ABSTRACT:
Models to predict the magnitude of agricultural nonpoint-source pollution in streams have been developed to meet a growing demand for management information. The Agricultural Nonpoint Source (AGNPS) model requires 20 parameters to calculate potential nonpoint-source pollution for a watershed. Lundsat thematic mapper (TM) data, SPOT multispectral data, and SPOT panchromatic data were tested to determine their ability to provide selected inputs to AGNPS. Each data set was classified using supervised and unsupervised methods, and the accuracy of each classification was evaluated using contingency tables and the kappa statistic. Highest classification accuracies were obtained using merged Landsat TM/SPOT panchromatic digital data, although the most cost-gective method for watersheds larger than 141 km2 (54 square miles) was a supervised classification of SPOT multispectral data. In watersheds less than 141 km2, interpretation of aerial photographs was the most accurate and least expensive method of developing land cover maps.
Footnotes
Mark E. Jakubauskas is a research assistant and Jerry L. Whistler is a research associate, Kansa Applied Remote Sensing Program (W), University of Kansas, Lawrence, 66045–2969; Mary E. Dillworth is an assistant professor, Department of Geography, Memphis State University, Memphis, Tennessee 38152; and Edward A. Martinko is director of KARS, University of Kansas, Lawrence, 64045–2969. This research was pegormed under EPA Region VII grant number X-007-331-001 to KARS. James W Merchant, formerly of KARS and now with the Center for Advanced Land Management Information Technology (CALMIT) at the University of Nebraska-Lincoln, originated this project and provided substantial guidance throughout its execution and completion. Others KARS staff who contributed to this project were A. J. Thomas, D. Bandi, and G. Haper.
- Copyright 1992 by the Soil and Water Conservation Society
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