2010
DOI: 10.1093/icesjms/fsq130
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Discriminating between Posidonia oceanica meadows and sand substratum using multibeam sonar

Abstract: Di Maida, G., Tomasello, A., Luzzu, F., Scannavino, A., Pirrotta, M., Orestano, C., and Calvo, S. 2011. Discriminating between Posidonia oceanica meadows and sand substratum using multibeam sonar. – ICES Journal of Marine Science, 68: 12–19. High-resolution, multibeam sonar (MBS) (455 kHz) was used to identify two typologies of seabed 8 m deep: Posidonia oceanica meadow and sandy substratum. The results showed that the heterogeneity of the architecture of the P. oceanica canopy and the relatively simple morpho… Show more

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Cited by 32 publications
(29 citation statements)
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“…Depth range is defined as the distance between the shallowest and the deepest beam depths within a grid cell. Because Zostera seagrass shoots and leaves reflect beams from the NMBS, depth range is used as a proxy for difference between seagrass height and bottom [47] (Figure 4). When Zostera seagrass grows luxuriantly, seagrass becomes high.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Depth range is defined as the distance between the shallowest and the deepest beam depths within a grid cell. Because Zostera seagrass shoots and leaves reflect beams from the NMBS, depth range is used as a proxy for difference between seagrass height and bottom [47] (Figure 4). When Zostera seagrass grows luxuriantly, seagrass becomes high.…”
Section: Discussionmentioning
confidence: 99%
“…In flat bottom areas, both the 95% CL and the depth range showed good correspondence with the relative abundance of seagrass. Since Di Maida et al [47] suggested that the depth range is highly influenced by the seagrass shoot length of Posidonia oceanica L., it is natural that depth range reflects seagrass distributions. This study shows that 95% CL is also influenced by shoot length and density within a grid.…”
Section: Discussionmentioning
confidence: 99%
“…Ierodiaconou et al (2007) applied automated decision trees on backscatter and bathymetry derivatives. Di Maida et al (2011) exploited statistical decision trees to discriminate seagrass meadows. Blondel et al (2015) used unsupervised k-means clustering on GLCM homogeneity and energy.…”
Section: Data Classification Methodsmentioning
confidence: 99%
“…The acoustic data enable to distinguish various types of sediment grain size, bathymetric relief and water depth. Acoustic signature diversity is successfully capturing a high variety of seabed types based on sediment grain size (Freitas et al, 2003;Han et al, 2011;Maida et al, 2011). It means that, the results from the acoustic sediment analyses are related mainly to the grain size distribution.…”
Section: Figurementioning
confidence: 99%