88
R.O. Duba:yah, E.F. Wood, E.T. Engman et al.
In remote sensing applications, one seldom duplicates detailed land use statistics
exactly. For example, a study by the Corps of Engineers (Rango et ai., 1983) estimated that an individual pixel may be incorrectly classified about one-third of the
time. However, by aggregating land use over a significant area, the misclassification of land use can be reduced to about two percent, which is too small to affect
the runoff coefficient or the resulting flood statistics.
Studies have shown (Jackson et ai., 1977) that for planning studies the Landsat
approach is cost effective. The authors estimated that the cost benefits were on the
order of 2.5 to 1 and can be as high as 6 to 1, in favor of the Landsat approach.
These benefits increase for larger basins or for multiple basins in the same general
hydrological area. Mettel et al., (1994) demonstrated that the recomputation of
Probable Maximum Flood (PMF) for the Au Sable River using HEC-l and updated and detailed land use data from Landsat TM resulted in 90% cost cuts in
upgrading dams and spillways in the basin.
Other types of runoff models that are not based only on land use are beginning to
be developed. For example Striibing and Schultz (1983) have developed a runoff
regression model that is based on Barrett's (1970) indexing technique. The cloud
area and temperature are the satellite variables used to develop a temperature
weighted cloud cover index. This index is then transformed linearly to mean
monthly runoff. Rott (1986) also developed a daily runoff model using Meteosat
data for a cloud index. Recently, Papadakis et ai., (1993) have used a cloud cover
index from satellite imagery to estimate monthly area precipitation. A series of
non-linear reservoirs then transforms the precipitation into monthly runoff values.
This approach was successfully demonstrated for the large (16000 sq. km.) Tano
River Basin in Africa and illustrates the value of remote sensing data when conventional data are not readily available (see also Chap. 18). Ottle et ai. (1989) have
shown how satellite-derived surface temperatures can be used to estimate ET and
soil moisture in a model that has been modified to use these data.
Integration with GIS. The pixel format of digital remote sensing data makes it
ideal for merging with Geographical Information Systems (GIS). Remote sensing
can be incorporated into the system in a variety of ways: as a measure of land use,
impervious surfaces, for providing initial conditions for flood forecasting, and for
monitoring flooded areas (Neumann et ai., 1990). The GIS allows for the combining of other spatial data forms such as topography, soils maps and hydrologic
variables such as rainfall distributions or soil moisture. This approach was demonstrated by Kouwen et al., (1993) where their Grouped Response Unit (GRU) included satellite based land use and lies within a computational element that may be
either a sub-basin or an area of uniform meteorological forcing. In HYDROTEL,
Fortin and Bernier (1991) propose combining SPOT DEM data with satellitederived land use and soils mapping data to defme Homogeneous Hydrologic Units
(HHU). In a study of the impact of land use change on the Mosel River Basin, Ott
et ai. (1991) and Schultz (1993) have defmed Hydrologically Similar Units (HSU)
by DEM data, soils maps and satellite-derived land use. They also used satellite
data to determine a vegetation index (NDVI) and a leaf water content index (WCI)
which are combined to delineate areas where subsurface supply of water is available to vegetation. Mauser (1991) has shown how multi-temporal SPOT and TM
R.O. Duba:yah, E.F. Wood, E.T. Engman et al.
In remote sensing applications, one seldom duplicates detailed land use statistics
exactly. For example, a study by the Corps of Engineers (Rango et ai., 1983) estimated that an individual pixel may be incorrectly classified about one-third of the
time. However, by aggregating land use over a significant area, the misclassification of land use can be reduced to about two percent, which is too small to affect
the runoff coefficient or the resulting flood statistics.
Studies have shown (Jackson et ai., 1977) that for planning studies the Landsat
approach is cost effective. The authors estimated that the cost benefits were on the
order of 2.5 to 1 and can be as high as 6 to 1, in favor of the Landsat approach.
These benefits increase for larger basins or for multiple basins in the same general
hydrological area. Mettel et al., (1994) demonstrated that the recomputation of
Probable Maximum Flood (PMF) for the Au Sable River using HEC-l and updated and detailed land use data from Landsat TM resulted in 90% cost cuts in
upgrading dams and spillways in the basin.
Other types of runoff models that are not based only on land use are beginning to
be developed. For example Striibing and Schultz (1983) have developed a runoff
regression model that is based on Barrett's (1970) indexing technique. The cloud
area and temperature are the satellite variables used to develop a temperature
weighted cloud cover index. This index is then transformed linearly to mean
monthly runoff. Rott (1986) also developed a daily runoff model using Meteosat
data for a cloud index. Recently, Papadakis et ai., (1993) have used a cloud cover
index from satellite imagery to estimate monthly area precipitation. A series of
non-linear reservoirs then transforms the precipitation into monthly runoff values.
This approach was successfully demonstrated for the large (16000 sq. km.) Tano
River Basin in Africa and illustrates the value of remote sensing data when conventional data are not readily available (see also Chap. 18). Ottle et ai. (1989) have
shown how satellite-derived surface temperatures can be used to estimate ET and
soil moisture in a model that has been modified to use these data.
Integration with GIS. The pixel format of digital remote sensing data makes it
ideal for merging with Geographical Information Systems (GIS). Remote sensing
can be incorporated into the system in a variety of ways: as a measure of land use,
impervious surfaces, for providing initial conditions for flood forecasting, and for
monitoring flooded areas (Neumann et ai., 1990). The GIS allows for the combining of other spatial data forms such as topography, soils maps and hydrologic
variables such as rainfall distributions or soil moisture. This approach was demonstrated by Kouwen et al., (1993) where their Grouped Response Unit (GRU) included satellite based land use and lies within a computational element that may be
either a sub-basin or an area of uniform meteorological forcing. In HYDROTEL,
Fortin and Bernier (1991) propose combining SPOT DEM data with satellitederived land use and soils mapping data to defme Homogeneous Hydrologic Units
(HHU). In a study of the impact of land use change on the Mosel River Basin, Ott
et ai. (1991) and Schultz (1993) have defmed Hydrologically Similar Units (HSU)
by DEM data, soils maps and satellite-derived land use. They also used satellite
data to determine a vegetation index (NDVI) and a leaf water content index (WCI)
which are combined to delineate areas where subsurface supply of water is available to vegetation. Mauser (1991) has shown how multi-temporal SPOT and TM