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A multi-method approach for benthic habitat mapping of shallow coastal areas with high-resolution multibeam data


Reference:

Micallef, A., LeBas, T. P., Huvenne, V. A. I., Blondel, P., Huenerbach, V. and Deidun, A., 2012. A multi-method approach for benthic habitat mapping of shallow coastal areas with high-resolution multibeam data. Continental Shelf Research, 39-40, pp. 14-26.

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    Official URL:

    http://dx.doi.org/10.1016/j.csr.2012.03.008

    Abstract

    The coastal waters of the Maltese Islands, central Mediterranean Sea, sustain a diversity of marine habitats and support a wide range of human activities. The islands’ shallow waters are characterised by a paucity of hydrographic and marine geo-environmental data, which is problematic in view of the requirements of the Maltese Islands to assess the state of their coastal waters by 2012 as part of the EU Marine Strategy Directive. Multibeam echosounder (MBES) systems are today recognised as one of the most effective tools to map the seafloor, although the quantitative characterisation of MBES data for seafloor and habitat mapping is still an underdeveloped field. The purpose of this study is to outline a semi-automated, Geographic Information System-based methodology to map the distribution of habitats in shallow coastal waters using high-resolution MBES data. What distinguishes our methodology from those proposed in previous studies is the combination of a suite of geomorphometric and textural analytical techniques to map specific types of seafloor morphologies and compositions; the selection of the techniques is based on identifying which geophysical parameter would be influenced by the seabed type under consideration. We tested our approach in a 28 km2 area of Maltese coastal waters. Three data sets were collected from this study area: (i) MBES bathymetry and backscatter data; (ii) Remotely Operated Vehicle imagery and (iii) photographs and sediment samples from dive surveys. The seabed was classified into five elementary morphological zones and features - flat and sloping zones, crests, depressions and breaks of slope – using morphometric derivatives, the Bathymetric Position Index and geomorphometric mapping. Segmentation of the study area into seagrass-covered and unvegetated seafloor was based on roughness estimation. Further subdivision of these classes into the four predominant types of composition - medium sand, maërl associated with sand and gravel, seagrass settled on sand and gravel, and seagrass settled on bedrock – was carried out through supervised classifications of morphometric derivatives of the bathymetry and textural indices of backscatter, based on information from training stations. The resulting morphologic and seabed composition maps were combined to plot the distribution of the predominant habitats in the coastal waters offshore NE Malta, some of which are of high conservation value. Ground-truthing of the habitat map using ROV imagery and dive observations confirms that our approach produces a simplified and accurate representation of seafloor habitats while using all the information available within the MBES data sets.

    Details

    Item Type Articles
    CreatorsMicallef, A., LeBas, T. P., Huvenne, V. A. I., Blondel, P., Huenerbach, V. and Deidun, A.
    DOI10.1016/j.csr.2012.03.008
    DepartmentsFaculty of Science > Physics
    Research CentresCentre for Space, Atmospheric and Oceanic Science
    Institute for Sustainable Energy and the Environment
    Publisher StatementCSR-S-11-00338.pdf: NOTICE: this is the author’s version of a work that was accepted for publication in Continental Shelf Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Continental Shelf Research, vol 39-40, 2012, DOI: 10.1016/j.csr.2012.03.008
    RefereedYes
    StatusPublished
    ID Code29249

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