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Assessing the use of harmonized multisource backscatter data for thematic benthic habitat mapping


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Misiuk, B, Lacharite, M ORCID: 0000-0001-7395-5236 and Brown, CJ 2021 , 'Assessing the use of harmonized multisource backscatter data for thematic benthic habitat mapping' , Science of Remote Sensing, vol. 3 , pp. 1-11 , doi: 10.1016/j.srs.2021.100015.

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Legacy seabed mapping datasets are increasingly common as the need for detailed seabed information is recognized. Acoustic backscatter data from multibeam echosounders can be a useful surrogate for seabed properties and are commonly used for benthic habitat mapping. Legacy backscatter data, however, are often uncalibrated, rendering measurements relative to a given survey and complicating the use of multisource acoustic datasets for habitat mapping. Recently, ‘bulk shift’ methods have been proposed to harmonize multisource backscatter layers that overlap spatially, but their application to benthic habitat mapping has not been evaluated. Here, four relative backscatter datasets at the St. Anns Bank Marine Protected Area were harmonized to produce a single continuous surface spanning the extent of available bathymetric data. The harmonized surface was used as a predictor in a benthic habitat (‘benthoscape’) classification, which was compared to previous results using individual backscatter coverages. Results were similar to those obtained previously, but the harmonized surface provided increased class discrimination, fewer unclassified areas, and predictions that cross dataset boundaries – eliminating the need for manual reclassification by the user. While this generally increases the efficiency and repeatability of the analysis and the useability of the data, we caution that an inappropriate harmonization model is a potential source of error for the classification.

Item Type: Article
Authors/Creators:Misiuk, B and Lacharite, M and Brown, CJ
Keywords: multibeam, seafloor mapping, benthic habitat mapping, multibeam echosounder, backscatter, seabed mapping, benthoscape, object-based image analysis
Journal or Publication Title: Science of Remote Sensing
Publisher: Elsevier Science Bv
ISSN: 2666-0172
DOI / ID Number: 10.1016/j.srs.2021.100015
Copyright Information:

© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (

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