powerful data decomposition approach, called Independent Component Analysis (ICA), is demonstrated regarding its suitability for detecting small targets in Side Scan Sonar imagery. Traditional ASC systems extract numerous texture descriptors, leading to a large feature vector, the dimensionality of which is reduced by means of data decomposition techniques, usually Principal Component Analysis (PCA), prior to classification. However, in the target detection issue, data decomposition should point towards finding components that represent sub-ordinary image information (i.e., small targets) rather than a dominant one. ICA has long been proved to be suitable for separating targets from a background, and this study represents a novel exhibition of its applicability to Side Scan Sonar (SSS) images. The present study attempts to build a fully automated target detection approach that combines image based feature extraction, ICA, and unsupervised classification. The suitability of the proposed approach has been demonstrated using an SSS data-set containing more than 70 manmade targets, most of them metallic, validated through a marine magnetic survey or ground truthing inspection. The method exhibited very good performance as it was able to detect more than 77% of the targets and it produced less than seven false alarms per km². Moreover, it was compared to cases where, in the exact same methodological scheme, no decomposition technique is used, or PCA is employed instead of ICA, achieving the highest detection rate, but, more importantly, producing more than six times less false alarms, thus proving that ICA successfully manages to maximize target to background separation.

Bottom trawl footprints are a prominent environmental impact of deep-sea fishery that was revealed through the evolution of underwater remote sensing technologies. Image processing techniques have been widely applied in acoustic remote sensing, but accurate trawl-mark (TM) detection is underdeveloped. The paper presents a new algorithm for the automatic detection and spatial quantification of TMs that is implemented on sidescan sonar (SSS) images of a fishing ground from the Gulf of Patras in the Eastern Mediterranean Sea. This method inspects any structure of the local seafloor in an environmentally adaptive procedure, in order to overcome the predicament of analyzing noisy and complex SSS images of the seafloor. The initial preprocessing stage deals with radiometric inconsistencies. Then, multiplex filters in the spatial domain are performed with multiscale rotated Haar-like features through integral images that locate the TM-like forms and additionally discriminate the textural characteristics of the seafloor. The final TMs are selected according to their geometric and background environment features, and the algorithm successfully produces a set of trawling-ground quantification values that could be established as a baseline measure for the status assessment of a fishing ground.

Amvrakikos Gulf is a Mediterranean landlocked, fjord-like embayment and marine protected area suffering from natural, human-induced hypoxia/anoxia and massive fish mortality events. Seasonal marine geophysical and oceanographic surveys were conducted focusing on the water-circulation patterns at the sill and the spatial-seasonal distribution of dissolved oxygen (DO) in the gulf. Detailed surveys at the sill, the only communication route between the gulf and the open sea, revealed a two-layer water circulation pattern (top brackish outflow–bottom seawater inflow) and the role of the tide in the daily water exchange. Statistical analysis of the known natural drivers of DO distribution (density difference between the Ionian Sea and Amvrakikos, river inflow, wind) revealed that horizontal density gradients strongly affect anoxia reduction and seafloor oxygenation, while river inflow and wind mainly oxygenate volume/areas located above or within the pycnocline range, with DO concentrations > 2 mg/L. Complex geomorphology with well-formed internal basins contributes to the development and preservation of low DO conditions below the pycnocline. Finally, 43% of the seafloor and 36% of the gulf’s total water volume are permanently hypoxic, and reach a maximum of 70% and 62%, respectively, in September and July. This work is tailored to future ecosystem management plans, decisions, and future research on coastal ecosystems.

Detailed multi-beam bathymetry, sub-bottom acoustic, and side-scan sonar observations of submerged canyons with tufa barriers were used to characterize the Zrmanja River karst estuary on the eastern Adriatic coast, Croatia. This unique karst environment consists of two submerged karst basins (Novigrad Sea and Karin Sea) that are connected with river canyons named Novsko Ždrilo and Karinsko Ždrilo. The combined use of high-resolution geophysical data with legacy topography and bathymetry data in a GIS environment allowed for the description and interpretation of this geomorphological setting in relation to the Holocene sea-level rise. The tufa barriers had a predominant influence on the Holocene flooding dynamics of the canyons and karst basins. Here, we describe the possible river pathways from the basins during the lowstand and the formation of a lengthening estuary during the Holocene sea-level rise. Based on the analyzed morphologies and the relative sea-level curve for the Adriatic Sea, the flooding of the Novsko Ždrilo occurred 9200 years before present (BP) and Karinsko Ždrilo was flooded after 8400 years BP. The combination of high-resolution geophysical methods gave an accurate representation of the karst estuarine seafloor and the flooding of semi-isolated basins due to sea-level rise.