Differentiation of Harmful Algal Bloom Signatures in the Indian River Lagoon by Remote Sensing

Located along the east coast of Florida, the Indian River Lagoon (IRL) is a shallow-water estuary which stretches along 250 km of coastline. Over the last few decades, freshwater flowing into areas of the lagoon has transported high concentrations of nitrogen and phosphorus runoff from agriculture and sewage. Nutrient-rich waters flowing into the IRL have driven eutrophication and growth of various types of harmful algal blooms (HABs). Previously, remote sensing research has focused on monitoring water quality by identifying the spectral characteristics of color producing agents (CPAs) associated with HABs by using ocean color chlorophyll-a algorithms. The ability to quickly and reliably distinguish CPAs of HABs, color dissolved organic matter, and terrigenous sediment in water over large areas with remote sensing methods has become critically important for monitoring regional water quality. Recent statistical techniques for processing Landsat 8 imagery have expanded retrievals beyond chlorophyll-a and corrected for common atmospheric interferences. The Kent State University spectral decomposition method, a type of Varimax-rotated Principal Component Analysis (VPCA), is used to process visible reflectance spectra (400-700 nm) from multispectral and hyperspectral imaging systems. The VPCA decomposition describes the total percentage of variability of CPAs mixed in the water column and determines the leading spectral components of the satellite image that contribute to the overall signal. We identify these leading spectral components obtained from this analysis with lab and field measured reflectance spectra, such as from the brown tide algae, Aureoumbra lagunensis, to qualitatively assess areas of the IRL which have relatively high or low proportions of suspended sediment or CPAs over time. Preliminary results using this method, have identified a component associated with Aureoumbra lagunensis within the Banana River region of the IRL.