Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree |
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| Authors: | Yuhan Zheng Carlos M Duarte Jiang Chen Dan Li Zhaohan Lou |
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| Institution: | 1. Ocean College, Zhejiang University, Zhoushan, China;2. Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, Zhejiang University, Zhoushan, China;3. Red Sea Research Center (RSRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia;4. Oceans Institute, The University of Western Australia, Crawley, Australia;5. College of Environment and Natural Resources, Zhejiang University, Hangzhou, China |
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| Abstract: | Remote sensing is the main approach to map aquatic vegetation, and classification tree (CT) 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 on 30 January 2014, 5 November 2014 and 21 January 2015 were selected, and two features were then employed to extract macroalgae farms. Results show that the overall accuracies of traditional CTs for three images are 92.0, 94.2 and 93.9%, respectively, whereas those of the two corresponding modified CTs for images obtained on 21 January 2015 and 5 November 2014 are 93.1 and 89.5%, respectively. This indicates modified CTs can map macroalgae with multi-date imagery and monitor their spatiotemporal distribution in coastal environments. |
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| Keywords: | Macroalgae modified classification tree GF-1 classification accuracy |
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