Quantification of Harmful Algal Blooms in Multiple Water Bodies of Mississippi Using In-Situ, Analytical and Remote Sensing Techniques

Globally, water bodies are increasingly affected by undesirable harmful algal blooms. This dissertation contributes to research methodology pertaining to quantification of the algal blooms in multiple water bodies of Mississippi using in situ, analytical, and remote sensing techniques. The main objectives of this study were to evaluate the potential of several techniques for phytoplankton enumeration and to develop remote sensing algorithms for several sensors and evaluate the performance of the sensors for quantifying phytoplankton in several water bodies. Analytical techniques such as “FlowCam”, an imaging flow cytometer; “HPLC”, high performance liquid chromatography with the chemical taxonomy program “ChemTax”; spectrofluorometric analyses; and “ELISA” assay were used to quantify a suite of parameters on algal blooms. Additionally, in-situ algal pigment biomass was measured using fluorescence probes. It was found that that each technique has unique potential. While some of the rapid and simpler techniques can be used instead of more involved techniques, sometimes use of several techniques together is beneficial for managing aquatic ecosystems and protecting human health. Algorithms were developed to quantify chlorophyll a using five remote sensing sensors including three currently operational satellite sensors and two popular sensors onboard the Unmanned Aerial Systems (UASs). Empirical band ratio algorithms were developed for each sensor and the best algorithms were chosen. Cluster analysis helped in differentiating the water types and linear regression was used to develop algorithms for each of the water types. The UAS sensor- Micasense was found to be most useful among the UAS sensors and the best overall with highest R2 value 0.75 with p\u3c0.05 and minimum %RMSE of 28.22% and satellite sensor OLCI was found to be most efficient among the three satellite sensors used in the study for chlorophyll a estimation with R2 of 0.75 with p\u3c0.05 and %RMSE 13.19%. The algorithms developed for these sensors in this study represent the best algorithms for chlorophyll a estimation in these water bodies based on R2 and %RMSE. The applicability of the algorithms can be extended to other water bodies directly or the approach developed in this study can be adopted for estimating Chl a in other water bodies