4 Integration of Remotely Sensed Data into Geographical Infonnation Systems
77
developed a linkage between AGNPS and a spatially distributed model. Osmond
et al. (1997) employed this linkage and added capabilities that allowed users to
enter point source, pesticide and channel information in a decision support system,
WATERSHEDSS (WATER, Soil, and Hydro-Environmental Decision Support
System). Using such a system a user can determine critical areas within a watershed and evaluate effects of alternative land treatment scenarios on water quality.
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 areal extent of different landuse/ land-cover and their associated fertilizer application rates: Mattikalli et al.
(1996) derived historical land-cover data from airborne and satellite sensors, and
implemented the model using ArclInfo to estimate historical nitrogen and phosphorus loading in the River Glen watershed in the UK (see Fig. 4.7 for the methodological approach).
GIS have also been used in aspects of groundwater management and modeling
(e.g., Evans and Myers, 1990; Maidment, 1993). DRASTIC (depth to water, D;
net recharge, R; aquifer media, A; soil media, S; slope, T, impact of the vadose
zone, I, and hydraulic conductivity, C) is an empirical model used to evaluate
regional groundwater pollution potential. Evans and Myers (1990) implemented
this model in ERDAS around the Rehoboth Beach, Delaware, and generated
groundwater pollution risk and hazard assessment maps. Methodology consisted
of additive overlay process of input layers assigned with certain weights. Most of
these examples have implemented spatial models designed to evaluate groundwater vulnerability to contamination. However, these approaches have not employed
data derived from remote sensing, probably because of specific nature of the input
parameters.
4.3.6
Soil Erosion Monitoring
Soil erosion monitoring and! or prediction is a popular application of integrated
remote sensing and GIS (see also Chap. 12). Soil erosion potential is typically
computed using the Universal Soil Loss Equation (USLE):
A=S·L·R·P·C·K
where A = mean annual soil loss per unit area, S = slope steepness factor, L =
slope length factor, R = rainfall factor, P = erosion control practice factor, C =
land-use/ land-cover (or cropping management) factor, and K = soil erodibility
factor. S- and L- factors can be derived using slope and aspect information generated from aDEM. R- factor can be assigned using a TIN structure created for
rainfall gauging stations. P- and C- factors can be estimated using remotely sensed
data via land-use/ land-cover classification and associated land management information.
Pelletier (1985) employed a GIS framework to implement the USLE formulation. Figure 4.8 shows various data sources and files managed by a GIS. This
study used data from Landsat (MSS and TM) sensors to determine land-cover, and
77
developed a linkage between AGNPS and a spatially distributed model. Osmond
et al. (1997) employed this linkage and added capabilities that allowed users to
enter point source, pesticide and channel information in a decision support system,
WATERSHEDSS (WATER, Soil, and Hydro-Environmental Decision Support
System). Using such a system a user can determine critical areas within a watershed and evaluate effects of alternative land treatment scenarios on water quality.
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 areal extent of different landuse/ land-cover and their associated fertilizer application rates: Mattikalli et al.
(1996) derived historical land-cover data from airborne and satellite sensors, and
implemented the model using ArclInfo to estimate historical nitrogen and phosphorus loading in the River Glen watershed in the UK (see Fig. 4.7 for the methodological approach).
GIS have also been used in aspects of groundwater management and modeling
(e.g., Evans and Myers, 1990; Maidment, 1993). DRASTIC (depth to water, D;
net recharge, R; aquifer media, A; soil media, S; slope, T, impact of the vadose
zone, I, and hydraulic conductivity, C) is an empirical model used to evaluate
regional groundwater pollution potential. Evans and Myers (1990) implemented
this model in ERDAS around the Rehoboth Beach, Delaware, and generated
groundwater pollution risk and hazard assessment maps. Methodology consisted
of additive overlay process of input layers assigned with certain weights. Most of
these examples have implemented spatial models designed to evaluate groundwater vulnerability to contamination. However, these approaches have not employed
data derived from remote sensing, probably because of specific nature of the input
parameters.
4.3.6
Soil Erosion Monitoring
Soil erosion monitoring and! or prediction is a popular application of integrated
remote sensing and GIS (see also Chap. 12). Soil erosion potential is typically
computed using the Universal Soil Loss Equation (USLE):
A=S·L·R·P·C·K
where A = mean annual soil loss per unit area, S = slope steepness factor, L =
slope length factor, R = rainfall factor, P = erosion control practice factor, C =
land-use/ land-cover (or cropping management) factor, and K = soil erodibility
factor. S- and L- factors can be derived using slope and aspect information generated from aDEM. R- factor can be assigned using a TIN structure created for
rainfall gauging stations. P- and C- factors can be estimated using remotely sensed
data via land-use/ land-cover classification and associated land management information.
Pelletier (1985) employed a GIS framework to implement the USLE formulation. Figure 4.8 shows various data sources and files managed by a GIS. This
study used data from Landsat (MSS and TM) sensors to determine land-cover, and