Dissertations and Theses @ UNI
Award Winner
Recipient of the 2007 Outstanding Master's Thesis Award - Second Place.
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Availability
Open Access Thesis
Keywords
Water quality--Iowa--Measurement--Remote sensing; Water--Pollution--Iowa--Measurement--Remote sensing;
Abstract
The U.S. Environmental Protection Agency (US EPA) has identified more than 20,000 water bodies across America as polluted mainly due to agricultural non-point source pollution. Current techniques for monitoring and assessing the quality of waters in streams, reservoirs, lakes, and estuaries involve on site measurements and/or the collection of water samples for subsequent laboratory analyses. While this approach yields accurate measurements for a point in time and space, it is expensive, laborious, and cannot provide a temporal or spatial overview of water-quality trends. To overcome this liability, state and local agencies are seeking alternative and more cost effective methods. A study was performed during the summer and fall of 2004 on two lakes in Eastern Iowa, Silver Lake and Casey Lake. The goal of the research was to explore whether the combination of hyperspectral remote sensing, GIS, and water sample analysis can simplify and accelerate the protocol for assessing water quality in Iowa lakes with an acceptable degree of accuracy. Hyperspectral images were collected using an airborne platform during the first week of the months from June through October 2004. Water samples and GPS points were collected nearly simultaneously with the hyperspectral images. By analyzing relationships between reflectance at specific wavelengths and water sample data, prediction algorithms were created. The prediction algorithms were applied to the hyperspectral images to create spatially continuous water quality maps. Maps of chlorophyll a were created for both lakes with an accuracy of R squared = 0.7672. The chlorophyll a maps showed highest concentrations of chlorophyll a in early July and lowest in September and October. Maps of turbidity were created for both lakes also with an accuracy of R squared= 0.7620. The highest turbidity occurred in early July and the lowest occurred in September. Secchi disk depth maps were created for Silver Lake with an accuracy of R squared = 0.8888. The smallest Secchi disk depths occurred in June and July and increased into October. Secchi disk depths were unable to be predicted in Casey Lake due to the clarity of the water and bottom reflectance being collected by the hyperspectral sensor. Examination of the water quality maps made from the hyperspectral images provides a more complete record of the trend in water quality for these two lakes over the study period. They also supply a more detailed understanding of the spatial and temporal variation in the water quality of the two lakes.
Year of Submission
2005
Year of Award
2007 Award
Degree Name
Master of Arts
Department
Department of Geography
First Advisor
Ramanathan Sugumaran, Chair, Thesis Committee
Date Original
12-2005
Object Description
1 PDF file (xi, 104 pages)
Copyright
©2005 Nathan Robert Green
Language
en
File Format
application/pdf
Recommended Citation
Green, Nathan Robert, "Monitoring spatial and temporal water quality changes in Iowa lakes using airborne hyperspectral imagery" (2005). Dissertations and Theses @ UNI. 583.
https://scholarworks.uni.edu/etd/583
Comments
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