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Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach

Authors :
Manuel González-Rivero
Oscar Beijbom
Alberto Rodriguez-Ramirez
Dominic E. P. Bryant
Anjani Ganase
Yeray Gonzalez-Marrero
Ana Herrera-Reveles
Emma V. Kennedy
Catherine J. S. Kim
Sebastian Lopez-Marcano
Kathryn Markey
Benjamin P. Neal
Kate Osborne
Catalina Reyes-Nivia
Eugenia M. Sampayo
Kristin Stolberg
Abbie Taylor
Julie Vercelloni
Mathew Wyatt
Ove Hoegh-Guldberg
Source :
Remote Sensing, Vol 12, Iss 3, p 489 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

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.

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.5f24218fa39a45d0a0528172d18634b9
Document Type :
article
Full Text :
https://doi.org/10.3390/rs12030489