A generalized machine learning model for long-term coral reef monitoring in the Red Sea
Justin J. Gapper,
Surendra Maharjan,
Wenzhao Li,
Erik Linstead,
Surya P. Tiwari,
Mohamed A. Qurban,
Hesham El-Askary
Affiliations
Justin J. Gapper
Earth Systems Science and Data Solutions Lab, Chapman University, Orange, CA, 92866, USA
Surendra Maharjan
Earth Systems Science and Data Solutions Lab, Chapman University, Orange, CA, 92866, USA
Wenzhao Li
Earth Systems Science and Data Solutions Lab, Chapman University, Orange, CA, 92866, USA; Schmid College of Science and Technology, Chapman University, Orange, CA, 92866, USA
Erik Linstead
Fowler School of Engineering, Chapman University, Orange, CA, 92866, USA
Surya P. Tiwari
Center for Environment and Water, The Research Institute, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia
Mohamed A. Qurban
National Center for Wildlife, Saudi Arabia
Hesham El-Askary
Earth Systems Science and Data Solutions Lab, Chapman University, Orange, CA, 92866, USA; Schmid College of Science and Technology, Chapman University, Orange, CA, 92866, USA; Department of Environmental Sciences, Faculty of Science, Alexandria University, Moharem Bek, Alexandria, 21522, Egypt; Corresponding author. Earth Systems Science and Data Solutions Lab, Chapman University, Orange, CA, 92866, USA.
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.
