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PREDICTION OF WATER QUALITY PARAMETERS FROM VIS-NIR RADIOMETRY: USING LAKE ERIE AS A NATURAL LABORATORY FOR ANALYSIS OF CASE 2 WATERS

Ali, Khalid A.
http://rave.ohiolink.edu/etdc/view?acc_num=kent1309980508

Abstract Details

2011, PHD, Kent State University, College of Arts and Sciences / Department of Earth Sciences.
Remote sensing has become very promising in providing temporal and spatial information regarding biogeodynamics in large and open freshwater bodies. For this work, Lake Erie was selected as the site for study because it is a compact region within which there are a wide variety of optical properties. In this study, spectral analytical techniques such as wavelets, Artificial Neural Networks (ANN) and multivariate techniques are applied to a full range of hyperspectral and multispectral data to better detect, differentiate among and estimate the concentrations of CPAs in optically complex environments. These methods produced more robust algorithms that can retrieve the concentrations of the various in-water constituents. Principal Component Analysis (PCA) of the first-derivative transformed hyperspectral data indicated that phytoplankton and inorganic sediments characterize up to 88% of the optical variability observed in the WBLE. PCA-based chlorophyll a and Total Suspended Matter (TSM) models yielded R2 values of 0.70 and 0.75, respectively. The associated percentage RMSE values are 11.5% and 32% for chlorophyll a and TSM, respectively. Wavelet-based spectral decomposition of first-derivative transformed hyperspectral data showed that most of the CPAs in the WBLE are characterized as having medium to high-frequency signals. The wavelet-based spectral models gave R2 values as high as 0.86 with RMSE = 9.7% for chlorophyll a prediction and R2 values of 0.88 with 15% percent RMSE for TSM. A new regional ANN algorithm is developed using Full–Resolution MERIS satellite observation for retrieval of selected CPAs. The ANN-based satellite model was able to explain 76% and 82% of the chlorophyll a and TSM variability, respectively, in the WBLE. The RMSE values for these models were 0.58 µg/l and 2.59 mg/l for chlorophyll a and TSM, respectively. The percentage RMSE in this case is approximately 8.4% and 30.1% for chlorophyll a and TSM, respectively. Calibration-validation procedures indicated that the spectral analytical techniques that employ the full range of spectral data are more stable than existing bio-optical algorithms. The results from this study and the conceptual approach may be extended to a variety of optically complex environments such as coastal areas and other large inland water bodies.
Joseph D. Ortiz, PhD (Committee Chair)
Abdul Shakoor, PhD (Committee Member)
Elizabeth Griffith, PhD (Committee Member)
Darren Bade, PhD (Committee Member)
Shanhu Lee, PhD (Committee Member)
Remote Sensing

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Citations

  • Ali, K. A. (2011). PREDICTION OF WATER QUALITY PARAMETERS FROM VIS-NIR RADIOMETRY: USING LAKE ERIE AS A NATURAL LABORATORY FOR ANALYSIS OF CASE 2 WATERS [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1309980508

    APA Style (7th edition)

  • Ali, Khalid. PREDICTION OF WATER QUALITY PARAMETERS FROM VIS-NIR RADIOMETRY: USING LAKE ERIE AS A NATURAL LABORATORY FOR ANALYSIS OF CASE 2 WATERS. 2011. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1309980508.

    MLA Style (8th edition)

  • Ali, Khalid. "PREDICTION OF WATER QUALITY PARAMETERS FROM VIS-NIR RADIOMETRY: USING LAKE ERIE AS A NATURAL LABORATORY FOR ANALYSIS OF CASE 2 WATERS." Doctoral dissertation, Kent State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=kent1309980508

    Chicago Manual of Style (17th edition)

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This open access ETD is published by Kent State University and OhioLINK.