Xu, H.; Liu, Z.; Zhu, J.; Lu, X., and Liu, Q., 2019. Classification of coral reef benthos around ganquan island using WorldView-2 satellite imagery. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 466–474. Coconut Creek (Florida), ISSN 0749-0208.

Coral reefs are structures formed by animals known as stony corals, which have an important impact on the physical and ecological environment of their surroundings. Presently, coral reefs are severely damaged by a variety of human activities and by global climate change. This paper is intended to explore the potential of using WorldView-2 (WV2) satellite images for coral reef bottom types classification. The research area for this study is around Ganquan Island in the South China Sea. Sunny sky and cloudless WV2 satellite imagery is selected to reduce atmospheric impact and improve the accuracy of coral reef classification. For the preprocessed imagery data, training samples were manually established, and the seafloor reef bottom types in the sea near Ganquan Island were divided into five substratum categories: coral reef, sand, reef, spray and deep sea water. These are classified using a Support Vector Machine (SVM), a Neural Network and the Maximum Likelihood method. The classification results were verified by comparing the satellite image visual interpretation classification results and the field seafloor image data. Confusion matrixes was compared and the classification accuracy was evaluated. The results show that the classification based on the SVM method is the best, with a total classification accuracy of 93.33% and a Kappa coefficient of 0.89. These results prove that high-resolution images can provide detailed and accurate information for coral reef bottom types classification and thus provide a scientific basis and reliable data for the establishment of a more complete coral reef ecological monitoring system and marine ecological environmental protection system.