Skip to main content


Log in

Multisource multibeam backscatter data: developing a strategy for the production of benthic habitat maps using semi-automated seafloor classification methods

  • Original Research Paper
  • Published:
Marine Geophysical Research Aims and scope Submit manuscript


The establishment of multibeam echosounders (MBES) as a mainstream tool in ocean mapping has facilitated integrative approaches towards nautical charting, benthic habitat mapping, and seafloor geotechnical surveys. The bathymetric and backscatter information generated by MBES enables marine scientists to present highly accurate bathymetric data with a spatial resolution closely matching that of terrestrial mapping, and can generate customized thematic seafloor maps to meet multiple ocean management needs. However, when a variety of MBES systems are used, the creation of objective habitat maps can be hindered by the lack of backscatter calibration, due for example, to system-specific settings, yielding relative rather than absolute values. Here, we describe an approach using object-based image analysis to combine 4 non-overlapping and uncalibrated (backscatter) MBES coverages to form a seamless habitat map on St. Anns Bank (Atlantic Canada), a marine protected area hosting a diversity of benthic habitats. The benthoscape map was produced by analysing each coverage independently with supervised classification (k-nearest neighbor) of image-objects based on a common suite of 7 benthoscapes (determined with 4214 ground-truthing photographs at 61 stations, and characterized with backscatter, bathymetry, and bathymetric position index). Manual re-classification based on uncertainty in membership values to individual classes—especially at the boundaries between coverages—was used to build the final benthoscape map. Given the costs and scarcity of MBES surveys in offshore marine ecosystems—particularly in large ecosystems in need of adequate conservation strategies, such as in Canadian waters—developing approaches to synthesize multiple datasets to meet management needs is warranted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others


  • Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16. doi:10.1016/j.isprsjprs.2009.06.004

    Article  Google Scholar 

  • Brown CJ, Collier JS (2008) Mapping benthic habitat in regions of gradational substrata: an automated approach utilising geophysical, geological, and biological relationships. Estuar Coast Shelf Sci 78:203–214. doi:10.1016/j.ecss.2007.11.026

    Article  Google Scholar 

  • Brown CJ, Smith SJ, Lawton P, Anderson JT (2011) Benthic habitat mapping: a review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuar Coast Shelf Sci 92:502–520. doi:10.1016/j.ecss.2011.02.007

    Article  Google Scholar 

  • Brown CJ, Sameoto JA, Smith SJ (2012) Multiple methods, maps, and management applications: purpose made seafloor maps in support of ocean management. J Sea Res 72:1–13. doi:10.1016/j.seares.2012.04.009

    Article  Google Scholar 

  • Calvert J, Strong JA, Service M, McGonigle C, Quinn R (2015) An evaluation of supervised and unsupervised classification techniques for marine benthic habitat mapping using multibeam echosounder data. ICES J Mar Sci 72:1498–1513. doi:10.1093/icesjms/fsu223

    Article  Google Scholar 

  • Cogan CB, Todd BJ, Lawton P, Noji TT (2009) The role of marine habitat mapping in ecosystem-based management. ICES J Mar Sci 66:2033–2042. doi:10.1093/icesjms/fsp214

    Article  Google Scholar 

  • Collier JS, Brown CJ (2005) Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments. Mar Geol 214:431–449. doi:10.1016/j.margeo.2004.11.011

    Article  Google Scholar 

  • Copeland A, Edinger E, Devillers R, Bell T, LeBlanc P, Wroblewski J (2013) Marine habitat mapping in support of Marine Protected Area management in a subarctic fjord: Gilbert Bay, Labrador, Canada. J Coast Conserv 17:225–237. doi:10.1007/s11852-011-0172-1

    Article  Google Scholar 

  • DFO (2012) Conservations Priorities, Objectives, and Ecosystem Assessment Approach for the St. Anns Bank Area of Interest (AOI). DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 3012/034

  • Diesing M, Green SL, Stephens D, Lark RM, Stewart HA, Dove D (2014) Mapping seabed sediments: comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Cont Shelf Res 84:107–119. doi:10.1016/j.csr.2014.05.004

    Article  Google Scholar 

  • Drǎguţ L, Tiede D, Levick SR (2010) ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int J Geogr Inf Sci 24:859–871. doi:10.1080/13658810903174803

    Article  Google Scholar 

  • Gordon DC Jr, McKeown DL, Steeves G, Vass WP, Bentham K, Chin-Yee M (2007) Canadian imaging and sampling technology for studying benthic habitat and biological communities. In: Todd BJ, Greene HG (eds) Mapping the seafloor for habitat characterization: Geological Association of Canada, Special Paper 47, pp 29–37

  • Hillman J, Lamarche G, Pallentin A, Pecher I, Gorman A, Schneider von Deimling J (2017) Validation of automated supervised segmentation of multibeam backscatter data from the Chatham Rise, New Zealand. Mar Geophys Res. doi:10.1007/s11001-016-9297-9

    Google Scholar 

  • Hogg OT, Huvenne VA, Griffiths HJ, Dorschel B, Linse K (2016) Landscape mapping at sub-Antarctic South Georgia provides a protocol for underpinning large-scale marine protected areas. Sci Rep 6:33163. doi:10.1038/srep33163

    Article  Google Scholar 

  • Huvenne VAI, Blondel P, Henriet JP (2002) Textural analyses of sidescan sonar imagery from two mound provinces in the Porcupine Seabight. Mar Geol 189:323–341. doi:10.1016/S0025-3227(02)00420-6

    Article  Google Scholar 

  • Ismail K, Huvenne VAI, Masson DG (2015) Objective automated classification technique for marine landscape mapping in submarine canyons. Mar Geol 362:17–32. doi:10.1016/j.margeo.2015.01.006

    Article  Google Scholar 

  • Jordan A, Lawler M, Halley V, Barrett N (2005) Seabed habitat mapping in the Kent Group of islands and its role in marine protected area planning. Aquat Conserv Mar Freshw Ecosyst 15:51–70. doi:10.1002/aqc.657

    Article  Google Scholar 

  • Kenny AJ, Cato I, Desprez M, Fader G, Schüttenhelm RTE, Side J (2003) An overview of seabed-mapping technologies in the context of marine habitat classification. ICES J Mar Sci 60:411–418. doi:10.1016/S1054-3139(03)00006-7

    Article  Google Scholar 

  • Lucieer VL (2008) Object-oriented classification of sidescan sonar data for mapping benthic marine habitats. Int J Remote Sens 29:905–921. doi:10.1080/01431160701311309

    Article  Google Scholar 

  • Lucieer V, Lamarche G (2011) Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand. Cont Shelf Res 31:1236–1247. doi:10.1016/j.csr.2011.04.016

    Article  Google Scholar 

  • Lucieer V, Lucieer A (2009) Fuzzy clustering for seafloor classification. Mar Geol 264:230–241. doi:10.1016/j.margeo.2009.06.006

    Article  Google Scholar 

  • Lucieer V, Hill NA, Barrett NS, Nichol S (2013) Do marine substrates “look” and “sound” the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images. Estuar Coast Shelf Sci 117:94–106. doi:10.1016/j.ecss.2012.11.001

    Article  Google Scholar 

  • Lurton X, Lamarche G (eds) (2015) Backscatter measurements by seafloor-mapping sonars. Guidelines and Recommendations. 200p.

  • McGonigle C, Collier JS (2014) Interlinking backscatter, grain size and benthic community structure. Estuar Coast Shelf Sci 147:123–136. doi:10.1016/j.ecss.2014.05.025

    Article  Google Scholar 

  • McGonigle C, Brown CJ, Quinn R (2010) Operational parameters, data density and benthic ecology: considerations for image-based classification of multibeam backscatter. Mar Geod 33:16–38. doi:10.1080/01490410903530273

    Article  Google Scholar 

  • Ming D, Li J, Wang J, Zhang M (2015) Scale parameter selection by spatial statistics for GeOBIA: using mean-shift based multi-scale segmentation as an example. ISPRS J Photogramm Remote Sens 106:28–41. doi:10.1016/j.isprsjprs.2015.04.010

    Article  Google Scholar 

  • Montereale-Gavazzi G, Madricardo F, Janowski L, Kruss A, Blondel P, Sigovini M, Foglini F (2016) Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats: application to the Venice Lagoon, Italy. Estuar Coast Shelf Sci 170:45–60. doi:10.1016/j.ecss.2015.12.014

    Article  Google Scholar 

  • Neves BM, Du Preez C, Edinger E (2014) Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada. Deep Res Part II Top Stud Oceanogr 99:169–183. doi:10.1016/j.dsr2.2013.05.026

    Article  Google Scholar 

  • Pickrill RA, Kostylev VE (2007) Habitat Mapping and National Seafloor Mapping Strategies in Canada. In: Todd BJ, Greene HG (eds) Mapping the seafloor for habitat characterization: Geological Association of Canada, Special Paper 47, pp 483–495

  • Pickrill RA, Todd BJ (2003) The multiple roles of acoustic mapping in integrated ocean management, Canadian Atlantic continental margin. Ocean Coast Manag 46:601–614. doi:10.1016/S0964-5691(03)00037-1

    Article  Google Scholar 

  • Roff JC, Taylor ME, Laughren J (2003) Geophysical approaches to the classification, delineation and monitoring of marine habitats and their communities. Aquat Conserv Mar Freshw Ecosyst 13:77–90. doi:10.1002/aqc.525

    Article  Google Scholar 

  • Stephens D, Diesing M (2014) A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data. PLoS ONE. doi:10.1371/journal.pone.0093950

    Google Scholar 

  • Wright DJ, Pendleton M, Boulware J, Walbridge S, Gerlt B, Eslinger D, Sampson D, Huntley E (2012) ArcGIS Benthic Terrain Modeler (BTM), v.3.0, Environmental Systems Research Institute, NOAA Coastal Services Center, Massachusetts Office of Coastal Zone Management.

  • Young M, Carr M (2015) Assessment of habitat representation across a network of marine protected areas with implications for the spatial design of monitoring. PLoS ONE 10:1–24. doi:10.1371/journal.pone.0116200

    Google Scholar 

  • Zajac RN, Lewis RS, Poppe LJ et al (2003) Responses of infaunal populations to benthoscape structure and the potential importance of transition zones. Limnol Oceanogr 48:829–842. doi:10.2307/3096584

    Article  Google Scholar 

Download references


The authors would like to thank Derek Fenton, Tanya Koropatnick and other colleagues in the Oceans and Coastal Management Division of Fisheries and Oceans, Canada (DFO) at the Bedford Institute of Oceanography for support and suggestions to this research project. Financial support for the research was through DFO Academic Research Contribution Program entitled Developing Methods for Benthic Habitat Mapping of MPAs in Atlantic Canada (project agreement #F5299-140076), and the NSERC Canadian Healthy Oceans Network and its partners: Department of Fisheries and Oceans Canada and INREST (representing the Port of Sept-Îles and City of Sept-Îles; NETGP 468437-14, Project 1.2.5).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Myriam Lacharité.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lacharité, M., Brown, C.J. & Gazzola, V. Multisource multibeam backscatter data: developing a strategy for the production of benthic habitat maps using semi-automated seafloor classification methods. Mar Geophys Res 39, 307–322 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: