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Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters
Citation
Ierodiaconou, D and Schimel, ACG and Kennedy, D and Monk, J and Gaylard, G and Young, M and Diesing, M and Rattray, A, Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters, Marine Geophysical Research, 39, (1-2) pp. 271-288. ISSN 0025-3235 (2018) [Refereed Article]
Copyright Statement
Copyright 2018 Springer Science+Business Media B.V.
DOI: doi:10.1007/s11001-017-9338-z
Abstract
Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB Image Analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | multibeam sonar, marine habitat mapping, object based image analysis, random forests |
| Research Division: | Agricultural, Veterinary and Food Sciences |
| Research Group: | Fisheries sciences |
| Research Field: | Aquaculture and fisheries stock assessment |
| Objective Division: | Environmental Management |
| Objective Group: | Marine systems and management |
| Objective Field: | Marine biodiversity |
| UTAS Author: | Monk, J (Dr Jacquomo Monk) |
| ID Code: | 123897 |
| Year Published: | 2018 |
| Web of Science® Times Cited: | 53 |
| Deposited By: | Ecology and Biodiversity |
| Deposited On: | 2018-01-31 |
| Last Modified: | 2018-11-14 |
| Downloads: | 0 |
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