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

Citation

Ierodiaconou, D and Monk, J and Rattray, A and Laurenson, L and Versace, VL, Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations, Continental Shelf Research, 31 pp. S28-S38. ISSN 0278-4343 (2011) [Refereed Article]

Copyright Statement

Copyright 2011 Elsevier Ltd.

DOI: doi:10.1016/j.csr.2010.01.012

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:marine habitat mapping, descision trees, model comparison
Research Division:Agricultural and Veterinary Sciences
Research Group:Fisheries Sciences
Research Field:Aquatic Ecosystem Studies and Stock Assessment
Objective Division:Environment
Objective Group:Flora, Fauna and Biodiversity
Objective Field:Marine Flora, Fauna and Biodiversity
Author:Monk, J (Dr Jacquomo Monk)
ID Code:99545
Year Published:2011
Web of Science® Times Cited:71
Deposited By:IMAS Research and Education Centre
Deposited On:2015-03-27
Last Modified:2017-11-04
Downloads:0

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