Next Article in Journal
Geology and Isotope Systematics of the Jianchaling Au Deposit, Shaanxi Province, China: Implications for Mineral Genesis
Next Article in Special Issue
Geomorphometric Characterization of Pockmarks by Using a GIS-Based Semi-Automated Toolbox
Previous Article in Journal
Formation MicroScanner Providing Better Answers for Carbonate Secondary Porosity in Alamein Dolomite Formation, NW Desert, Egypt
Previous Article in Special Issue
The Impact of Acoustic Imaging Geometry on the Fidelity of Seabed Bathymetric Models
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Geosciences 2018, 8(4), 119; https://doi.org/10.3390/geosciences8040119

Multiscale and Hierarchical Classification for Benthic Habitat Mapping

Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, P.O. Box 423, Warrnambool, VIC 3280, Australia
*
Author to whom correspondence should be addressed.
Received: 28 February 2018 / Revised: 29 March 2018 / Accepted: 30 March 2018 / Published: 2 April 2018
(This article belongs to the Special Issue Marine Geomorphometry)
Full-Text   |   PDF [38054 KB, uploaded 3 May 2018]   |  

Abstract

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-km2 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. View Full-Text
Keywords: Multibeam bathymetry; benthic habitat mapping; multiscale; Random Forests Multibeam bathymetry; benthic habitat mapping; multiscale; Random Forests
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Porskamp, P.; Rattray, A.; Young, M.; Ierodiaconou, D. Multiscale and Hierarchical Classification for Benthic Habitat Mapping. Geosciences 2018, 8, 119.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top