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Multiscale and hierarchical classification for benthic habitat mapping

Porskamp, Peter, Rattray, Alexander, Young, Mary and Ierodiaconou, Daniel 2018, Multiscale and hierarchical classification for benthic habitat mapping, Geosciences, vol. 8, no. 4, doi: 10.3390/geosciences8040119.

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Title Multiscale and hierarchical classification for benthic habitat mapping
Author(s) Porskamp, Peter
Rattray, Alexander
Young, MaryORCID iD for Young, Mary orcid.org/0000-0001-7426-2343
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Journal name Geosciences
Volume number 8
Issue number 4
Article ID 119
Total pages 24
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2018-04
ISSN 2076-3263
2076-3263
Summary © 2018 by the authors. Licensee MDPI, Basel, Switzerland. Developing quantitative and objective approaches to integrate multibeam echosounder (MBES) data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for benthic habitat mapping. The scale of predictors within predictive models directly influences habitat distribution maps, therefore matching the scale of predictors to the scale of environmental drivers is key to improving model accuracy. This study uses a multi-scalar and hierarchical classification approach to improve the accuracy of benthic habitat maps. We used a 700-km 2 region surrounding Cape Otway in Southeast Australia with full MBES data coverage to conduct this study. Additionally, over 180 linear kilometers of towed video data collected in this area were classified using a hierarchical classification approach. Using a machine learning approach, Random Forests, we combined MBES bathymetry, backscatter, towed video and wave exposure to model the distribution of biotic classes at three hierarchical levels. Confusion matrix results indicated that greater numbers of classes within the hierarchy led to lower model accuracy. Broader scale predictors were generally favored across all three hierarchical levels. This study demonstrates the benefits of testing predictor scales across multiple hierarchies for benthic habitat characterization.
Language eng
DOI 10.3390/geosciences8040119
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, The authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30108101

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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.