Coral reefs are undergoing a severe decline due to ocean acidification, seawater warming and anthropogenic eutrophication. We demonstrate the applicability of Deep Learning (DL) for following these changes. We examined the distribution and frequency appearance of the eleven most common coral species at four sites in the Gulf of Eilat. We compared deep learning with conventional census methods. The methods used in this research were natural sampling units via photographing the coral reef, line transects for estimating the cover percentage at the four test sites and deep convolutional neural networks, which proved to be an efficient sparse classification for coral species using the supervised deep learning method. The main research goal was to identify the common coral species at four test sites in the Gulf of Eilat, using DL to detect differences in coral cover and species composition among the sites, and relate these to ecological characteristics, such as depth and anthropogenic disturbance. The use of this method will produce a vital database to follow changes over time in coral reefs, identify trend lines and recommend remediation measures accordingly. We outline future monitoring needs and the corresponding system developments required to meet these.
Bibliographical noteFunding Information:
The authors thank Irit Shoval from the Scientific Equipment Center of the Faculty of Life Sciences at Bar-Ilan University for helping with the data analysis and processing of the results. We also thank Jennifer Benichou Israel Cohen from the Statistics Unit at Bar-Ilan University for helping with presentation of the results and preparation for statistical analyses. We thank Sharon Victor for her help with formatting the manuscript. No funding was received for this research.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Coral reef
- Coral species
- Deep learning
- Machine learning and networks
- Marine ecosystem