Date of Award
College of Science
Type of Degree
Dr. Anne Axel, Committee Chairperson
Dr. Gary Schultz
Mr. Steve Foster
Traditional direct water quality methodologies limit the ability to spatially and temporally predict algal blooms in lotic systems due to the size and characteristics of large river systems. Algal blooms potentially can be predicted by knowing the spatial and temporal patterns of change in cyanobacteria concentrations at large scales. Remote sensing studies investigating freshwater algal blooms, some known to secrete harmful toxins, are primarily conducted on lentic systems while large lotic systems are greatly ignored. In this study I developed a chlorophyll concentration estimation model for the Ohio River using a satellite remote sensing approach. Ground-truth water quality measures, including temperature, dissolved oxygen, turbidity, as well as chlorophyll concentrations, were obtained through hand-samples on days the satellite flew over the study area. Concentrations of chlorophyll were correlated with spectral signatures from Landsat-8 OLI satellite imagery. Then a predictive model was developed using two bands of Landsat 8 to predict chlorophyll a and the generated model has an R2 = 0.879 (Adj. R2 = 0.819) and a p-value = 0.015. Two other models were generated for estimating both chlorophyll a & b and total chlorophyll; however, the models were not as robust, R2 = 0.801 (Adj. R2 = 0.603), p-value = 0.141 and R2 = 0.764 (Adj. R2 = 0.528), p-value = 0.18, respectively.
Ohio River Watershed -- Environmental conditions.
Algal blooms -- Monitoring.
Tuggle, Thaddaeus Stephen, "Modeling Chlorophyll Concentrations on the Ohio River using Remotely Sensed Data" (2018). Theses, Dissertations and Capstones. 1174.
Environmental Health Commons, Marine Biology Commons, Terrestrial and Aquatic Ecology Commons