Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden
Climate change is causing a structural change in Arctic ecosystems, decreasing the effectiveness that the polar regions have in cooling water masses, with inevitable repercussions on the climate and with an impact on marine biodiversity. The Svalbard islands under study are an area greatly influenced by Atlantic waters. This area is undergoing changes that are modifying the composition and distribution of the species present. The aim of this work is to provide a method for the classification of acoustic patterns acquired in the Kongsfjorden, Svalbard, Arctic Circle using multibeam technology. Therefore the general objective is the implementation of a methodology useful for identifying the a…
Benthic foraminiferal response to trace element pollution. The case study of the Gulf of Milazzo, NE Sicily (Central Mediterranean Sea).
The response of benthic foraminiferal assemblages to trace element pollution in the marine sediments of the Gulf of Milazzo (north-eastern Sicily) was investigated. Since the 1960s, this coastal area has been a preferred site for the development of two small marinas and a commercial harbour as well as for heavy industry. Forty samples collected in the uppermost 3-4 cm of an undisturbed layer of sediment in the littoral environment were used for this benthic foraminiferal analysis. The enrichment factors (EFs) of selected trace elements (As, Co, Cr, Cu, Mn, Ni, Pb and Zn) were also calculated. Changes both in benthic foraminiferal assemblages and in some trace elements concentrations have pr…
A pattern recognition approach to identify biological clusters acquired by acoustic multi-beam in Kongsfjorden
The Svalbardsis one of the most intensively studied marine regions in the Artic; here the composition and distribution of marine assemblages are changing under the effect of global change, and marine communities are monitored in order to understand the long-term effects on marine biodiversity. In the present work, acoustic data collected in the Kongsfjorden using multi-beam technology was analyzed to develop a methodology for identifying and classifying 3D acoustic patterns related to fish aggregations. In particular, morphological, energetic and depth features were taken into account to develop a multi-variate classification procedure allowing to discriminate fish species. The results obta…