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.

Mediterranean seagrasses are represented by five species, whose most representative are Posidonia oceanica (L.) Delile and Cymodocea nodosa (Ucria) Ascherson. Spatial data analysis through remote sensing techniques is certainly a useful tool in order to understand and quantify the extent or loss of seagrass areas. Seagrass mapping and monitoring by remote sensing have been established using various optical remote sensors and mapping methods1. In most studies for habitat mapping, the most common satellite images used are Landsat, Ikonos, Quickbird, Pleiades, World View 2 and the recent Sentinel-2 etc. 1-2. The aim of this work is to compare the spatial accuracy of medium – resolution satellite images (Sentinel-2 and Landsat-8 OLI) vs. high-resolution images (MIVIS and WorldView-2) for mapping P. oceanica meadows and evaluating their conservation status. The present study was conducted in 2016 within the MPA “Capo Rizzuto” (Mediterranean Sea - Southern Ionian Sea). Remote sensing images were processed following several stages such as preprocessing phase, segmentation, supervised classification and accuracy classification assessment. Preliminary results highlighted differences in spatial and thematic accuracy between medium and very high spatial resolution images for seagrass habitat mapping.

This study undertook the mineralogical and chemical analysis of anglesite (PbSO₄), cerussite (PbCO₃), gypsum (CaSO₄∙2H₂O), langite (Cu₄(SO₄)(OH)₆∙2H₂O), malachite (Cu₂CO₃(OH)₂), and posnjakite (Cu₄(SO₄)(OH)₆·H₂O) detected for the first time in the abandoned Mastricarro Barite Mine (Catanzaro, Calabria, southern Italy). Geological conditions make this one of the few well-preserved, partly accessible mines in the Calabria region. Numerous mineralogical species, some beautifully crystallized, have been discovered among the alteration products of the sulfides present within the barite veins. The newly identified crystals occur as secondary minerals in the oxidized portions of deposits originally containing lead and copper sulfides; they are widely distributed and are almost always detected as microcrystals. The anglesite, cerussite, gypsum, langite, malachite, and posnjakite crystals were identified and characterized using optical microscopy (OM), micro-Raman spectroscopy (µR), and scanning electron microscopy, combined with energy dispersive spectrometry (SEM/EDS). The new mineral occurrences can be useful for scientific and didactic purposes; further, for langite, malachite, and posnjakite crystals, new Raman bands, which have not been mentioned before in the literature, were identified.

This data article contains mineralogical and chemical data of the wulfenite (PbMoO4) sampled from mine of Fiumarella in Calabria region (Southern Italy). Wulfenite is a rare mineral belonging to the class of minerals called molybdates and if found in large amounts it can be used for the extraction of molybdenum. In the mine of Fiumarella, in addition to primary minerals such as barite, galena, cerussite, anglesite, fluorite and chalcopyrite, wulfenite was also detected. Wulfenite crystals are bipyramidal, few microns in size and grow as single crystals that can reach 1 mm. Methods for obtaining the data sets include optical microscopy, micro X-Ray Fluorescence and micro-Raman spectroscopy.