Abstract
As human populations increase along coastal watersheds, the understanding and monitoring of Harmful Algal Blooms (or red tides) is an increasingly important issue. A consistent method for accurately detecting red tides using satellite measurements would bring tremendous societal benefits to resource managers, the scientific community and to the public as well. In the West Florida Shelf, blooms of the toxic dinoflagelate Karenia brevis are responsible for massive red tides causing fish kills, massive die-offs of marine mammals, shellfish poisoning, and acute respiratory irritation in humans. In this work, for the first time a long-term dataset (2002~2006) the MODerate Resolution Imaging Spectroradiometer (MODIS) is compared (i.e., matched-up) to an extensive data set of in situ cell counts of K. brevis; provided by the Florida Fish and Wildlife Conservation Commission's Fish and Wildlife Research Institute. The pairing of remote sensing data with near-coincident field measurements of cell abundance was successfully used to derive the basis for the development of an alternative ocean color based algorithm for detecting the optical signatures associated with blooms of K. brevis in waters of the West coast of Florida. Conclusions are geographically limited to the Central West Florida Shelf during the boreal Summer-Fall (i.e., the K. brevis blooming season). The new simpler Empirical approach is compared with other two more complicated published techniques. Their potential is verified and uncertainties involved in the identification of blooms of K. brevis are presented. The results shown here indicate that the operational NOAA method for detecting red tides in the Gulf of Mexico (Stumpf et al., 2003; Tomlinson et al., 2004) performs less accurately than the other two algorithms at identifying K. brevis blooms. The sensitivity and specificity of the Bio-optical (Cannizzaro, 2004; Cannizzaro et al., 2008) and Empirical algorithms are simultaneously maximized with an optimization procedure. The combined use of these two optimized algorithms in sequence provides another new monitoring tool with improved accuracy at detecting K. brevis of blooms. The ability of this Hybrid scheme ranges about 80% for both sensitivity and specificity; and the capability at predicting a correct red tides is 70%, and ~85% for non-blooms conditions. The spatial and temporal knowledge of K. brevis blooms can improve the direction of field monitoring to areas that should receive special attention, allowing better understanding of the red tide phenomenon by the scientific community. The relevant agencies can also develop more appropriate mitigation action plans, and public health guidance can be improved with the enhancement of sustainable coastal management strategies.