Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN

Schoening T, Bergmann M, Purser A, Dannheim J, Gutt J, Nattkemper TW (2012)
PLoS ONE 7(6): e38179.

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Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.
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PLoS ONE
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7
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6
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e38179
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Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
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Schoening T, Bergmann M, Purser A, Dannheim J, Gutt J, Nattkemper TW. Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN. PLoS ONE. 2012;7(6):e38179.
Schoening, T., Bergmann, M., Purser, A., Dannheim, J., Gutt, J., & Nattkemper, T. W. (2012). Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN. PLoS ONE, 7(6), e38179. doi:10.1371/journal.pone.0038179
Schoening, T., Bergmann, M., Purser, A., Dannheim, J., Gutt, J., and Nattkemper, T. W. (2012). Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN. PLoS ONE 7, e38179.
Schoening, T., et al., 2012. Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN. PLoS ONE, 7(6), p e38179.
T. Schoening, et al., “Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN”, PLoS ONE, vol. 7, 2012, pp. e38179.
Schoening, T., Bergmann, M., Purser, A., Dannheim, J., Gutt, J., Nattkemper, T.W.: Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN. PLoS ONE. 7, e38179 (2012).
Schoening, Timm, Bergmann, Melanie, Purser, Autun, Dannheim, Jennifer, Gutt, Julian, and Nattkemper, Tim Wilhelm. “Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN”. PLoS ONE 7.6 (2012): e38179.
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PMID: 31036904
Underwater hyperspectral imaging as an in situ taxonomic tool for deep-sea megafauna.
Dumke I, Purser A, Marcon Y, Nornes SM, Johnsen G, Ludvigsen M, Søreide F., Sci Rep 8(1), 2018
PMID: 30150709
Quantitative comparison of taxa and taxon concepts in the diatom genus Fragilariopsis: a case study on using slide scanning, multiexpert image annotation, and image analysis in taxonomy1.
Beszteri B, Allen C, Almandoz GO, Armand L, Barcena MÁ, Cantzler H, Crosta X, Esper O, Jordan RW, Kauer G, Klaas C, Kloster M, Leventer A, Pike J, Rigual Hernández AS., J Phycol 54(5), 2018
PMID: 30014469
Compact-Morphology-based poly-metallic Nodule Delineation.
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PMID: 29042585
Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.
Osterloff J, Nilssen I, Eide I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Nattkemper TW., PLoS One 11(6), 2016
PMID: 27285611
Integrated environmental mapping and monitoring, a methodological approach to optimise knowledge gathering and sampling strategy.
Nilssen I, Ødegård Ø, Sørensen AJ, Johnsen G, Moline MA, Berge J., Mar Pollut Bull 96(1-2), 2015
PMID: 25956441
Area Estimation of Deep-Sea Surfaces from Oblique Still Images.
Dias FC, Gomes-Pereira J, Tojeira I, Souto M, Afonso A, Calado A, Madureira P, Campos A., PLoS One 10(7), 2015
PMID: 26177287
A Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme.
Althaus F, Hill N, Ferrari R, Edwards L, Przeslawski R, Schönberg CH, Stuart-Smith R, Barrett N, Edgar G, Colquhoun J, Tran M, Jordan A, Rees T, Gowlett-Holmes K., PLoS One 10(10), 2015
PMID: 26509918
High biodiversity on a deep-water reef in the eastern Fram Strait.
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PMID: 25153985
Increase of litter at the Arctic deep-sea observatory HAUSGARTEN.
Bergmann M, Klages M., Mar Pollut Bull 64(12), 2012
PMID: 23083926

97 References

Daten bereitgestellt von Europe PubMed Central.

Geology and geochemistry of abyssal plains.
Thomson J, Weaver P., 1987
Basing conservation policies for the deep-sea oor on current-diversity concepts: a consideration of rarity.
Carney RS., 1997
Diversity in deep-sea benthic macrofauna: the importance of local ecology, the larger scale, history and the Antarctic.
Gage JD., 2004
Characteristic size distributions of integral benthic communities.
Schwinghamer P., 1981
Biomass of the invertebrate megabenthos from 500 to 4100 m in the northeast Atlantic Ocean.
Lampitt RS, Billett DSM, Rice AL., 1986
Deep-sea epibenthic megafauna of the northeast Atlantic: Abundance and biomass at three mid-oceanic locations estimated from photographic transects.
Christiansen B, Thiel H., 1992
Megabenthic communities in the waters around Svalbard.
Piepenburg D, Chernova N, Dorrien C, Gutt J, Neyelov A., 1996
Pattern and zonation: a study of the bathyal megafauna using the research submersible Alvin.
Grassle JF, Sanders HL, Hessler RR, Rowe GT, McLellan T., 1975
Community structure in the deep-sea benthos.
Rex MA., 1981
Effects of bottom fishing on the benthic megafauna of georges bank.
Collie J, Escanero G, Valentine P., 1997
Importance of benthic habitat complexity for demersal fish assemblages. In: American Fisheries Society Symposium.
Kaiser M, Rogers S, Ellis J., 1999
Bacterial abundance and biomass in response to organismgenerated habitat heterogeneity in deep-sea sediments.
Soltwedel T, Vopel K., 2001
Small-scale heterogeneity in the arctic deep sea: impact of small coldwater-sponges on the diversity of benthic nematode communities.
Hasemann C, Soltwedel T., 2006
Impact of small-scale biogenic sediment structures on bacterial distribution and activity in arctic deep-sea sediments.
Quéric N, Soltwedel T., 2007
Detecting pollution induced changes in communities using the log-normal distribution of individuals among species.
Gray J., 1981
The benthic ecology of loch linnhe and loch eil, a sea-loch system on the west coast of scotland. v. biology of the dominant soft-bottom epifauna and their interaction with the infauna.
Feder H, Pearson T., 1988
Feeding ecology of liocarcinus depurator (decapoda: Portunidae) in the ria de arousa (galicia, north-west spain): effects of habitat, season and life history.
Freire J., 1996
The impact of epifaunal predation on the structure of macroinfaunal invertebrate communities of tidal saltmarsh creeks.
Sarda R, Foreman K, Werme C, Valiela I., 1998
Effects of megafauna exclusion on nematode assemblages at a deep-sea site.
Gallucci F, Fonseca G, Soltwedel T., 2008
Impact of bioroughness on interfacial solute exchange in permeable sediments.
Huettel M, Gust G., 1992
Mobile megafaunal activity monitored with a timelapse camera in the abyssal north pacific.
Smith K, Kaufmann R, Wakefield W., 1993
Partitioning of benthic community respiration in the arctic (northwestern barents sea).
Piepenburg D, Blackburn T, Dorrien C, Gutt J., 1995
Temporal variability in phytodetritus and megabenthic activity at the seabed in the deep northeast Atlantic.
Bett B, Malzone M, Narayanaswamy B, Wigham B., 2001
Processes driven by the small sized organisms at the water-sediment interface.
Lochte K, Pfannkuche O, Wefer G, Billett D, Hebbeln D., 2003
Alteration of bottom roughness by benthic organisms in a sandy coastal environment.
Guillén J, Soriano S, Demestre M, Falqués A, Palanques A., 2008
Temporal changes in benthic megafaunal abundance and composition across the west antarctic peninsula shelf: results from video surveys.
Sumida P, Bernardino A, Stedall V, Glover A, Smith C., 2008
Activity patterns of mobile epibenthic megafauna at an abyssal site in the eastern North Pacific: Results from a 17-month time-lapse photographic study.
Kaufmann R, Smith K., 1997
Re-establishment of an abyssal megabenthic community after experimental physical disturbance of the seaoor.
Hartmut null, Bluhm null., 2001
ATOC/Pioneer seamount cable after 8 years on the seaoor: Observations, environmental impact.
Kogan I, Paull CK, Kuhnz LA, Burton EJ, Thun SV., 2006
The interannual variability of megafaunal assemblages in the arctic deep sea: Preliminary results from the HAUSGARTEN observatory (79 N).
Bergmann M, Soltwedel T, Klages M., 2011
Climate, carbon cycling, and deep-ocean ecosystems.
Smith KL, Ruhl HA, Bett BJ, Billett DSM, Lampitt RS., 2009
Long-term change in the abyssal ne Atlantic: The Amperima Event revisited.
Billett D, Bett B, Reid W, Boorman B, Priede I., 2010
Biological and geological observations on the first photographs of the Arctic Ocean deep-sea oor.
Hunkins KL, Ewing M, Heezen BC, Menzies RJ., 1960
Investigations of the deep-sea bottom fauna in the central part of the Arctic Ocean.
Afanasev I., 1978
Benthic macrofauna and megafauna assemblages in the arctic deep-sea Canada Basin.
MacDonald IR, Bluhm BA, Iken K, Gagaev S, Strong S., 2010
Benthic ecology of the high arctic deep sea.
Paul AZ, Menzies RJ., 1974
Epibenthic distribution patterns on the continental slope off East Greenland at 75 N.
Mayer M, Piepenburg D., 1996
Mega-epibenthic diversity: a polar comparison.
Starmans A, Gutt J., 2002
Depth-related changes in the arctic epibenthic megafaunal assemblages of Kangerdlugssuaq, East Greenland.
Jones DOB, Bett BJ, Tyler PA., 2007
Colonisation of hard substrata along a channel system in the deep Greenland Sea.
Schulz M, Bergmann M, von K, Soltwedel T., 2010
Macro- and megabenthic communities in the high arctic Canada Basin: initial findings.
Bluhm BA, MacDonald IR, Debenham C, Iken K., 2005
Bathymetric patterns of megafaunal assemblages from the arctic deep-sea observatory HAUSGARTEN.
Soltwedel T, Jaeckisch N, Ritter N, Hasemann C, Bergmann M., 2009
HAUSGARTEN: multidisciplinary investigations at a deep-sea, long-term observatory in the Arctic Ocean.
Soltwedel T, Bauerfeind E, Bergmann M, Budaeva N, Hoste E., 2005
The distribution of the larger epifauna during summer and winter in the North Sea and its suitability for environmental monitoring.
Frauenheim K, Neumann V, Thiel H, Türkay M., 1989
Variations in abundance and distribution of demersal fish species in the coastal zone of the southeastern North Sea between 1980 and 1993.
van P, Rijnsdorp A, Vingerhoed B., 1994
Impact-ii: The effects of different types of fisheries on the north sea and irish sea benthic ecosystems.
Lindeboom H, De S., 1998
Estimating the catching efficiency of a 2-m beam trawl for sampling epifauna by removal experiments.
Reiss H, Kröncke I, Ehrich S., 2006
Unusual megafaunal assemblages on the continental slope off Cape Hatteras.
Hecker B., 1994
Distribution density and relative abundance of benthic invertebrate megafauna from three sites at the base of the continental slope off central California as determined by camera sled and beam trawl.
Nybakken J, Craig S, Smith-Beasley L, Moreno G, Summers A., 1998
Megafaunal assemblages from two shelf stations west of Svalbard.
Bergmann M, Langwald N, Ontrup J, Soltwedel T, Schewe I., 2011
Distribution and abundance of epibenthic megafauna at a long time-series station in the abyssal northeast Pacific.
Weaver ML, Noebe RD, Kaufmann MJ, Lauerman LML, Kaufmann RS., 1996
Towards a greater understanding of pattern, scale and process in marine benthic systems: a picture is worth a thousand worms.
Solan M, Germano JD, Rhoads DC, Smith C, Michaud E., 2003
Ecosystems of the deep oceans, volume 28.
Tyler P., 2003
Do experts make mistakes? a comparison of human and machine identification of dinoagellates.
Culverhouse P, Williams R, Reguera B, Herry V, Gonzlez-Gil S., 2003
Time to automate identification.
MacLeod N, Benfield M, Culverhouse P., Nature 467(7312), 2010
PMID: 20829777
A multiview, multimodal fusion framework for classifying small marine animals with an opto-acoustic imaging system.
Roberts P, Jaffe J, Trivedi M., 2009
Detecting, tracking and classifying animals in underwater video.
Edgington D, Cline D, Davis D, Kerkez I, Mariette J., 2006
A new SVM-based architecture for object recognition in color underwater images with classification refinement by shape descriptors. In: IEEE International Conference on Automation, Quality and Testing.
Gordan M, Dancea O, Stoian I, Georgakis A, Tsatos O., 2006
A hierarchical classification system for object recognition in underwater environments.
Foresti G, Gentili S., 2002
A robust visual attention system for detecting manufactured objects in underwater video.
Barat C, Rendas MJ., 2006
One fish, two fish, butterfish, trumpeter: Recognizing fish in underwater video.
Rova A, Mori G, Dill LM., 2007
Automatic fish classification for underwater species behavior understanding.
Spampinato C, Giordano D, Di R, Chen-Burger YHJ, Fisher RB., 2010
Finding essential features for tracking starfish in a video sequence.
Di V, Isgro F, Tegolo D, Trucco E., 2003
Detection and identification of sardine eggs at sea using a machine vision system. In: OCEANS 2003. volume 1, p.
Powell J, Krotosky S, Ochoa B, Checkley D, Cosman P., 2003
Toward robust image detection of crown-of-thorns starfish for autonomous population monitoring.
Clement R, Dunbabin M, Wyeth G., 2005
Use of machine-learning algorithms for the automated detection of cold-water coral habitats - a pilot study.
Purser A, Bergmann M, Lundälv T, Ontrup J, Nattkemper TW., 2009
Toward adaptive benthic habitat mapping using gaussian process classification.
Rigby P, Pizarro O, Williams S., 2010
Evolution of a benthic imaging system from a towed camera to an automated habitat characterization system.
Taylor R, Vine N, York A, Lerner S, Hart D., 2008
Using a towed optical habitat mapping system to monitor the invasive tunicate species Didemnum sp. along the northeast continental shelf.
York A, Gallager S, Taylor R, Vine N, Lerner S., 2008
Development of a towed survey system for deployment by the fishing industry.
Howland J, Gallager S, Singh H, Girard A, Abrams L., 2006
Hierarchical segmentation-based software for cover classification analyses of seabed images (seascape).
Teixido N, Albajes-Eizagirre A, Bolbo D, Hir EL, Demestre M., 2011
Evidence for long-range chemoreceptive tracking of food odour in deep-sea scavengers by scanning sonar data.
Premke K, Muyakshin S, Klages M, Wegner J., 2003
Aggregations of arctic deep-sea scavengers at large food falls: temporal distribution, consumption rates and population structure.
Premke K, Klages M, Arntz W., 2006
Caging experiment in the deep sea: Efficiency and artefacts from a case study at the arctic long-term observatory HAUSGARTEN.
Gallucci F, Sauter E, Sachs O, Klages M, Soltwedel T., 2008
Characterization of prokaryotic community dynamics in the sedimentary microenvironment of the demosponge Tentorium semisuberites from arctic deep waters.
Quric N, Arrieta null, JM null, Soltwedel T, Arntz W., 2008
Microbial colonisation of artificial and deep-sea sediments in the Arctic Ocean.
Kanzog C, Ramette A., 2009
Response of benthic microbial communities to chitin enrichment: an in situ study in the deep Arctic Ocean.
Kanzog C, Ramette A, Quric N, Klages M., 2009
Nutritional importance of benthic bacteria for deep-sea nematodes from the arctic ice margin: Results of an isotope tracer experiment.
Guilini K, Van D, Soetaert K, Middelburg J, Vanreusel A., 2010
Carbon ows in the benthic food web at the deep-sea observatory HAUSGARTEN (Fram Strait).
van D, Bergmann M, Soetaert K, Bauerfeind E, Hasemann C., 2011
BIIGLE - Web 2.0 enabled labelling and exploring of images from the Arctic deep-sea observatory HAUSGARTEN.
Ontrup J, Ehnert N, Bergmann M, Nattkemper T., 2009

White OR., 2006
The MPEG-7 visual standard for content description-an overview.
Sikora T., 2001
BilVideo-7: An MPEG-7-compatible video indexing and retrieval system.
Baştan M, Çam H, Güdükbay U, Özgür Ulusoy., 2010
Statistical geometrical features for texture classification.
Chen YQ, Nixon MS, Thomas DW., 1995
Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression.
Daugman J., 1988
A computational feature binding model of human texture perception.
Ontrup J, Wersing H, Ritter H., 2004
A dendrite method for cluster analysis.
Caliski T, Harabasz J., 1974
Multiobjective genetic clustering for pixel classification in remote sensing imagery.
Bandyopadhyay S, Maulik U, Mukhopadhyay A., 2007
A cluster separation measure.
Davies DL, Bouldin DW., 1979
Digital Picture Processing.
Rosenfeld A, Kak AC., 1982
The nature of statistical learning theory.
Vapnik V., 2000
An introduction to support vector machines: and other kernel-based learning methods.
Cristianini N, Shawe-Taylor J., 2000
Making large-scale SVM learning practical.
Joachims T., 1999
Pattern Recognition and Machine Learning (Information Science and Statistics).
Bishop CM., 2007
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers.
Platt JC., 1999

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