Quantification of Spatial and Thematic Uncertainty in the Application of Underwater Video for Benthic Habitat Mapping

This study presents an analysis of the application of underwater video data collected for training and validating benthic habitat distribution models. Specifically, we quantify the two major sources of error pertaining to collection of this type of reference data. A theoretical spatial error budget is developed for a positioning system used to co-register video frames to their corresponding locations at the seafloor. Second, we compare interpretation variability among trained operators assessing the same video frames between times over three hierarchical levels of a benthic classification scheme. Propagated error in the positioning system described was found to be highly correlated with depth of operation and varies from 1.5m near the surface to 5.7m in 100m of water. In order of decreasing classification hierarchy, mean overall observer agreement was found to be 98% (range 6%), 82% (range 12%) and 75% (range 17%) for the 2, 4, and 6 class levels of the scheme, respectively. Patterns in between-observer variation are related to the level of detail imposed by each hierarchical level of the classification scheme, the feature of interest, and to the amount of observer experience.

[1]  A. Agresti An introduction to categorical data analysis , 1997 .

[2]  Giles M. Foody,et al.  The impact of imperfect ground reference data on the accuracy of land cover change estimation , 2009 .

[3]  D. Rooij,et al.  Habitat Mapping of a Cold-Water Coral Mound on Pen Duick Escarpment (Gulf of Cadiz) , 2012 .

[4]  R. Fisher On the Interpretation of χ2 from Contingency Tables, and the Calculation of P , 2018, Journal of the Royal Statistical Society Series A (Statistics in Society).

[5]  S. Lourie,et al.  Using Biogeography to Help Set Priorities in Marine Conservation , 2004 .

[6]  C. McClean,et al.  An investigation of uncertainty in field habitat mapping and the implications for detecting land cover change , 1995, Landscape Ecology.

[7]  Weiqi Zhou,et al.  Mapping urban landscape heterogeneity: agreement between visual interpretation and digital classification approaches , 2009, Landscape Ecology.

[8]  J. Gutt,et al.  Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN , 2012, PloS one.

[9]  G. Foody Assessing the Accuracy of Remotely Sensed Data: Principles and Practices , 2010 .

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

[11]  Michael A. Wulder,et al.  Validation of a large area land cover product using purpose-acquired airborne video , 2007 .

[12]  R. Lunetta,et al.  Remote sensing and Geographic Information System data integration: error sources and research issues , 1991 .

[13]  J. Opderbecke,et al.  The Moira Mounds, small cold-water coral mounds in the Porcupine Seabight, NE Atlantic: Part B—Evaluating the impact of sediment dynamics through high-resolution ROV-borne bathymetric mapping , 2011 .

[14]  Chris McGonigle,et al.  Evaluation of image-based multibeam sonar backscatter classification for benthic habitat discrimination and mapping at Stanton Banks, UK , 2009 .

[15]  John W. King,et al.  Comparison of methods for integrating biological and physical data for marine habitat mapping and classification , 2010 .

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

[17]  J. Henriet,et al.  Environmental setting of deep-water oysters in the Bay of Biscay , 2010 .

[18]  Donald A. Walker,et al.  Accuracy Assessment of a Land-Cover Map of the Kuparu k River Basin, Alaska: Considerations for Remote Regions , 1998 .

[19]  Pedro J. Leitão,et al.  Effects of species and habitat positional errors on the performance and interpretation of species distribution models , 2009 .

[20]  K. Howell,et al.  When the species is also a habitat: Comparing the predictively modelled distributions of Lophelia pertusa and the reef habitat it forms , 2011 .

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

[22]  R. Fisher On the Interpretation of χ2 from Contingency Tables, and the Calculation of P , 2010 .

[23]  D R C Philip AN EVALUATION OF USBL AND SBL ACOUSTIC SYSTEMS AND THE OPTIMISATION OF METHODS OF CALIBRATION - PART 3 , 2003 .

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

[25]  Veerle Cnudde,et al.  Cold-water coral mounds on the Pen Duick Escarpment, Gulf of Cadiz: The MiCROSYSTEMS project approach , 2011 .

[26]  A. Lucieer,et al.  Fuzzy clustering for seafloor classification , 2009 .

[27]  Jacquomo Monk,et al.  Detecting patterns of change in benthic habitats by acoustic remote sensing , 2013 .

[28]  P. Harris,et al.  Deep-sea bio-physical variables as surrogates for biological assemblages, an example from the Lord Howe Rise , 2011 .

[29]  Colin J. McClean,et al.  Between‐observer variation in the application of a standard method of habitat mapping by environmental consultants in the UK , 1999 .

[30]  P. Culverhouse,et al.  Do experts make mistakes? A comparison of human and machine identification of dinoflagellates , 2003 .

[31]  J. Tebbs,et al.  An Introduction to Categorical Data Analysis , 2008 .

[32]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[33]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[34]  Mark Zimmermann,et al.  A bottom-up methodology for integrating underwater video and acoustic mapping for seafloor substrate classification , 2007 .

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

[36]  D. Roberts,et al.  Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon , 2004 .

[37]  J R Healey,et al.  The repeatability of vegetation classification and mapping. , 2011, Journal of environmental management.

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

[39]  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 .

[40]  D. Parsons,et al.  Fine-scale habitat change in a marine reserve, mapped using radio-acoustically positioned video transects , 2004 .

[41]  V. Lucieer,et al.  Characterization of Shallow Inshore Coastal Reefs on the Tasman Peninsula, Southeastern Tasmania, Australia , 2012 .

[42]  Peter T. Harris,et al.  GeoHab Atlas of Seafloor Geomorphic Features and Benthic Habitats: Synthesis and Lessons Learned , 2012 .

[43]  G. Foody,et al.  A fuzzy classification of sub-urban land cover from remotely sensed imagery , 1998 .

[44]  C. Woodcock,et al.  Theory and methods for accuracy assessment of thematic maps using fuzzy sets , 1994 .

[45]  G. Foody Assessing the accuracy of land cover change with imperfect ground reference data , 2010 .

[46]  Rudy J. Kloser,et al.  Multi-beam backscatter measurements used to infer seabed habitats , 2010 .

[47]  F. Douvere The importance of marine spatial planning in advancing ecosystem-based sea use management , 2008 .

[48]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[49]  R. Quinn,et al.  Operational Parameters, Data Density and Benthic Ecology: Considerations for Image-Based Classification of Multibeam Backscatter , 2010 .

[50]  J. M. Rey Benayas,et al.  Remote sensing and the future of landscape ecology , 2009 .