Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

Remote sensing is the main approach used to classify and map aquatic vegetation, and classification tree (CT) analysis is superior to various classification methods. Based on previous studies, modified CT can be developed from traditional CT by adjusting the thresholds based on the statistical relationship between spectral features to classify different images without ground-truth data. However, no studies have yet employed this method to resolve marine vegetation. In this study, three Gao-Fen 1 satellite images obtained with the same sensor on January 30, 2014, November 5, 2014, and January 21, 2015 were selected, and two features were then employed to extract macroalgae from aquaculture farms from the seawater background. Besides, object-based classification and other image analysis methods were adopted to improve the classification accuracy in this study. Results show that the overall accuracies of traditional CTs for three images are 92.0%, 94.2% and 93.9%, respectively, whereas the overall accuracies of the two corresponding modified CTs for images obtained on January 21, 2015 and November 5, 2014 are 93.1% and 89.5%, respectively. This indicates modified CTs can help map macroalgae with multi-date imagery and monitor the spatiotemporal distribution of macroalgae in coastal environments.