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Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping

Che Hasan, Rozaimi, Ierodiaconou, Daniel, Laurenson, Laurie and Schimel, Alexandre 2014, Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping, PLoS One, vol. 9, no. 5, Article Number : e97339, pp. 1-14, doi: 10.1371/journal.pone.0097339.

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Title Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping
Author(s) Che Hasan, Rozaimi
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Laurenson, LaurieORCID iD for Laurenson, Laurie orcid.org/0000-0003-2321-7512
Schimel, Alexandre
Journal name PLoS One
Volume number 9
Issue number 5
Season Article Number : e97339
Start page 1
End page 14
Total pages 14
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2014
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
IMAGE CLASSIFICATION
CONTINENTAL-SHELF
SPECIES RICHNESS
ECHOSOUNDER DATA
SONAR IMAGERY
AUSTRALIA
FEATURES
VIDEO
ECOSYSTEMS
ACCURACY
Summary Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.
Language eng
DOI 10.1371/journal.pone.0097339
Field of Research 060205 Marine and Estuarine Ecology (incl Marine Ichthyology)
Socio Economic Objective 960507 Ecosystem Assessment and Management of Marine Environments
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Public Library of Science
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070103

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.