博士 / 國立臺灣海洋大學 / 海洋環境資訊學系 / 96 / The purpose of this dissertation is to present a new method for estimating amplitudes of internal waves (IWs) in ocean color imagery. The method is tested in the northern South China Sea (SCS), because the environmental conditions there are such that IWs are generated frequently and therefore can be observed easily. The relationship between satellite derived chlorophyll concentration (Chl) and the amplitudes of IWs is obtained. However the variation of Chl is small when an IW passes through, an artificial neural network (ANN) model with a bipartite classification scheme is derived to increase accuracy of Chl retrieval. The bipartite ANN (BANN) model can significantly improve the accuracy of Chl especially in high Chl region. The model also performs well in a test with in situ measurements. The biases induced by errors in atmospheric correction are also reduced in the coastal water. Using a deep chlorophyll maximum (DCM) model of IWs, the amplitude at the light penetration depth can be estimated. Furthermore, from theoretical models, the amplitudes of IWs at the depth where the maximum vertical isopycnal displacement occurs is also derived. The results indicate that the amplitudes of IWs in the northern SCS determined by the method developed in this study are comparable with historical regional data.
| Identifer | oai:union.ndltd.org:TW/096NTOU5282003 |
| Date | January 2008 |
| Creators | Feng-Chun Su, 蘇蜂鈞 |
| Contributors | Chung-Ru Ho, 何宗儒 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 121 |
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