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TitleHow to homogeneously map adjacent backscatter datasets at regional scale - a case study from the southern Adriatic Sea (Italy)
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LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorPrampolini, M; Foglini, F; Angeletti, L; Campiani, E; Grande, V; Mercorella, A
SourceProgram and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B JORCID logo; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 95, Open Access logo Open Access
LinksGeoHab 2017
PublisherNatural Resources Canada
Meeting2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017
Documentopen file
Mediaon-line; digital
RelatedThis publication is contained in Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada
File formatpdf
AreaAdriatic Sea; Italy
Lat/Long WENS 13.5000 21.0000 43.0000 39.7500
Subjectsmarine geology; surficial geology/geomorphology; environmental geology; geophysics; mapping techniques; oceanography; marine environments; coastal studies; conservation; marine organisms; marine ecology; resource management; biological communities; environmental studies; ecosystems; benthos; geophysical interpretations; acoustic surveys, marine; sonar surveys; side-scan sonar; statistical methods; bathymetry; seafloor topography; Biology; Methodology
ProgramOffshore Geoscience
Released2017 09 26
AbstractBenthic habitat mapping is a field of marine research highly developed in a small time-span. Classification of backscatter data constitutes a key approach, and one of the most widespread approaches, for seabed and benthic habitat mapping and a large number of methodologies are described in literature. The first method used is the visual interpretation of backscatter imagery, but it is subjective and time-consuming. Then, taking inspiration from the terrestrial remote sensing, automatic classifications have been developed based both on signal (e.g. ARA) and/or image analysis (e.g. TexAn, Principal Component Analysis, Neural Network). Image analysis is the most applied approach, both for supervised and unsupervised classification because it describes large-scale organizations of seafloor substrate and benthic habitats better than backscatter signal analysis. However, any type of image segmentation based on pixels as units of analysis may lead to some disadvantages such as noisy results, uni-scale approach, texture considerations, context and shape and, finally, pixels are not true geographical objects. For this reason Object-Based Image Analysis (OBIA) is getting more and more success since it is devoted to segment the backscatter image in "meaningful image objects" and should be able to overcome the differences among backscatter datasets acquired with different instruments.
In the last ten years, a large amount of high resolution bathymetry, backscatter data and seafloor samples have been acquired in the Southern Adriatic Sea (Italy), a physiographically complex basin hosting a variety of benthic habitats. The latter constitutes an ideal laboratory for integrated methodologies aiming at habitat mapping at different scales, in different seafloor settings and including heterogeneous datasets. The most challenging aspect of benthic habitat mapping is given by the necessity to produce an integrated map that could unify different datasets, showing comparable results. Within this framework, we present the classification of the backscatter data of some key areas of the Adriatic seafloor: we chose to apply the OBIA classification since it could be the most suitable approach in order to overcome the differences in backscatter intensity and imagery due the use of different devices.
Summary(Plain Language Summary, not published)
The sixteenth annual GeoHab Conference was held this year (2017) at the Waterfront Campus of the Nova Scotia Community College in Dartmouth, Nova Scotia, Canada.

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