Wiangwang, N. 2006. Hyperspectral data modeling for water quality studies in Michigan's inland lakes. Ph.D. Dissertation, Michigan State University, East Lansing, Michigan, USA.
Hyperspectral remote sensing imagery has been used to estimate spatial and temporal variation of water quality, such as chlorophyll a , transparency, and suspended solids, primarily for marine and coastal waters. Although physicochemical properties of marine and inland waters differ, hyperspectral data and modeling may provide an alternative tool for inland lake assessment. However, little has been done to identify the most suitable spectral bands for water quality estimation and there is a lack of quantitative relationship between water quality and hyperspectral data. The primary objectives of this study are to identify optimal spectral bands most sensitive to water quality indicators and to develop improved hyperspectral water quality indicators of inland lakes. The secondary objective is to determine the most effective filters for noise removal in hyperspectral data.
To address these objectives, a field campaign was conducted on 42 inland lakes in Michigan in 2004. Radiometric spectra, Secchi disk depth, dissolved oxygen, temperature, and light extinction profile data were collected. Water samples were analyzed for chlorophyll a , suspended solid, total nitrogen, total phosphorus, non-purgable organic carbon, and phytoplankton species composition. Spectral radiances were measured with a hand-held spectrometer (LabSpec ® Pro) and with an airborne Imaging Spectrometer for Applications (AISA) sensor, to correlate the water quality and hyperspectral data.
Principal Component Analysis was used to identify the narrow-wavebands, and derivative analysis used to determine the region-wavebands. Statistical spectral water quality indicators were developed to correlate with Secchi depth, chlorophyll a , total suspended solid, non-purgable organic carbon, diatom biomass, green algal biomass, and bluegreen algal biomass. These relations were validated to suggest that high accuracies were achieved for Secchi depth (R 2 0.76—0.84), chlorophyll a (R 2 0.70—0.76), and bluegreen algae (R 2 0.56—0.72). The quantitative relationship between remotely sensed variables and water quality indicators can be used to extrapolate point-based water quality measurements to large spatial extents for an improved water quality assessment. Additionally, the Savitsky Golay filter was found the best to remove spectral noises. The innovation of this study is that it developed a quantitative relationship between hyperspectral data and water quality variables of inland lakes in Michigan.
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