INTEGRATING TOOLS FOR SURFACE WATER QUALITY MANAGEMENT - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 62

water body and taken back to a laboratory for analysis. Lake temperature, pH, dissolved oxygen are
taken in the field using instruments designed for that purpose. The most useful measurements taken by
a field spectrometer are those coordinated with acquisition of aircraft or satellite data (Campbell,
1996). This is a necessary step in aerial or satellite-based remote sensing because images obtained
from remote sensors are not immediately comparable to ground truth data or field spectrometer data
because of atmospheric distortion (Aspinall, Marcus, & Boardman, 2002). Nordheim and Lillesand
(2004) measured upwelling and incoming radiation over 2048 spectral bands from 339.99 nanometers
(nm) to 1023.9 nm for the purpose of developing a CHL-aorophyll a estimation algorithm. Using two
spectrometers, above and below the surface of lakes in Northern Wisconsin and Michigan, reflectance
spectra were taken at 6 sites directly above the surface and 6 sites immediately below the surface.
They then classified the lakes into classes based on absorption spectra, oligotrophic or blue lakes,
mesotrophic or green lakes, and high dissolved organic carbon (DOC) or black lakes. They concluded
that their estimates of CHL-aorophyll concentration in “blue” and “green” lakes were reasonable,
while their estimates in “black” lakes were questionable. From the available literature, it is clear that
the use of in situ field samples is an extremely accurate method of determining water quality and
measuring water-quality variables. However, this method gives a limited spatial representation of the
study area as well as being costly and time consuming (Ritchie & Cooper, 2002). Using a
spectrometer, water samples, and regression analysis, Rao (1998) was able to estimate CHL-aorophyll
a concentration with great accuracy. Her estimates of turbidity and suspended sediments were also
very good. It appears that the use of spectrometer readings, water samples, and regression analysis is a
good way to determine CHL-aorophyll a concentration, turbidity, and suspended sediments based on
the spectral signatures of the water body.
Airborne Remote Sensing Data
Airborne remote sensing data is gathered by a number of different sensors, Multi Spectral Scaners
(MSS), which collect data in a few bands, and hyperspectral sensors, which collect data in a large
number of spectral bands. Monitoring of water-quality parameters, such as CHL-aorophyll a, turbidity,
and suspended sediments, is done more effectively with hyperspectral data because there are more
bands and better spectral resolution. Higher spectral resolution is required to measure the fine features
of water quality (Ritchie & Cooper 2002). Hyperspectral remote sensing of inland water bodies, like
lakes and rivers, can be a useful tool for monitoring water-quality parameters according to Shafique et
al., (2001). Airborne hyperspectral remote sensing, can be used to measure CHL-aorophyll a , total
suspended solids, turbidity, and Secchi depth, which is a measure of suspended materials. (Dekker,
Malthus, & Seyhan 1991; Gittleson, 1992; and Kallio, Kutser, Hannonen, Koponen, Pulliainen,
Vepsalainen, & Pyhalahti, 2001). The many bands available in the hyperspectral sensors allow
researchers to detect these water quality parameters unlike the few coarse bands of the multispectral
sensors. By analyzing the hyperspectral profile of individual pixels or groups of pixels, one can
compare those profiles to library spectra, profiles of known concentrations, to determine CHL-
aorophyll concentration, suspended sediment, etc. A study on a river system in southwest Ohio that
included the Great Miami River, a tributary of the Ohio River, was conducted by Shafique, Autrey,
Flotermersch, & Fulk (1999).
Using the Compact Airborne Spectrometer Imager (CASI). Using ENVI software, Shafique et al.,
(2002) determined that the two bands, 672 nm and 705 nm, best correlated the ground truth data with
the remote sensing data. They stated that the use of hyperspectral imagery to assess water quality
issues of CHL-aorophyll a and turbidity was an accurate way of mapping spatially continuous data.
Karaska, Huguenin, Beacham, Wang, Jensen, and Kaufmann (1999) studied the Neuse River of North
Carolina using Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) hyperspectral images to
measure CHL-aorophyll, suspended minerals, DOC, and turbidity to determine source points of
pollution. The team concluded that the hyperspectral sensor AVIRIS has the ability to directly
measure CHL-aorophyll concentrations in the Neuse River. In the case of dissolved organic carbon
and suspended minerals, field sampling was inadequate to assess these values. Therefore the team
concluded that the accuracy of hyperspectral measurements of suspended minerals, dissolved organic
carbon, and turbidity measurements remains uncertain.