One of the first applications of remote-sensing data in hydrologie models used Landsat data to determine both urban and rural land use for estimating runoff coefficients (Jackson et al., 1976). Land use is an important characteristic of the runoff process that affects infiltration, erosion, and évapotranspiration. Distributed models, in particular, need specific data on land use and its location within the basin. Most of the work on adapting remote sensing to hydrologie modeling has involved the Soil Conservation Service (SCS) runoff curve number model (U.S. Department of Agriculture, 1972) for which remote-sensing data are used as a substitute for land cover maps obtained by conventional means (Jackson et al., 1977; Bondelid et al., 1982).
In remote-sensing applications, one seldom duplicates detailed land-use statistics exactly. For example, a study by the Corps of Engineers (Rango et al., 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 misclassifica-tion of land use can be reduced to about 2%, which is too small to affect the runoff coefficient or the resulting flood statistics.
Studies have shown (Jackson et al., 1977) that for planning studies the Landsat approach is cost effective. The authors estimated that the cost benefits were on the order to 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 hydro-logical area. Mettel et al. (1994) demonstrated the recomputation of Probable Maximum Flood (PMFs) for the Au Sable River using HEC-1 and updated and detailed land-use data from Landsat TM resulted in 90% cost cuts in upgrading dams and spillways in the basin.
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