Document Type
Article
Publication Date
9-21-2024
Abstract
Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground truth data from Umluj. The classifier was then applied to Al Wajh, demonstrating its robustness across different sites and times. Results indicated a significant decline in coral cover: 11.4 % in the Gulf of Aqaba, 3.4 % in Umluj, and 13.6 % in Al Wajh. This study highlights the importance of continuous monitoring using generalized classifiers to mitigate the impacts of environmental changes on coral reefs.
Recommended Citation
J.J. Gapper, S. Maharjan, W. Li, E. Linstead, S.P. Tiwari, M.A. Qurban, H. El-Askary, A generalized machine learning model for long-term coral reef monitoring in the Red Sea, Heliyon 10 (2024) e38249, https://doi.org/10.1016/j.heliyon.2024.e38249
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This work is licensed under a Creative Commons Attribution 4.0 License.
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Artificial Intelligence and Robotics Commons, Climate Commons, Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Marine Biology Commons, Oceanography Commons
Comments
This article was originally published in Heliyon, volume 10, issue 18, in 2024. https://doi.org/10.1016/j.heliyon.2024.e38249