Improving coral monitoring by reducing variability and bias in cover estimates from seabed images

Curtis, Emma J.; Durden, Jennifer ORCID:; Bett, Brian J. ORCID:; Huvenne, Veerle A.I. ORCID:; Piechaud, Nils; Walker, Jenny; Albrecht, James; Massot-Campos, Miquel; Yamada, Takaki; Bodenmann, Adrian; Cappelletto, Jose; Strong, James A. ORCID:; Thornton, Blair. 2024 Improving coral monitoring by reducing variability and bias in cover estimates from seabed images. Progress in Oceanography, 222, 103214.

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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.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 00796611
Additional Keywords: Underwater photography, Image annotation, Data imputation, Environmental monitoring, Conservation, Ocean floor
Date made live: 21 Feb 2024 11:48 +0 (UTC)

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