Marine Geophysical Research

, Volume 39, Issue 1–2, pp 271–288 | Cite as

Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters

  • Daniel IerodiaconouEmail author
  • Alexandre C. G. Schimel
  • David Kennedy
  • Jacquomo Monk
  • Grace Gaylard
  • Mary Young
  • Markus Diesing
  • Alex Rattray
Original Research Paper


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.


Multibeam echosounder Marine habitat mapping Object based image analysis Random forests 



We thank Dr Matt Edmunds from Australian Marine Ecology and Dr Steffan Howe from Parks Victoria for provision of the AUV data collected as part of an invasive pest survey. We thank Sean Blake for assistance with the collection of MBES data aboard Deakin University’s research vessel Yolla. We thank the Port Welshpool Coast Guard for providing accommodation and vessel support during video and sediment surveys. AR and MY were supported by the Victorian Marine Habitat Mapping Program with funds through Department of Environment, Land Water and Planning, Parks Victoria and Australian National Data Services (ANDS) through funding from the Australian Government’s National Environmental Science Programme. JM was supported by the Marine Biodiversity Hub through funding from the Australian Government’s National Environmental Science Programme. This project was funded by Parks Victoria, POZIBLE project Voyages of Discovery and Somers Carroll Productions.

Supplementary material

11001_2017_9338_MOESM1_ESM.docx (1.2 mb)
Supplementary material 1 (DOCX 1254 KB)


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Life and Environmental SciencesDeakin University, Centre for Integrative Ecology (Warrnambool Campus)WarrnamboolAustralia
  2. 2.National Institute of Water and Atmospheric Research (NIWA), Taihoro NukurangiWellingtonNew Zealand
  3. 3.School of GeographyThe University of MelbourneParkvilleAustralia
  4. 4.Institute of Marine and Antarctic StudiesUniversity of TasmaniaHobartAustralia
  5. 5.Geological Survey of NorwayTrondheimNorway
  6. 6.Centre for EnvironmentFisheries and Aquaculture ScienceLowestoftUK

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