In this study, we present a framework for seagrass habitat mapping in shallow (5–50 m) and very shallow water (0–5 m) by combining acoustic, optical data and Object-based Image classification. The combination of satellite multispectral images-acquired from 2017 to 2019, together with Unmanned Aerial Vehicle (UAV) photomosaic maps, high-resolution multibeam bathymetry/backscatter and underwater photogrammetry data, provided insights on the short-term characterization and distribution of Posidonia oceanica (L.) Delile, 1813 meadows in the Calabrian Tyrrhenian Sea. We used a supervised Object-based Image Analysis (OBIA) processing and classification technique to create a high-resolution thematic distribution map of P. oceanica meadows from multibeam bathymetry, backscatter data, drone photogrammetry and multispectral images that can be used as a model for classification of marine and coastal areas. As a part of this work, within the SIC CARLIT project, a field application was carried out in a Site of Community Importance (SCI) on Cirella Island in Calabria (Italy); different multiscale mapping techniques have been performed and integrated: the optical and acoustic data were processed and classified by different OBIA algorithms, i.e., k-Nearest Neighbors’ algorithm (k-NN), Random Tree algorithm (RT) and Decision Tree algorithm (DT). These acoustic and optical data combinations were shown to be a reliable tool to obtain high-resolution thematic maps for the preliminary characterization of seagrass habitats. These thematic maps can be used for time-lapse comparisons aimed to quantify changes in seabed coverage, such as those caused by anthropogenic impacts (e.g., trawl fishing activities and boat anchoring) to assess the blue carbon sinks and might be useful for future seagrass habitats conservation strategies.
Plastic is everywhere—increasing evidence suggests that plastic pollution is ubiquitous and persistent in ecosystems worldwide. Microplastic pollution in marine environments is particularly insidious, as small fragmentation can increase interaction with biota and food chain access. Of particular concern is the Mediterranean Sea, which has become a large area of accumulation of plastic debris, including microplastics, whose polymeric composition is still largely unknown. In this study, we analyzed the polymeric composition, particle size distribution, shape, and color of small plastic particles (ranging from 50 to 5000 µm) collected from the sea surface in six stations at the center of the Mediterranean Sea. We also described, for the first time, the different distribution of microplastics from coastal areas up to 12 nautical miles offshore. The microplastic density was 0.13 ± 0.19 particles/m2, with a marked prevalence of smaller particles (73% < 3 mm) and a peak between 1 and 2 mm (34.74%). Microplastics composition analysis showed that the most abundant material was polyethylene (69%), followed by polypropylene (24%). Moreover, we reported a comparison of the two Calabrian coasts providing the first characterization of a great difference in microplastic concentration between the Tyrrhenian and Ionian sides (87% vs. 13%, respectively), probably due to the complex marine and atmospheric circulation, which make the Tyrrhenian side an area of accumulation of materials originating even from faraway places. We demonstrate, for the first time, a great difference in microplastic concentration between Tyrrhenian and Ionian Calabrian coasts, providing a full characterization and highlighting that microplastic pollution is affected by both local release and hydrography of the areas.
The aim of the present paper is to define the advantage to use innovative techniques based on sperimental tool to supplement the traditional techniques in marine monitoring, through experience of CRSM-ARPACAL (Centro Regionale Strategia Marina) into two regional projects called "SIC Carlit" and "Musmap". Both projects have shown that in the monitoring of coastal marine ecosystems the sperimental techniques to supplement traditional methods can provide more accurate and data with reduced costs and times of work.
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