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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baker EK, Harris PT (2012) Habitat mapping and marine management. Seafloor Geomorphol Benthic Habitat 21:23–38. https://doi.org/10.1016/b978-0-12-385140-6.00002-5
Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogrammetry Remote Sens 65:2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Blaschke T et al (2014) Geographic object-based image analysis—towards a new paradigm. ISPRS Isprs J Photogrammetry Remote Sens 87:180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/a:1010933404324
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. https://doi.org/10.1016/j.ecss.2011.02.007
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. https://doi.org/10.1093/icesjms/fsu223
Campbell J (1981) Spatial autocorrelation effects upon the accuracy of supervised classification of land cover. Photogram Eng Remote Sens 47:355–363
Costa BM, Battista TA, Pittman SJ (2009) Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sens Environ 113:1082–1100. https://doi.org/10.1016/j.rse.2009.01.015
Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88:2783–2792. https://doi.org/10.1890/07-0539.1
Dauvin JC, Bellan G, Bellan-Santini D (2008) The need for clear and comparable terminology in benthic ecology. Part II. application of the European directives. Aquat Conserv-Mar Freshw Ecosyst 18:446–456. https://doi.org/10.1002/aqc.864
Devillers R, Pressey RL, Grech A, Kittinger JN, Edgar GJ, Ward T, Watson R (2015) Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection? Aquat Conserv-Mar Freshw Ecosyst 25:480–504. https://doi.org/10.1002/aqc.2445
Diesing M, Stephens D (2015) A multi-model ensemble approach to seabed mapping. J Sea Res 100:62–69. https://doi.org/10.1016/j.seares.2014.10.013
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. https://doi.org/10.1016/j.csr.2014.05.004
Diesing M, Mitchell P, Stephens D (2016) Image-based seabed classification: what can we learn from terrestrial remote sensing? ICES J Mar Sci 73:2425–2441. https://doi.org/10.1093/icesjms/fsw118
Dragut 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
Dragut L, Csillik O, Eisank C, Tiede D (2014) Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J Photogrammetry Remote Sens 88:119–127
Edmunds M, Pritchard K, McArthur M (2012) Victorian Subtidal Reef Monitoring Program: the reef biota at Wilsons Promontory Marine National Park, November 2010 vol 71. Melbourne
Edmunds M, Donnelly D, Brown H (2013) Survey for Marine Invasive Species at Refuge Cove, Wilsons Promontory, May 2013. Report to Parks Victoria
Fonseca L, Calder B (2005) Geocoder: an efficient backscatter map constructor. In: U.S. Hydro 2005 Conference, San Diego, USA, p 9
Fonseca L, Brown C, Calder B, Mayer L, Rzhanov Y (2009) Angular range analysis of acoustic themes from stanton banks Ireland: a link between visual interpretation and multibeam echosounder angular signatures. Appl Acoustics 70:1298–1304. https://doi.org/10.1016/j.apacoust.2008.09.008
Gray J (1997) Marine biodiversity: patterns, threats and conservation needs. Biodivers Conserv 6:153–175
Greene HG et al (1999) A classification scheme for deep seafloor habitats. Oceanol Acta 22:663–678. https://doi.org/10.1016/s0399-1784(00)88957-4
Hammerstad E (2000) EM technical note: backscattering and seabed image reflectivity. Kongsberg Maritime AS, Horten
Hasan RC, Ierodiaconou D, Laurenson L (2012a) Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping. Estuar Coast Shelf Sci 97:1–9. https://doi.org/10.1016/j.ecss.2011.10.004
Hasan RC, Ierodiaconou D, Monk J (2012b) Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar. Remote Sens 4:3427–3443. https://doi.org/10.3390/rs4113427
Hasan RC, Ierodiaconou D, Laurenson L, Schimel A (2014) Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping. PLoS ONE 9 https://doi.org/10.1371/journal.pone.0097339
Hurlbert SH (1984) Pseudoreplication and the design of ecological field experiments. Ecol Monogr 54:187–211
Ierodiaconou D, Burq S, Reston M, Laurenson L (2007) Marine benthic habitat mapping using multibeam data, georeferenced video and image classification techniques in Victoria, Australia. J Spat Sci 52:93–104. https://doi.org/10.1080/14498596.2007.9635105
Ierodiaconou D, Monk J, Rattray A, Laurenson L, Versace VL (2011) Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations. Cont Shelf Res 31:S28–S38. https://doi.org/10.1016/j.csr.2010.01.012
Jackson JBC (2008) Ecological extinction and evolution in the brave new ocean. Proc Natl Acad Sci USA 105:11458–11465. https://doi.org/10.1073/pnas.0802812105
James N, Bone Y (2011) Neritic carbonate sediments in a temperate realm. Springer, New York
Kendall MS, Jensen OP, Alexander C, Field D, McFall G, Bohne R, Monaco ME (2005) Benthic mapping using sonar, video transects, and an innovative approach to accuracy assessment: a characterization of bottom features in the Georgia Bight. J Coast Res 21:1154–1165. https://doi.org/10.2112/03-0101r.1
Kennedy DM, Ierodiaconou D, Schimel AGC (2014) Granitic coastal geomorphology: applying integrated terrestrial and bathymetric LiDAR with multibeam sonar to examine coastal landscape evolution. Earth Surf Proc Land 39:1663–1674. https://doi.org/10.1002/esp.3615
Kostylev VE, Todd BJ, Fader GBJ, Courtney RC, Cameron GDM, Pickrill RA (2001) Benthic habitat mapping on the Scotian Shelf based on multibeam bathymetry, surficial geology and sea floor photographs. Mar Ecol Prog Ser 219:121–137
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26
Lacharité M, Brown C, Gazzola V (2017) Multisource multibeam backscatter data: developing a strategy for the production of benthic habitat maps using semi-automated seafloor classification methods. Mar Gephys Res. https://doi.org/10.1007/s11001-017-9331-6
Lamarche G, Lurton X (2017) Recommendations for improved and coherent acquisitionand processing of backscatter data from seafloor-mapping sonars. Mar Gephys Res. https://doi.org/10.1007/s11001-017-9315-6
Lecours V, Dolan MFJ, Micallef A, Lucieer VL (2016) A review of marine geomorphometry, the quantitative study of the seafloor. Hydrol Earth Syst Sci 20:3207–3244. https://doi.org/10.5194/hess-20-3207-2016
Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2:18–22
Lucieer VL, Lamarche G (2011) Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait. NZ Cont Shelf Res 31:1236–1247. https://doi.org/10.1016/j.csr.2011.04.016
Lucieer VL, 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. https://doi.org/10.1016/j.ecss.2012.11.001
Lucieer V, Roche M, Degrendele K, Malik M, Dolan M, Lamarche G (2017) User expectations for multibeam echo sounders backscatter strength data-looking back into the future. Mar Geophys Res. https://doi.org/10.1007/s11001-017-9316-5
Lundblad ER et al (2006) A benthic terrain classification scheme for American Samoa. Mar Geodesy 29:89–111. https://doi.org/10.1080/01490410600738021
Lurton X (2010) An introduction to underwater acoustics—principles and applications, 2nd edn. Springer, Berlin
Lurton X, Lamarche G (2015) Backscatter measurements by seafloor-mapping sonars. Guidelines and recommendations vol. http://geohab.org/publications/
McArthur MA et al (2010) On the use of abiotic surrogates to describe marine benthic biodiversity. Estuar Coast Shelf Sci 88:21–32. https://doi.org/10.1016/j.ecss.2010.03.003
McKenzie DP et al (1996) Comparing correlated kappas by resampling: is one level of agreement significantly different from another? J Psychiatric Res 30:483–492
Micallef A, Le Bas TP, Huvenne VAI, Blondel P, Huhnerbach V, Deidun A (2012) A multi-method approach for benthic habitat mapping of shallow coastal areas with high-resolution multibeam data. Cont Shelf Res 39–40:14–26. https://doi.org/10.1016/j.csr.2012.03.008
Mitchell PJ, Monk J, Laurenson L (2017) Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes. Methods Ecol Evol 8:12–21. https://doi.org/10.1111/2041-210x.12645
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. https://doi.org/10.1016/j.ecss.2015.12.014
Montereale-Gavazzi G, Roche M, Lurton X, Degrendele K, Terseleer N, Van Lancker V (2017) Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea. Mar Geophys Res. https://doi.org/10.1007/s11001-017-9323-6
Phinn SR, Roelfsema CM, Mumby PJ (2012) Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs. Int J Remote Sens 33:3768–3797. https://doi.org/10.1080/01431161.2011.633122
R Development Core Team (2008) R: a Language and environment for statistical computing. R Foundation for Statistical Computing. URL: http://www.R-project.org
Rattray A, Ierodiaconou D, Laurenson L, Burq S, Reston M (2009) Hydro-acoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf. Estuar Coast Shelf Sci 84:237–245. https://doi.org/10.1016/j.ecss.2009.06.023
Rattray A, Ierodiaconou D, Monk J, Versace VL, Laurenson LJB (2013) Detecting patterns of change in benthic habitats by acoustic remote sensing. Mar Ecol Prog Ser 477:1–13. https://doi.org/10.3354/meps10264
Rattray A, Ierodiaconou D, Monk J, Laurenson LJB, Kennedy P (2014) Quantification of spatial and thematic uncertainty in the application of underwater video for benthic habitat mapping. Mar Geodesy 37:315–336. https://doi.org/10.1080/01490419.2013.877105
Rattray A, Ierodiaconou D, Womersley T (2015) Wave exposure as a predictor of benthic habitat distribution on high energy temperate reefs. Front Mar Sci. https://doi.org/10.3389/fmars.2015.00008
Sandwell D, Gille S, Orcutt J, Smith W (2003) Bathymetry from space is now possible. Eos Trans Am Geophys Union 84:37–44
Schimel A, Beaudoin J, Gaillot A, Keith G, Le Bas T, Parnum I, V. S (2015a) Chapter 6 processing backscatter data: from datagrams to angular responses and mosaics. In: Lurton X, Lamarche G (eds) Backscatter measurements by seafloor-mapping sonars. Guidelines and Recommendations. p 200
Schimel ACG, Ierodiaconou D, Hulands L, Kennedy DM (2015b) Accounting for uncertainty in volumes of seabed change measured with repeat multibeam sonar surveys. Cont Shelf Res 111:52–68. https://doi.org/10.1016/j.csr.2015.10.019
Schmidt J, Evans IS, Brinkmann J (2003) Comparison of polynomial models for land surface curvature calculation. Int J Geogr Inf Sci 17:797–814. https://doi.org/10.1080/13658810310001596058
Strobl C, Boulesteix AL, Augustin T (2006) Unbiased split selection for classification trees based on the gini index. Comput Stati Data Anal 52:483
Vanbelle S, Albert A (2008) A bootstrap method for comparing correlated kappa coefficients. J Stat Comput Simul 78:1009–1015. https://doi.org/10.1080/00949650701410249
Wernberg T et al (2016) Climate-driven regime shift of a temperate marine ecosystem. Science 353:169–172. https://doi.org/10.1126/science.aad8745
Wilson MFJ, O’Connell B, Brown C, Guinan JC, Grehan AJ (2007) Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental Slope. Mar Geodesy 30:3–35. https://doi.org/10.1080/01490410701295962
Wright D (2003) Undersea with GIS. ESRI Press, Redlands
Young M, Ierodiaconou D, Womersley T (2015) Forests of the sea: predictive habitat modelling to assess the abundance of canopy forming kelp forests on temperate reefs. Remote Sens Environ 170:178–187. https://doi.org/10.1016/j.rse.2015.09.020
Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Ierodiaconou, D., Schimel, A.C.G., Kennedy, D. et al. Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters. Mar Geophys Res 39, 271–288 (2018). https://doi.org/10.1007/s11001-017-9338-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11001-017-9338-z