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Abstract
The application of statistical models in a remote sensing field is an indispensable tool. The main purpose of this study was to develop an empirical model to detect algal blooms phenomenon in the Techi reservoir, Taiwan. We used ratios of logarithm transformed radiance values from Landsat Thematic Mapper (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% 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 model accuracy.
Acknowledgments
We thank Dr Bo-Jein Kuo for his kind assistance with the statistical model and review of the manuscript. This study was supported by a grant from the Water Resources Agency, Ministry of Economic Affairs, Taichung, Taiwan (MOEA/WRB 900029V4 ).
