INTEGRATING TOOLS FOR SURFACE WATER QUALITY MANAGEMENT - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 63

It is evident from the literature review that the use of airborne remote sensing of water quality is a
superior method of monitoring water quality of inland water bodies due to the speed, accuracy, and
amount of data that can be collected. The use of airborne multispectral sensors for water quality
monitoring is limited by the small number of spectral bands available with this type of sensor and the
fact that many bands are required to monitor the large number of water quality parameters involved in
water quality (Ritchie & Schiebe 2000). Therefore, hyperspectral sensors are optimal for this task. It
has been demonstrated that CHL-aorophyll, turbidity, total suspended solids, and Secchi depth can be
determined by this method. (Dekker,1991).
3.4.2. Integrating GIS, Remote Sensing and Modelling for Surface Water Quality
Management
A GIS processes any data that has a spatial component. Remotely sensed data can be best utilized if
they are incorporated in a GIS that is designed to accept large volumes of spatial data. Beside data
handling and processing, specific applications of of GIS and remote sensing have mainly concentrated
on non-point source pollution (NPS). This is because remotely sensed data products such as land-
use/land cover could be directly utilised in NPS modelling. Several watershed models have been
interfaced with GIS including the export coefficient model, AGNPS and DRASTIC. Agricultural Non
Point Source Pollution (AGNPS) model estimates nitrogen, phosphorous and chemical oxygen
demand concentration in runoff and assesses agricultural impact on surface water quality based on
spatially varying controlling parameters (e.g. topography, soils, land-use, etc…). Kim and Ventura
(1993) managed and manipulated land-use data for modeling NPS pollution of an urban basin using an
empirical urban water quality model. Another approach uses an export coefficient model to calculate
nutrient losses from catchment to surface water mainly in terms of aerial extent of different land-
use/land cover and their associated fertilizer application rates. Matikalli et al. (1996) derived historical
land cover data from air-born and satellite sensors, and implemented the model using Arc/Info to
estimate historical nitrogen and phosphorous loading in river Glen watershed in the UK.
As important as the improvements in the quality and availability of remote sensing data is the growing
number of geospatial data management and analysis tools available for use in different application of
water resources. With geographic information systems (GIS), digital remote sensing data can now be
integrated with other types of digital data and with models. Such technological advances can be useful
for development of remote sensing applications for water resources and water quality management.
However, the use of remote sensing data and applications involves more than the underlying technical
capacity. From the perspective of the remote sensing applications end users, what is important is the
information that remote sensing applications can make available, not the raw data that it can provide.
Equally important, is the ability and positive acceptance of applying the recent technological advances.
This depends on institutional, leadership, budgetary, procedural, and even personnel factors.
Applications of Remote Sensing in Surface Water Quality
Suspended sediments
Suspended sediments could be a common pollutant both in weight and volume in surface waters of
freshwater systems (Lal, 1994). Suspended sediments increase the reflected energy from surface
waters in the visible and near infrared proportion of the electromagnetic spectrum (Ritchei et al., 1976)
Figure (3-3) . In situ and controlled laboratory measurements have shown that surface water radiance is
affected by sediment type, texture and color (Holyer 1978; Novo et al., 1989a; Han and Rundquist
1996), sensor view and sun angles (Ritchei et al., 1975; Novo et al., 1989b; Ferrier 1995), and water
depth (Mantovani and Cabral 1992).