Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations

Ierodiaconou, D., Monk, J., Rattray, A., Laurenson, L. and Versace, V. L. 2011, Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations, Continental shelf research, vol. 31, no. 2, Supplement 1 : Geological and Biological Mapping and Characterisation of Benthic Marine Environments, pp. S28-S38.

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Title Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations
Author(s) Ierodiaconou, D.
Monk, J.
Rattray, A.
Laurenson, L.
Versace, V. L.
Journal name Continental shelf research
Volume number 31
Issue number 2
Season Supplement 1 : Geological and Biological Mapping and Characterisation of Benthic Marine Environments
Start page S28
End page S38
Total pages 11
Publisher Pergamon
Place of publication Oxford, England
Publication date 2011-02-15
ISSN 0278-4343
1873-6955
Summary

The effective management of our marine ecosystems requires the capability to identify, characterise and predict the distribution of benthic biological communities within the overall seascape architecture. The rapid expansion of seabed mapping studies has seen an increase in the application of automated classification techniques to efficiently map benthic habitats, and the need of techniques to assess confidence of model outputs. We use towed video observations and 11 seafloor complexity variables derived from multibeam echosounder (MBES) bathymetry and backscatter to predict the distribution of 8 dominant benthic biological communities in a 54 km2 site, off the central coast of Victoria, Australia. The same training and evaluation datasets were used to compare the accuracies of a Maximum Likelihood Classifier (MLC) and two new generation decision tree methods, QUEST (Quick Unbiased Efficient Statistical Tree) and CRUISE (Classification Rule with Unbiased Interaction Selection and Estimation), for predicting dominant biological communities. The QUEST classifier produced significantly better results than CRUISE and MLC model runs, with an overall accuracy of 80% (Kappa 0.75). We found that the level of accuracy with the size of training set varies for different algorithms. The QUEST results generally increased in a linear fashion, CRUISE performed well with smaller training data sets, and MLC performed least favourably overall, generating anomalous results with changes to training size. We also demonstrate how predicted habitat maps can provide insights into habitat spatial complexity on the continental shelf. Significant variation between patch-size and habitat types and significant correlations between patch size and depth were also observed.

Notes Available online 29 January 2010.
Language eng
Field of Research 050211 Wildlife and Habitat Management
Socio Economic Objective 960507 Ecosystem Assessment and Management of Marine Environments
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2011
Copyright notice ©2010, Elsevier Ltd. All rights reserved.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032334

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