| Title | How to homogeneously map adjacent backscatter datasets at regional scale - a case study from the southern Adriatic Sea (Italy) |
| Download | Download (whole publication) |
| |
| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Prampolini, M; Foglini, F; Angeletti, L; Campiani, E; Grande, V; Mercorella, A |
| Source | Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B J ; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 95, https://doi.org/10.4095/305913  Open Access |
| Links | GeoHab 2017
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| Year | 2017 |
| Publisher | Natural Resources Canada |
| Meeting | 2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017 |
| Document | open file |
| Lang. | English |
| Media | on-line; digital |
| Related | This publication is contained in Program and abstracts: 2017
GeoHab Conference, Dartmouth, Nova Scotia, Canada |
| File format | pdf |
| Area | Adriatic Sea; Italy |
| Lat/Long WENS | 13.5000 21.0000 43.0000 39.7500 |
| Subjects | marine 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 |
| Program | Offshore Geoscience |
| Released | 2017 09 26 |
| Abstract | Benthic 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. |
| GEOSCAN ID | 305913 |
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