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Harmonizing multi-source sonar backscatter datasets for seabed mapping using bulk shift approaches

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Misiuk, B, Brown, CJ, Robert, K and Lacharite, M ORCID: 0000-0001-7395-5236 2020 , 'Harmonizing multi-source sonar backscatter datasets for seabed mapping using bulk shift approaches' , Remote Sensing, vol. 12, no. 4 , pp. 1-36 , doi: 10.3390/rs12040601.

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Abstract

The development of multibeam echosounders (MBES) as a seabed mapping tool has resulted in the widespread uptake of backscatter intensity as an indicator of seabed substrate properties. Though increasingly common, the lack of standard calibration and the characteristics of individual sonars generally produce backscatter measurements that are relative to a given survey, presenting major challenges for seabed mapping in areas that comprise multiple MBES surveys. Here, we explore methods for backscatter dataset harmonization that leverage areas of mutual overlap between surveys for relative statistical calibration—referred to as “bulk shift” approaches. We use three multispectral MBES datasets to simulate the harmonization of backscatter collected over multiple years, and using multiple operating frequencies. Results suggest that relatively simple statistical models are adequate for bulk shift harmonization procedures, and that more flexible approaches may produce inconsistent results that risk statistical overfitting. While harmonizing datasets collected using the same operating frequency from separate surveys is generally feasible given reasonable temporal limitations, results suggest that the success at harmonizing datasets of different operating frequencies partly depends on the extent to which the frequencies differ. We recommend approaches and diagnostics for ensuring the quality of harmonized backscatter mosaics, and provide an R function for implementing the methods presented here.

Item Type: Article
Authors/Creators:Misiuk, B and Brown, CJ and Robert, K and Lacharite, M
Keywords: multibeam, backscatter, seabed mapping, benthos, habitat mapping
Journal or Publication Title: Remote Sensing
Publisher: MDPIAG
ISSN: 2072-4292
DOI / ID Number: 10.3390/rs12040601
Copyright Information:

Copyright 2020 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

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