USING AIRBORNE HYPERSPECTRAL IMAGERY TO ESTIMATE CHLOROPHYLL A AND PHYCOCYANIN IN THREE CENTRAL INDIANA MESOTROPHIC TO EUTROPHIC RESERVOIRS
type: CreativeWork
description: This thesis presents the results of an analysis of predicting phytoplankton pigment concentrations (chlorophyll a and phycocyanin) from remotely sensed imagery. Hyperspectral airborne and hand-held reflectance spectra were acquired on three reservoirs (Geist, Morse and Eagle Creek) in Central Indiana, USA. Concurrent with the reflectance acquisition, in situ samples were collected and analyzed in laboratories to quantify the pigment concentration and other water quality parameters. The resultant concentration was then linked to Airborne Imaging Spectrometer for Applications (AISA) reflectance spectra for the sampling stations to develop predictive models. AISA reflectance spectra were extracted from the imagery which had been processed for radiometric calibration and geometric correction. Several previously published algorithms were examined for the estimation of pigment concentration from the spectra. High coefficients of determination were achieved for predicting chlorophyll a in two of the three reservoirs (Geist R2 = 0.712, Morse R2 = 0.895 and Eagle Creek Reservoir R2 = 0.392). This situation was similar for PC prediction, where two of the three reservoirs had high coefficients of determination between pigment concentration and reflectance (Geist R2 = 0.805, Morse R2 = 0.878 and Eagle Creek Reservoir R2 = 0.316). The results of this study show that reflectance spectra collected with an airborne hyperspectral imager are statistically significant, p < 0.03, in predicting chlorophyll a and phycocyanin pigment concentration in all three reservoirs in this study without the consideration of other parameters. The algorithms were then applied to the AISA image to generate high spatial resolution (1 m2) maps of Chlorophyll a and Phycocyanin distribution for each reservoir.
abstract: This thesis presents the results of an analysis of predicting phytoplankton pigment concentrations (chlorophyll a and phycocyanin) from remotely sensed imagery. Hyperspectral airborne and hand-held reflectance spectra were acquired on three reservoirs (Geist, Morse and Eagle Creek) in Central Indiana, USA. Concurrent with the reflectance acquisition, in situ samples were collected and analyzed in laboratories to quantify the pigment concentration and other water quality parameters. The resultant concentration was then linked to Airborne Imaging Spectrometer for Applications (AISA) reflectance spectra for the sampling stations to develop predictive models. AISA reflectance spectra were extracted from the imagery which had been processed for radiometric calibration and geometric correction. Several previously published algorithms were examined for the estimation of pigment concentration from the spectra. High coefficients of determination were achieved for predicting chlorophyll a in two of the three reservoirs (Geist R2 = 0.712, Morse R2 = 0.895 and Eagle Creek Reservoir R2 = 0.392). This situation was similar for PC prediction, where two of the three reservoirs had high coefficients of determination between pigment concentration and reflectance (Geist R2 = 0.805, Morse R2 = 0.878 and Eagle Creek Reservoir R2 = 0.316). The results of this study show that reflectance spectra collected with an airborne hyperspectral imager are statistically significant, p < 0.03, in predicting chlorophyll a and phycocyanin pigment concentration in all three reservoirs in this study without the consideration of other parameters. The algorithms were then applied to the AISA image to generate high spatial resolution (1 m2) maps of Chlorophyll a and Phycocyanin distribution for each reservoir.
author: Sengpiel, Rebecca Elizabeth
language: en
keywords: Remote Sensing, Phycocyanin
date published: 2007-08-08T15:35:17Z
date modified: 2026-07-24T02:41:41Z
provider: IUPUI
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: IUPUI

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1805/1128

PropertyValue
property ID: DOI
value: 10.7912/C2/503