Improving coral monitoring by reducing variability and bias in cover estimates from seabed images
Improving coral monitoring by reducing variability and bias in cover estimates from seabed images
Seabed cover of organisms is an established metric for assessing the status of many vulnerable marine ecosystems. When deriving cover estimates from seafloor imagery, a source of uncertainty in capturing the true distribution of organisms is introduced by the inherent variability and bias of the annotation method used to extract ecological data. We investigated variability and bias in two common annotation methods for estimating organism cover, and the role of size selectivity in this variability. Eleven annotators estimated sparse cold-water coral cover in the same 96 images with both grid-based and manual segmentation annotation methods. The standard deviation between annotators was three times greater in the grid-based method compared to segmentation, and grid-based estimates from annotators tended to overestimate coral cover. Size selectivity biased the manual segmentation; the minimum size of colonies segmented varied between annotators fivefold. Two modelling techniques (based on Richard’s selection curves and Gaussian processes) were used to impute areas where annotators identified colonies too small for segmentation. By imputing small coral sizes in segmentation estimates, the coefficient of variation between annotators was reduced by approximately 10%, and method bias (compared to a reference dataset) was reduced by up to 23%. Therefore, for sparse, low cover organisms, manual segmentation of images is recommended to minimise annotator variability and bias. Uncertainty in cover estimates may be further reduced by addressing size selectivity bias when annotating small organisms in images using a data-driven modelling technique.
Conservation, Data imputation, Environmental monitoring, Image annotation, Ocean floor, Underwater photography
103214
Curtis, Emma J.
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Durden, Jennifer M.
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Bett, Brian J.
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Huvenne, Veerle A.I.
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Piechaud, Nils
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Walker, Jenny
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Albrecht, James
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Massot-Campos, Miguel
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Yamada, Takaki
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Bodenmann, Adrian
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Cappelletto, Jose
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Strong, James A.
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Thornton, Blair
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March 2024
Curtis, Emma J.
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Durden, Jennifer M.
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Bett, Brian J.
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Huvenne, Veerle A.I.
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Piechaud, Nils
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Walker, Jenny
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Albrecht, James
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Massot-Campos, Miguel
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Yamada, Takaki
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Bodenmann, Adrian
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Cappelletto, Jose
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Strong, James A.
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Thornton, Blair
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Curtis, Emma J., Durden, Jennifer M., Bett, Brian J., Huvenne, Veerle A.I., Piechaud, Nils, Walker, Jenny, Albrecht, James, Massot-Campos, Miguel, Yamada, Takaki, Bodenmann, Adrian, Cappelletto, Jose, Strong, James A. and Thornton, Blair
(2024)
Improving coral monitoring by reducing variability and bias in cover estimates from seabed images.
Progress in Oceanography, 222, , [103214].
(doi:10.1016/j.pocean.2024.103214).
Abstract
Seabed cover of organisms is an established metric for assessing the status of many vulnerable marine ecosystems. When deriving cover estimates from seafloor imagery, a source of uncertainty in capturing the true distribution of organisms is introduced by the inherent variability and bias of the annotation method used to extract ecological data. We investigated variability and bias in two common annotation methods for estimating organism cover, and the role of size selectivity in this variability. Eleven annotators estimated sparse cold-water coral cover in the same 96 images with both grid-based and manual segmentation annotation methods. The standard deviation between annotators was three times greater in the grid-based method compared to segmentation, and grid-based estimates from annotators tended to overestimate coral cover. Size selectivity biased the manual segmentation; the minimum size of colonies segmented varied between annotators fivefold. Two modelling techniques (based on Richard’s selection curves and Gaussian processes) were used to impute areas where annotators identified colonies too small for segmentation. By imputing small coral sizes in segmentation estimates, the coefficient of variation between annotators was reduced by approximately 10%, and method bias (compared to a reference dataset) was reduced by up to 23%. Therefore, for sparse, low cover organisms, manual segmentation of images is recommended to minimise annotator variability and bias. Uncertainty in cover estimates may be further reduced by addressing size selectivity bias when annotating small organisms in images using a data-driven modelling technique.
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PREPROOF_Curtis_2024 - Improving coral monitoring by reducing variability and bias in cover estimates from seabed images
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Accepted/In Press date: 31 January 2024
e-pub ahead of print date: 6 February 2024
Published date: March 2024
Additional Information:
Many thanks to the captain and crew of the RRS Discovery DY108/109 and the Autosub6000 team. We also thank the members of the University of Southampton and the National Oceanography Centre who provided the image annotations for this study. This research was funded by the UK Natural Environment Research Council’s Oceanids programme (NE/P020887/1 and NE/P020739/1). Curtis was funded by the UK Natural Environment Research Council’s INSPIRE programme (NE/S007210/1). Durden, Bett and Huvenne were also funded by the Climate Linked Atlantic Sector Science (CLASS) project supported by the UK Natural Environment Research Council’s National Capability funding (NE/R015953/1). Huvenne enjoyed a Fellowship from the Hanse-Wissenschaftskolleg Institute for Advanced Study during the final preparation stages of this manuscript. Data used in this article are available on request on the online benthic imaging repository Squidle+ (www.soi.squidle.org).
Publisher Copyright:
© 2024
Keywords:
Conservation, Data imputation, Environmental monitoring, Image annotation, Ocean floor, Underwater photography
Identifiers
Local EPrints ID: 487184
URI: http://eprints.soton.ac.uk/id/eprint/487184
ISSN: 0079-6611
PURE UUID: 13b8ffbb-d775-4bb7-a2e1-e277ddb12f4e
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Date deposited: 15 Feb 2024 17:30
Last modified: 21 May 2024 01:59
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Contributors
Author:
Jennifer M. Durden
Author:
Brian J. Bett
Author:
Veerle A.I. Huvenne
Author:
Nils Piechaud
Author:
Jenny Walker
Author:
James Albrecht
Author:
Takaki Yamada
Author:
Jose Cappelletto
Author:
James A. Strong
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