| Summary: | 博士 === 國立中興大學 === 生命科學系 === 91 === Abstract
The microscopic planktonic algae of the world’s oceans are critical food for filter-feeding bivalve shellfish such as, oyster, mussels, scallops and clams as well as the larvae of commercially important crustaceans and finfishes. Some microscopic planktonic algae have the capacity to produce potent toxins that may be transfered through fish and shellfish to humans. On a global base, near 2000 cases of human food poisonings (15% mortality) from consumption of fish or shellfish consumption are reported each year. If not controlled, the economic damage through reduced local consumption and reduced seafood product exports can be considerable. Whales and porpoises can also become victims when they receive toxins through the food chain via contaminated zooplankton or fish.
From water quality investigations in the past, dinoflagellate occur blooms are known frequently in the Techi reservoir in summer. Past algal bloom investigations at the Techi reservoir were conducted using boats and point sampling. Sometimes, the data can not represent truly the large areas due to time lacking and few samples per area are available. It is therefore a need of tool that can monitor algal blooms in large areas.
Remote sensing data can acquire temporal, large spatial and vast spectral data and also track the past data. Remote sensing, which quantitatively measures the light reflected from the surface of the earth, is a powerful tool for studying regional-scale dynamic ecosystem of aquatic environment. Landsat TM data was used to monitor dinoflagellate blooms in the Techi reservoir with supervised classification in this study. The results afforded us comprehensive algal bloom information of the Techi reservoir instead of the conventional point data.
This thesis is divided into three parts. First, Landsat TM data was used to monitor dinoflagellate blooms in the Techi reservoir with supervised classification in 1995 and the predicted accuracies for algal blooms reached higher than 87.5%. From the classification results, dinoflagellate blooms were predicted the most frequent in summer and the least in winter. The phenomenon was associated with the catchments management. The catchments are located on the upper stream of the Techi reservoir. The results afforded us the entire algal bloom information for the Techi reservoir instead of the conventional point data. The algal bloom areas and degree of potential seriousness were defined in this study.
Second, we used ratios of logarithm transformed radiance values from Landsat TM data to establish statistical relationships to dinoflagellate densities. The procedure used a forward selection method to develop multiple linear regression models. The selected independent variables matched the dinoflagellate algal cell densities to build the bloom prediction model. The result showed that the bloom prediction model can predict the algal bloom phenomenon with 74.07 % accuracy in this study. The major limits were the spectral sensitivity and spatial resolution of the scanning device. If we can acquire greater spectral sensitivity and spatial resolution in the remote sensing data, we can attain higher accuracy of model analysis.
Chlorophyll a is the common pigment in algae, so we can use the Chlorophyll a concentrations as the index of algal bloom phenomenon. Last, we use the hyperspectral data of algal suspensions, algal pigments crude extra and Chlorophyll a concentrations to established the prediction model of Chlorophyll a concentrations. So we can use the Chlorophyll a concentrations to make a judgment of algal bloom phenomenon happen or not.
From the research of this thesis, we can find that the remote sensing is a powerful tool in monitoring the algal bloom phenomenon in the Techi reservoir.
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