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MEMO 89.2
<br />meetings with water users indicate that the results obtained are closely related to the population of
<br />irrigated lands and crop types in the SPDSS area and therefore represent a good estimate of the accuracy
<br />of the mapping efforts.
<br />The process of aggregating all agricultural crops in the SPDSS area into a few groups of similar
<br />consumptive use, as described in Section 2.5.2.1 likely introduced some error in the classification results
<br />for crops not included in the classification crop list.
<br />Although most of the Landsat imagery used in this project was cloud-free, a few images contained small
<br />clouds and haze over irrigated areas. This problem was solved using cloud-free areas in overlapping
<br />frames to produce the irrigated lands and crop type classification. Adjacent Landsat frames have an
<br />overlap of 20 percent. This was particularly the case of a few areas that were cloud covered in Landsat
<br />frame 033/032 but cloud free in similar date, usually seven days, apart in frame 034/032.
<br />4. References
<br />Chavez, P.S. Jr., 1996, Image-based atmospheric corrections-revisited and revised. Photogrammetric
<br />Engineering and Remote Sensing 62(9): 1025-1036.
<br />Cipra, J.E. 2003. A method for improving classification accuracy and acreage assessment in irrigated
<br />crops. Photogrammetric Engineering and Remote Sensing 69(1): 6-9.
<br />Colorado Division of Wildlife. 2004.Riparian and Wetland Mapping. Wildlife GIS. 317 W. Prospect.
<br />Fort Collins, Colorado 80525. http://ndisl.nrel.colostate.edu/riparian/riparian.htm
<br />Congalton, R.G., and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and
<br />Practices. Lewis Publishers. Boca Raton, FL. 137 p.
<br />Crist, E.P., and R.C. Cicone. 1984. "Application of the Tasseled Cap Concept to Simulated Thematic
<br />Mapper Data. Proceedings, American Society of Photogrammetry, 2:508-517.
<br />ERDAS IMAGINE software and On-Line Help. Copyright ©1982 - 2004 by Leica Geosystems, GIS &
<br />Mapping, LLC. All rights reserved.
<br />Huang C., L. Yang, C. Homer, B. Wylie, J. Vogelman, and T. DeFelice, 2002. At-satellite reflectance: A
<br />first normalization of Landsat 7 ETM+ Images. WWW URL:
<br />http://landcover.usgs.~ov/pdf/huang2.pdf, US Dept. of Interior, USGS.
<br />Jackson, R.D., and A. R. Huete. 199E Interpreting vegetation indices. Preventive Veterinary Medicine.
<br />11: 185- 200.
<br />Jensen, J.R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. Second
<br />Edition. Prentice Hall Series in Geographic Information Science. Upper Saddle River, New Jersey.
<br />318 p.
<br />Kauth, R.J., and G.S. Thomas. 1976. "The Tasseled Cap - A Graphic Description of the Spectral-
<br />Temporal Development of Agricultural Crops as Seen by Landsat". Proceedings, Symposium on
<br />Machine Processing of Remotely Sensed Data. West Lafayette, IN: Laboratory for Applications of
<br />Remote Sensing, pp. 41-51.
<br />Lillesand, T.M., and R.W. Kiefer. 2000. Remote Sensing and Image Interpretation. Fourth Edition. John
<br />Wiley and Sons, Inc. New York. 724 p.
<br />Markham, B.L., and J.L. Baker. 1986. Landsat MSS and TM post-calibration dynamic ranges,
<br />exoatmospheric reflectances, and at-satellite temperatures. EOSAT Landsat Tech. Notes 1:3-7.
<br />Lanham, Maryland.
<br />Theobald, D., N. Peterson, and W. Romme. 2004. The Colorado Vegetation Model: Using National Land
<br />Cover Data and Ancillary Spatial Data to Produce a High Resolution, Fine-Classification Map Of
<br />Colorado. Final report v8. Natural Resource Ecology Lab (NREL). Colorado State University.
<br />www.ndis.nrel.colostate.edu/davet/cvm.htm, 32 p.
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