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Oceanological and Hydrobiological Studies


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Volume 47, Issue 3

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Seafloor mapping based on multibeam echosounder bathymetry and backscatter data using Object-Based Image Analysis: a case study from the Rewal site, the Southern Baltic

Łukasz Janowski
  • Corresponding author
  • Institute of Oceanography, University of Gdańsk, Al. M. Piłsudskiego 46, 81-378 Gdynia, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jarosław Tęgowski
  • Institute of Oceanography, University of Gdańsk, Al. M. Piłsudskiego 46, 81-378 Gdynia, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jarosław Nowak
Published Online: 2018-09-21 | DOI: https://doi.org/10.1515/ohs-2018-0024

Abstract

Seafloor mapping is a fast developing multidisciplinary branch of oceanology that combines geophysics, geostatistics, sedimentology and ecology. One of its objectives is to isolate distinct seabed features in a repeatable, fast and objective way, taking into consideration multibeam echosounder (MBES) bathymetry and backscatter data. A large-scale acoustic survey was conducted by the Maritime Institute in Gdańsk in 2010 using Reson 8125 MBES. The dataset covered over 20 km2 of a shallow seabed area (depth of up to 22 m) in the Polish Exclusive Economic Zone within the Southern Baltic. Determination of sediments was possible based on ground-truth grab samples acquired during the MBES survey. Four classes of sediments were recognized as muddy sand, very fine sand, fine sand and clay. The backscatter mosaic created using the Angular Variable Gain (AVG) empirical method was the primary contribution to the image processing method used in this study. The use of the Object-Based Image Analysis (OBIA) and the Classification and Regression Trees (CART) classifier makes it possible to isolate the backscatter image with 87.5% overall and 81.0% Kappa accuracy. The obtained results confirm the possibility of creating reliable maps of the seafloor based on MBES measurements. Once developed, the OBIA workflow can be applied to other spatial and temporal scenes.

Key words: habitat mapping; multibeam echosounder; image processing; Object-Based Image Analysis; Classification and Regression Trees; Southern Baltic; Angle Varied Gain; feature selection

References

  • Baatz, M. & Schäpe, A. (2000). Multiresolution segmentation – an optimization approach for high quality multi-scale image segmentation. In J. Stobl, T. Blashke & G. Griesebner (Eds.), Angewandte Geograpische InformationsVerarbeitung XII (pp. 12–23). Karlsruche: Wichmann Verlag.Google Scholar

  • Benz, U., Hofman, P., Willhauck, G., Lingenfelder, I. & Heyen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. Jorunal of Photogrammetry and Remote Sensing 58(3): 239–258. .CrossrefGoogle Scholar

  • Blondel, P. (2009). The Handbook of Sidescan Sonar. Springer Praxis: Heidelberg.Google Scholar

  • Breiman, L. (2001). Random Forests. Machine Learning 45(1): 5–32. .CrossrefGoogle Scholar

  • Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Belmont: Wadsworth.Google Scholar

  • Brown, C.J., Todd, B.J., Kostylev, V.E. & Pickrill, R.A. (2011). Image-based classification of multibeam sonar backscatter data for objective surficial sediment mapping of Georges Bank, Canada. Continental Shelf Research 31(2): 110–119. .CrossrefGoogle Scholar

  • Carlotto, M.J. (2009). Effect of errors in ground truth on classification accuracy. International Journal of Remote Sensing 30(18): 4831–4849. .CrossrefGoogle Scholar

  • Che Hasan, R., Ierodiaconou, D. & Laurenson, L. (2012a). Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping. Estuarine, Coastal and Shelf Science 97: 1–9. .CrossrefGoogle Scholar

  • Che Hasan, R., Ierodiaconou, D. & Monk, J. (2012b). Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar. Remote Sensing 4(11): 3427–3443. .CrossrefGoogle Scholar

  • Cohen, J. (1960). A coefficient of agreement of nominal scales. Educational and Psychological Measurement 20(1): 37–46. .CrossrefGoogle Scholar

  • Congalton, R.G. (1991). A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment 37(1): 35–46. .CrossrefGoogle Scholar

  • Cortes, C. &Vapnik, V. (1995). Support-Vector Networks. Machine Learning 20: 273–297. .CrossrefGoogle Scholar

  • Dartnell, P. & Gardner, J.V. (2004). Predicting seafloor facies from multibeam bathymetry and backscatter data. Photogrammetric Engineering and Remote Sensing 70: 1081–1091. .CrossrefGoogle Scholar

  • Diesing, M., Green, S.L., Stephens, D., Lark, R.M., Stewart, H.A. et al. (2014). Mapping seabed sediments: Comparison of manual, geostatical, object-based image analysis an machine learning approaches. Continental Shelf Research 84: 107–119. .CrossrefGoogle Scholar

  • Diesing, M. (2016). Application of geobia to map the seafloor. In GEOBIA 2016: Solutions and Synergies, 1416 September 2016 (pp. 3). Enschede, Netherlands: ITC / University of Twente.Google Scholar

  • Diesing, M., Mitchell, P. & Stephens, D. (2016). Image-based seabed classification: what can we learn from terrestrial remote sensing? ICES Journal of Marine Science. 73(10): 2425–2441. .CrossrefGoogle Scholar

  • Drăguţ, L., Csillik, O., Eisank, C. & Tiede, D. (2014). Automated parametrisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing 88(100): 119–127. .CrossrefGoogle Scholar

  • Drăguţ, L., Tiede, D. & Levick, S.R. (2010). ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science 24(6): 859–871. .CrossrefGoogle Scholar

  • Esri. (2016, February 5). Mosaic dataset properties. Retrieved June 27, 2017, from http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/mosaic-dataset-properties.html

  • 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 angular signatures. Applied Acoustics 70(10): 1298–1304. .CrossrefGoogle Scholar

  • Foody, G.M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment 80(1): 185–201. .CrossrefGoogle Scholar

  • Gudelis, W.K. & Jemielianow, J.M. (1982). Geologia Morza Bałtyckiego. Warszawa: Wyd. Geologiczne. (In Polish).Google Scholar

  • Haralick, R.M., Shanmugam, K. & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics SMC-3(6): 610–621. .CrossrefGoogle Scholar

  • Hay, G.J. & Castilla, G. (2006). Object-Based Image Analysis: Strengths, Weaknesses, Opportunities, and Threats (SWOT). In 1st International Conference on Object-based Image Analysis, 4–5 July 2006 (pp. 3). Salzburg, Austria: International Society for Photogrammetry and Remote Sensing.Google Scholar

  • Huang, Z., Nichol, S.L., Siwabessy, J.P., Daniell, J. & Brooke, B.P. (2012). Predictive modelling of seabed parameters using mutibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia. International Journal of Geographical Information Science 26: 283–307. .CrossrefGoogle Scholar

  • Ierodiaconou, D., Monk, J., Rattray, A., Laurenson, L. & Versace, V.L. (2011). Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustic and video observations. Continental Shelf Research. 31: 28–38. .CrossrefGoogle Scholar

  • Lamarche, G., Lurton, X., Verdier, A.L. & Augustin, J.M. (2011). Quantitative characterization of seafloor substrate and bedforms using advanced processing of multibeam backscatter. Application to the Cook Strait, New Zealand. Continental Shelf Research 31(2): 93–109. .CrossrefGoogle Scholar

  • Lecours, V., Dolan, M.F.J., Micallef, A. & Lucieer, V.L. (2016). A review of marine geomorphometry, the quantitative study of the seafloor. Hydrology and Earth System Sciences. 20: 3207–3244. .CrossrefGoogle Scholar

  • Li, J., Tran, M. & Siwabessy, J. (2016). Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness. Plos ONE 11(2): 1–29. .CrossrefGoogle Scholar

  • Li, D., Tang, C., Xia, C. & Zhang, H. (2017). Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water. Estuarine, Coastal and Shelf Science 185: 11–21. .CrossrefGoogle Scholar

  • Lucieer, V. (2008). Object-oriented classification of sidescan sonar data for mapping benthic marine habitats. International Journal of Remote Sensing 29(3): 905–921. .CrossrefGoogle 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. Continental Shelf Research 31: 1236–1247. .CrossrefGoogle Scholar

  • Lucieer, V., Hill, N.A., Barrett, N.S. & Nichol, S. (2013). So marine substrates ‘look’ and ‘sound’ the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle. Estuarine, Coastal and Shelf Science. 117: 94–106. .CrossrefGoogle Scholar

  • Lurton, X. & Lamarche, G. (2015, May). Backscatter measurements by seafloor-mapping sonarsGuidelines and Recommendations. Retrieved June 27, 2017, from http://geohab.org/publications/

  • Madricardo, F., Foglini, F., Kruss, A., Ferrarin, C., Pizzeghello, N.M. et al. (2017). High resolution multibeam and hydrodynamic datasets of tidal channels and inlets of the Venice Lagoon. Scientific Data 4: 170121. .CrossrefPubMedGoogle Scholar

  • Mehryar, M., Rostamizadeh, A. & Talwalkar, A. (2012). Foundations of Machine Learning. Cambridge: The MIT Press.Google Scholar

  • Micallef, A., Le Bas, T.P., Huvenne, V.A., Blondel, P., Hühnerbach, V. et al. (2012). A multi-method approach for benthic mapping of shallow coastal areas with high-resolution Multibeam data. Continental Shelf Research. 39–40: 14–26. .CrossrefGoogle Scholar

  • Mojski, J.E. (1995). Atlas Geologiczny Południowego Bałtyku. Sopot-Warszawa: PIG. (In Polish).Google Scholar

  • Montereale Gavazzi, G., Madricardo, F., Janowski, L., Kruss, A., Blondel, P. et al. (2016). Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats – Application to the Venice Lagoon, Italy. Estuarine, Coastal and Shelf Science 170: 45–60. .CrossrefGoogle Scholar

  • Montereale Gavazzi, G., Roche, M., Lurton, X., Degrendele, K., Terseleer, N. et al. (2017). Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea. Marine Geophysical Research 1–19. .CrossrefGoogle Scholar

  • Parnum, I.M. (2007). Benthic habitat mapping using multibeam sonar systems. Perth, Australia: Curtin University.Google Scholar

  • Pieczka, F. (1980). Geomorfologia i Osady Denne Bałtyku Południowego. PerribalticumProblemy Badawcze Obszaru Bałtyckiego. Gdańsk: Ossolineum. (In Polish).Google Scholar

  • QPS. (2015, March 31). QINSy 8.10.2015.03.31.1 Release Notes. Retrieved June 27, 2017, from https://confluence.qps.nl/display/dwn/QINSy+8.10.2015.03.31.1+Release+Notes

  • Rattray, A., Ierodiaconou, D., Monk, J., Versace, V.L. & Laurenson, L.J.B. (2013). Detecting patterns of change in benthic habitats by acoustic remote sensing. Marine Ecology Progress Series 477: 1–13. .CrossrefGoogle Scholar

  • Sappington, J.M., Longshore, K.M. & Thomson, D.B. (2007). Quantifying Landscape Ruggedness for Animal Habitat Analysis: A Case Study Using Bighorn Sheep in the Mojave Desert. Journal of Wildlife Management 71(5): 1419–1426. .CrossrefGoogle 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 9(4): 1–14. .CrossrefGoogle Scholar

  • Story, M. & Congalton, R.G. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing. 52: 397–399.Google Scholar

  • Tęgowski, J. & Łubniewski, Z. (2002). Seabed characterisation using spectral moments of the echo signal. Acta Acustica / Acustica 88(5): 623–626.Google Scholar

  • Wentworth, C.K. (1922). A Scale of Grade and Class terms for Clastic Sediments. The Journal of Geology 30(5): 377–392. .CrossrefGoogle Scholar

  • Wilson, M.F.J., O’Connell, B., Brown, C., Guinan, J.C & Grehan, A.J. (2007). Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope. Marine Geodesy 30: 3–35. .CrossrefGoogle Scholar

  • Wright, D. & Heyman, W. (2008). Introduction to the special issue: marine and coastal GIS for geomorphology, habitat mapping, and marine reserves. Marine Geodesy 31: 223–230. .CrossrefGoogle Scholar

About the article

Received: 2017-10-04

Accepted: 2018-01-16

Published Online: 2018-09-21

Published in Print: 2018-09-25


Citation Information: Oceanological and Hydrobiological Studies, Volume 47, Issue 3, Pages 248–259, ISSN (Online) 1897-3191, ISSN (Print) 1730-413X, DOI: https://doi.org/10.1515/ohs-2018-0024.

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