Abstract

Remote sensing has been used as an effective chlorophyll-a detection method in inland lakes and reservoirs. Concentration estimates of chlorophyll-a approximate the amounts of algae and phytoplankton in a body of water, can indicate the existence of large blooms and high nutrient loading, and can be used as an indicator of water quality. These biomasses pose potential threats to the quality of the water and the local environment by depleting oxygen, influencing the taste of the drinking water and detrimentally affecting aesthetics and recreation. Deer Creek Reservoir exhibited eutrophic tendencies in the early 1990's, caused by phosphorus pollution. This was made evident by accelerated algae growth. Following remediation efforts, Deer Creek Reservoir, as well as nearby Jordanelle Reservoir have been closely monitored with regular field sampling. These field data have been used to develop remote sensing methods using Landsat images to provide supplementary information for reservoir management. These remote sensing methods allow for mapping of the distribution of chlorophyll-a, which provides spatial distribution average, and maximum estimates of chlorophyll-a concentrations, data and information that are not feasible with in-field sampling. In this thesis, traditional methods for remote sensing models are discussed, and a novel sub-seasonal approach based on seasonal algal succession is proposed and demonstrated. Each seasonal model is created using a standard stepwise regression using historic field data and the associated Landsat images and is statistically tested for leverage to ensure unbiased model development. These sub-seasonal detection models are applied to 5 reservoirs in the central-Utah region to provide a more comprehensive description of reservoir behavior and water quality trends over the past 30 years. Historic trends of the average and maximum chlorophyll-a estimates are provided for each of the reservoirs. Example color maps are presented to demonstrate the ability of remote sensing to represent the spatial distribution of algae (using chlorophyll as an indicator). Limitations for this approach are discussed, as well as applications for remotely sensed water quality data on a regional scale.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering

Rights

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