A series of the Orbview-2/SeaWiFS (Sea-viewing Wide Field-of-view Sensor) images during the period from 1997
to 2003 is used to understand the spatial and temporal distribution of the chlorophyll-a concentration (Chl-a) in the
Taiwan Strait (TS). It is found that the area with higher Chl-a is mainly along the western TS; it extends more offshore in
cold seasons. The lowest Chl-a is always inside the deep Peng-Hu Channel, it can spread further northward in summer.
From mode 1 results of the Empirical Orthogonal Function (EOF) analysis, we find the Chl-a in La Nina years (during
the period from June 1998 to May 2001) showing greater variation than the other El Nino or normal years. The EOF1
results also indicate the highest Chl-a always in fall. Meanwhile, the peak in the 1997/1998 El Nino fall was the lowest
maximum, while the lowest Chl-a is mainly in winter, but its interannual variation is not so clear.
Synthetic Aperture Radar (SAR) images from European Remote Sensing (ERS) satellites are used to investigate oil spill from ship navigations in the water adjacent to Taiwan. A total number of 136 images taken from 1993 to 1997 are used in this study. On the 136 images, only 46 images showing the possibility of oil spill which are based on the position and the shape of the discharge, the path of the ship, the sea characteristics of the area, and the weather conditions. The result shows that oil spill occurs most frequently in spring and least in winter. The sea area off eastern Taiwan has a probability which far surpassed other areas, followed by the middle sea area, the northern sea area, and the southern sea area. Regarding the oil spills at different areas with the distance to the shore, the oil spills at the middle area, with an average distance of 50 km (28 nautical miles), is closer than those at other areas. The statistical analysis demonstrates that the oil spill around Taiwan mostly occurs over 44 km (24 nautical miles) away from shore. Therefore, it is obvious that the probability of oil spills occurring as a ship leaves or enters the harbor is not high. Instead, the majority of oil spills takes place from middle to long distance navigating fishing boats as well as from oil and cargo freighters navigating international waterways.
The multilayer perceptron (MLP) neural network have been widely used to fit non-linear transfer function and performed well. In this study, we use MLP to estimate chlorophyll-a concentrations from marine reflectance measures. The optical data were assembled from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm Mini-workshop (SeaBAM). Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis. Regression analysis has limitations for nonlinear function. Neural network, however, have been shown better performance for nonlinear problems. The result showed that accuracy of chlorophyll-a concentration using MLP is much higher than that of regression method. Nevertheless, using all of the five bands as input can derive the best performance. The results showed that each band could carry some useful messages for ocean color remote sensing. Only using band ratio (OC2) or band switch (OC4) might lose some available information. By preprocessing reflectance data with the principle component analysis (PCA), MLP could derive much better accuracy than traditional methods. The result showed that the reflectance of all bands should not be ignored for deriving the chlorophyll-a concentration because each band carries different useful ocean color information.