Remote Sensing of Water Quality in Rotorua and Waikato Lakes

Abstract

Remote sensing has the potential to monitor spatial variation in water quality over large areas. While ocean colour work has developed analytical bio-optical water quality retrieval algorithms for medium spatial resolution platforms, remote sensing of lake water is often limited to high spatial resolution satellites such as Landsat, which have limited spectral resolution. This thesis presents the results of an investigation into satellite monitoring of lake water quality. The aim of this investigation was to ascertain the feasibility of estimating water quality and its spatial distribution using Landsat 7 ETM+ imagery combined with in situ data from Rotorua and Waikato lakes. For the comparatively deep Rotorua lakes, r² values of 0.91 (January 2002) and 0.83 (March 2002) were found between in situ chlorophyll (chl) a and the Band1/Band3 ratio. This technique proved useful for analysing the spatial distribution of phytoplankton, especially in lakes Rotoiti and Rotoehu. For the more bio-optically complex shallow lakes of the Waikato, a linear spectral unmixing (LSU) approach was investigated where the water surface reflectance spectrum is defined by the contribution from pure pixels or endmembers. The model estimates the percentage of the endmember within the pixel, which is then used in a final regression with in situ data to map water quality in all pixels. This approach was used to estimate the concentration of chl a (r² = 0.84). Total suspended solid (TSS) concentration was mapped using the traditional Band 3 regression with in situ data, which combined atmospherically corrected reflectance for both images into a single relationship (r² = 0.98). The time difference between in situ data collection and satellite data capture is a potential source of error. Other potential sources of error include sample location accuracy, the influence of dissolved organic matter, and masking of chl a signatures by high concentrations of TSS. The results from this investigation suggest that remote sensing of water quality provides meaningful and useful information with a range of applications and could provide information on temporal spatial variability in water quality.