Predictive Mapping of Seabed Substrata Using High-Resolution Multibeam Data  

Authors / CoAuthors

Huang, Z. | Siwabessy, J. | Nichol, S. | Brooke, B.

Abstract

This study explored the full potential of high-resolution multibeam data for an automatic and accurate mapping of complex seabed under a predictive modelling framework. Despite of the extremely complex distributions of various hard substrata at the inner-shelf of the study area, we achieved a nearly perfect prediction of 'hard vs soft' classification with an AUC close to 1.0. The predictions were also satisfactory for four out of five sediment properties, with R2 values range from 0.55 to 0.73. In general, this study demonstrated that both bathymetry and backscatter information (from the multibeam data) should be fully utilised to maximise the accuracy of seabed mapping. From the modelled relationships between sediment properties and multibeam data, we found that coarser sediment generally generates stronger backscatter return and that deeper water with its low energy favours the deposition of mud content. Sorting was also found to be a better sediment composite property than mean grain size. In addition, the results proved one again the advantages of applying proper feature extraction approaches over original backscatter angular response curves.

Product Type

nonGeographicDataset

eCat Id

75186

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Keywords

• External PublicationArticle

( Theme )

• multibeam

( Theme )

• sedimentology

( Theme )

• mapping

( Theme )

• marine

( Theme )

• NERP

Australian and New Zealand Standard Research Classification (ANZSRC)

• Environmental Science and Management

• Published_Internal

Publication Date

2013-01-01T00:00:00

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Australian Government Security ClassificationSystem    

Classification - unclassified

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Creative Commons Attribution 4.0 International Licence    

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Use - license

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asNeeded

Topic Category

oceans

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The paper was written based on the research results of the CERF Marine Biodiversity Hub Surrogates Program.

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NERF Marine Biodiversity Hub