Monitoring of coral reefs using artificial intelligence: a feasible and cost-effective approach

González-Rivero, Manuel, Beijbom, Oscar, Rodriguez-Ramirez, Alberto, Bryant, Dominic E.P., Ganase, Anjani, Gonzalez-Marrero, Yeray, Herrera-Reveles, Ana, Kennedy, Emma V., Kim, Catherine J.S., Lopez-Marcano, Sebastian, Markey, Kathryn, Neal, Benjamin P., Osborne, Kate, Reyes-Nivia, Catalina, Sampayo, Eugenia M., Stolberg, Kristin, Taylor, Abbie, Vercelloni, Julie, Wyatt, Mathew, and Hoegh-Guldberg, Ove (2020) Monitoring of coral reefs using artificial intelligence: a feasible and cost-effective approach. Remote Sensing, 12 (3). 489.

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Abstract

Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas.

Item ID: 64813
Item Type: Article (Research - C1)
ISSN: 2072-4292
Keywords: coral reefs; monitoring; artificial intelligence; automated image analysis
Copyright Information: ©2020 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Funders: AXA XL, Australian Research Council (ARC), Vulcan Inc
Projects and Grants: XL Catlin Seaview Survey, ARC Laureate, ARC Centre for Excellence
Date Deposited: 02 Nov 2020 19:33
FoR Codes: 31 BIOLOGICAL SCIENCES > 3103 Ecology > 310305 Marine and estuarine ecology (incl. marine ichthyology) @ 60%
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 40%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960507 Ecosystem Assessment and Management of Marine Environments @ 100%
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