Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.

[1]  Guaraci J. Erthal,et al.  Satellite Imagery Segmentation: a region growing approach , 1996 .

[2]  Á. Borja,et al.  Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis , 2009 .

[3]  Veerle A.I. Huvenne,et al.  Acquisition and processing of backscatter data for habitat mapping - comparison of multibeam and sidescan systems , 2009 .

[4]  Iain Parnum,et al.  Relationships between multibeam backscatter, sediment grain size and Posidonia oceanica seagrass distribution , 2010 .

[5]  N. Coleman,et al.  High species richness in the shallow marine waters of south-east Australia , 1997 .

[6]  G. Cutter,et al.  Automated segmentation of seafloor bathymetry from multibeam echosounder data using local Fourier histogram texture features , 2003 .

[7]  V. Lucieer,et al.  Do marine substrates 'look' and 'sound' the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images , 2013 .

[8]  Peter T. Harris,et al.  Geology–benthos relationships on a temperate rocky bank, eastern Bass Strait, Australia , 2005 .

[9]  D. Wright,et al.  A Benthic Terrain Classification Scheme for American Samoa , 2006 .

[10]  Iain Parnum,et al.  Acoustic seabed segmentation from direct statistical clustering of entire multibeam sonar backscatter curves , 2011 .

[11]  John S. Gray,et al.  Marine biodiversity: patterns, threats and conservation needs , 2004, Biodiversity & Conservation.

[12]  V. Lucieer,et al.  Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand , 2011 .

[13]  Carrie V. Kappel,et al.  A Global Map of Human Impact on Marine Ecosystems , 2008, Science.

[14]  Erica Morris,et al.  Recent Advances in Automated Genus-specific Marine Habitat Mapping Enabled by High-resolution Multibeam Bathymetry , 2005 .

[15]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[16]  Craig J. Brown,et al.  Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques , 2011 .

[17]  J. Clarke,et al.  Areal Seabed Classification using Backscatter Angular Response at 95 kHz , 1997 .

[18]  D. Simons,et al.  A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data , 2009 .

[19]  C. Brown,et al.  Mapping benthic habitat in regions of gradational substrata: An automated approach utilising geophysical, geological, and biological relationships , 2008 .

[20]  M. Kendall,et al.  Benthic Mapping Using Sonar, Video Transects, and an Innovative Approach to Accuracy Assessment: A Characterization of Bottom Features in the Georgia Bight , 2005 .

[21]  Thomas Heege,et al.  Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor , 2013, PloS one.

[22]  Daniel Ierodiaconou,et al.  Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping , 2012 .

[23]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[24]  Russell Congalton,et al.  Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition , 1998 .

[25]  Iain Parnum,et al.  Benthic habitat mapping using multibeam sonar systems , 2007 .

[26]  Xavier Lurton,et al.  Quantitative characterisation of seafloor substrate and bedforms using advanced processing of multibeam backscatter—Application to Cook Strait, New Zealand , 2011 .

[27]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[28]  Jacquomo Monk,et al.  Victorian marine habitat mapping project , 2007 .

[29]  Luciano E. Fonseca,et al.  Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data , 2007 .

[30]  Zhi Huang,et al.  Predictive mapping of seabed cover types using angular response curves of multibeam backscatter data: Testing different feature analysis approaches , 2013 .

[31]  G. Kendrick,et al.  Modelling distribution of marine benthos from hydroacoustics and underwater video , 2008 .

[32]  D. Ierodiaconou,et al.  Marine benthic habitat mapping using Multibeam data, georeferencedvideo and image classification techniques in Victoria, Australia , 2007 .

[33]  B. Calder,et al.  Angular range analysis of acoustic themes from Stanton Banks Ireland: A link between visual interpretation and multibeam echosounder angular signatures , 2009 .

[34]  N. Holbrook,et al.  Image-based continental shelf habitat mapping using novel automated data extraction techniques , 2012 .

[35]  G. Fader,et al.  Benthic habitat mapping on the Scotian Shelf based on multibeam bathymetry, surficial geology and sea floor photographs , 2001 .

[36]  J. Guinan,et al.  Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope , 2007 .

[37]  I. Wright,et al.  Inhomogeneous substrate analysis using EM300 backscatter imagery , 2003 .

[38]  Jochen Schmidt,et al.  Comparison of polynomial models for land surface curvature calculation , 2003, Int. J. Geogr. Inf. Sci..

[39]  V. Quintino,et al.  Benthic biotopes remote sensing using acoustics , 2003 .

[40]  Yuri Rzhanov,et al.  Construction of seafloor thematic maps from multibeam acoustic backscatter angular response data , 2012, Comput. Geosci..

[41]  James V. Gardner,et al.  Predicting seafloor facies from multibeam bathymetry and backscatter data , 2004 .

[42]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[43]  Iain Parnum,et al.  High-frequency multibeam echo-sounder measurements of seafloor backscatter in shallow water: Part 1 - Data acquisition and processing , 2011 .

[44]  P. Dartnell,et al.  Characterizing benthic substrates of Santa Monica Bay with seafloor photography and multibeam sonar imagery. , 2003, Marine environmental research.

[45]  Tsehaie Woldai,et al.  Multi- and hyperspectral geologic remote sensing: A review , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[46]  Jeremy S. Collie,et al.  Modification of marine habitats by trawling activities: prognosis and solutions , 2002 .

[47]  Sovan Lek,et al.  Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin , 2012 .

[48]  S. White,et al.  Lava morphology mapping by expert system classification of high-resolution side-scan sonar imagery from the East Pacific Rise, 9°–10°N , 2007 .

[49]  Chris McGonigle,et al.  Detection of deep water benthic macroalgae using image-based classification techniques on multibeam backscatter at Cashes Ledge, Gulf of Maine, USA , 2011 .

[50]  Philippe Blondel,et al.  Textural analyses of multibeam sonar imagery from Stanton Banks, Northern Ireland continental shelf , 2009 .

[51]  L. Hellequin,et al.  Postprocessing and signal corrections for multibeam echosounder images , 1997, Oceans '97. MTS/IEEE Conference Proceedings.

[52]  D. Ierodiaconou,et al.  Hydro-acoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf , 2009 .

[53]  Brian Gratwicke,et al.  The relationship between fish species richness, abundance and habitat complexity in a range of shallow tropical marine habitats , 2005 .

[54]  K. Bjorndal,et al.  Historical Overfishing and the Recent Collapse of Coastal Ecosystems , 2001, Science.

[55]  A. Collin,et al.  Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners , 2011, PloS one.

[56]  Iain Parnum,et al.  High-frequency multibeam echo-sounder measurements of seafloor backscatter in shallow water: Part 2 - Mosaic production, analysis and classification , 2011 .

[57]  W. T. Collins,et al.  STATISTICAL SEABED SEGMENTATION - FROM IMAGES AND ECHOES TO OBJECTIVE CLUSTERING , 2004 .

[58]  B. Calder,et al.  Remote identification of a shipwreck site from MBES backscatter. , 2012, Journal of environmental management.

[59]  Philippe Blondel,et al.  A multi-method approach for benthic habitat mapping of shallow coastal areas with high-resolution multibeam data , 2012 .

[60]  Benthic habitat mapping using multibeam sonar , 2009 .

[61]  Peter Barnes,et al.  Benthic Habitats and the Effects of Fishing , 2005 .

[62]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[63]  Jacquomo Monk,et al.  Comparison of automated classification techniques for predicting benthic biological communities using hydroacoustics and video observations , 2011 .

[64]  A. Potter,et al.  Can we improve the spatial predictions of seabed sediments? A case study of spatial interpolation of mud content across the southwest Australian margin , 2011 .

[65]  Colin Brown,et al.  Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV) , 2009 .

[66]  X. Lurton,et al.  Analysis of multibeam echo-sounder signals from the deep seafloor , 1994, Proceedings of OCEANS'94.

[67]  M. Daily,et al.  Hue-saturation-intensity split-spectrum processing of Seasat radar imagery , 1983 .