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Analysis of Coincident HICO and Airborne Hyperspectral Images Over Lake Erie Western Basin HABs

Cline, Michael T, Jr.
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470324418

Abstract Details

2016, Master of Science, University of Toledo, Geology.
Harmful algal blooms (HABs) produce waterborne toxins that pose a significant threat to people, livestock, and wildlife. Nearly 40 million people in both Canada and the U.S. depend on Great Lakes water. In the summer of 2014, in the Lake Erie Western Basin, an HAB of the cyanobacteria Microcystis was so severe that a do-not-drink advisory was in effect for the greater Toledo area, Ohio. This advisory applied to the water supply to over 400,000 people from a single water intake. Bloom intensity, composition, and spatial variability were investigated by comparing coincidental hyperspectral data from NASA's HICO, and NASA GRC’s HSI airborne sensor, with on-lake ASD radiometer measurements and in situ water quality testing as ground reference data. Coincident data sets were obtained with HICO only on one day in 2014, however all other datasets coincide four times in 2015. Remote sensing data were atmospherically corrected using the empirical line method, utilizing dark reference spectra from a nearby asphalt parking lot measured from ASD and HSI radiometers. Cyanobacteria Index (CI) images were created from processed images using the Wynne (2010) algorithm, previously used for MODIS and MERIS imagery. This algorithm-generated CI images provide reliable results for both ground level (R²=0.921), airborne (R²=0.7981), and satellite imagery (R²=0.7794) for seven sampling points. The ability to robustly atmospherically correct and generate useful CI maps from airborne and satellite sensors can provide a time- and cost-effective method for HABs analysis. Timely processing of these high spatial and spectral resolution remote sensing data can aid in management of water intake resources. These results will help to improve methods leading to HABs mapping by testing different algal retrieval algorithms and atmospheric correction techniques using a three tiered hyperspectral sensor approach utilizing satellite, airborne, and ground level sensors, coupled with water quality measurements as reference data.
Richard Becker (Advisor)
Thomas Bridgeman (Committee Member)
Kevin Czajkowski (Committee Member)
84 p.
Geology; Limnology; Remote Sensing
remote sensing, hyperspectral, algae bloom, lake erie, western basin, cyanobacteria index, atmospheric correction

Recommended Citations

Citations

  • Cline, Jr., M. T. (2016). Analysis of Coincident HICO and Airborne Hyperspectral Images Over Lake Erie Western Basin HABs [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470324418

    APA Style (7th edition)

  • Cline, Jr., Michael. Analysis of Coincident HICO and Airborne Hyperspectral Images Over Lake Erie Western Basin HABs. 2016. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470324418.

    MLA Style (8th edition)

  • Cline, Jr., Michael. "Analysis of Coincident HICO and Airborne Hyperspectral Images Over Lake Erie Western Basin HABs." Master's thesis, University of Toledo, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470324418

    Chicago Manual of Style (17th edition)

toledo1470324418
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© 2016, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.