There are many approaches to detecting in-water constituents, like color producing agents, in the field of remote sensing. Previously, harmful algal bloom (HAB) monitoring practices via satellite imagery analysis have held a similar goal of identifying a single constituent associated with HAB’s, particularly chlorophyll. Recently, the Kent State University Spectral Decomposition Method has been developed to better distinguish multiple water constituents, such as phylum level Cyanobacteria, Chlorophyta, Bacillariophyta, and Ochrophyta, as well as constituents of HAB’s, color dissolved organic matter (CDOM), and sediment within large water bodies. Using this technique, we can more effectively monitor HAB’s by separating mixed water signals using a varimax-rotated principal component analysis to remotely detect in-water constituents including HAB-causing cyanobacteria. The KSU Spectral Decomposition Method has been successful using sensors such as the Malvern Panalytical Fieldspec HH2, the NASA Glenn second-generation hyperspectral imagery (HSI2), MODIS, Landsat 8 OLI, and Sentinel 3A/B OLCI. It is apparent that better monitoring practices make better management practices possible, and our goal is to provide a method that will trailblaze the path to better water management practices globally. Case studies in Guantanamo Bay, Cuba and Lake Okeechobee, Florida are presented to document the success of the KSU Spectral Decomposition Method.
Remote Sensing of Cyanobacterial and Harmful Algal Blooms in Lake Okeechobee and Biscayne Bay, Florida03/21/2019
Cyanobacterial and Harmful Algal Blooms (CyanoHABs) have become a major topic of concern for homeowners and environmental groups in Florida, with blooms occurring in both Lake Okeechobee and Biscayne Bay in prior years. While Biscayne Bay and Lake Okeechobee are distinct water bodies, with different manifestations of the blooms, in both environments CyanoHABs can contain toxins that are harmful to humans and animals, can lead to fish and wildlife kills, as well as disrupt ecosystems. Furthermore, recreational and economic use of the waters of Biscayne Bay and Lake Okeechobee are negatively impacted by these blooms. Monitoring and assessment of the CyanoHABs in both water bodies is a vital aspect of understanding the drivers and impacts of CyanoHAB growth in Florida. Spectral decomposition of satellite remote sensing images of Lake Erie has been shown to be effective at discriminating between in-water constituents, both those related to CyanoHABs, and those that are non-HAB forming. Here we show that the KSU spectral decomposition method is also successful in identifying in-water constituents in Florida waters using images from the Sentinel 3A- Ocean and Land Color Instrument, acquired on 16 July 2017 and 28 July 2018. We identify the CyanoHAB signal in Lake Okeechobee on both days, as well as the sediment and algal signal in Biscayne Bay.