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SURFACE ROUGHNESS EFFECTS ON SOIL SPECTRAL REFLECTANCE.Wallace, Karen Sue, 1957- January 1986 (has links)
No description available.
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Multispectral reflectance and image textural signatures of arid alluvial geomorphic surfaces in the Castle Dome Mountains and piedmont, southwestern ArizonaShih, Ernest Hsiao Hsin January 1982 (has links)
No description available.
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Geologic Applications of Landsat Images in Northeastern Arizona to the Location of Water Supplies for Municipal and Industrial Uses (Final Report)Babcock, Elizabeth, Briggs, Philip, DeCook, Kenneth, Ethridge, Loch, Foster, Kennith, Glass, Charles, Schowengerdt, Robert 04 1900 (has links)
Geologic applications of Landsat images in northeastern Arizona to the location of water supplies for municipal and industrial uses / A Final Report of Work Performed Under OWRT Matching Grant B-066-ARIZ, Agreement number: 14-34-0001-8060 / April 1979
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Soil water potential as related to the Crop Water Stress Index of irrigated cottonCopeland, Stephen Mark, 1955- January 1989 (has links)
The application of the crop water stress index (CWSI) method to irrigation scheduling is enhanced by knowledge of the relationship between CWSI and soil water potential (SWP) and how this relationship is affected by soil texture. A study using the same cultivar of cotton on three different soils was conducted in southern Arizona over a single growing season. Detailed data were collected of CWSI and soil moisture content for several treatments that scheduled irrigations at threshold CWSI values. CWSI was correlated with soil water potential values calculated from pressure plate determined moisture release curves. Spatial variability of soil characteristics necessitated use of average rather than plot specific moisture release curves. Analysis showed a linear CWSI-SWP relationship that varied greatly with soil depth and study site. The study concluded that soil profile average SWP alone does not normalize the CWSI between sites with different soil textures.
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A Study to Explore the Use of Orbital Remote Sensing to Determine Native Arid Plant DistributionMcGinnies, W. G., Haase, E. F., Lepley, L. K., Conn, J. S., Musick, H. B., Foster, K. E. 08 1900 (has links)
"Last biannual progress report and final report describing work under NASA contract no. NAS5-21812."
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THE RELATIONSHIP OF THE MUNSELL SOIL COLOR WITH THE LANDSAT SPECTRAL BANDS.Rodriguez-Morean, Alfonso Luis. January 1982 (has links)
No description available.
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Using Classification and Regression Tree and Valley Bottom Modeling Techniques to Identify Riparian Vegetation in Pinal County, ArizonaHickson, Benjamin 01 January 2015 (has links)
The ecological value and functionality of riparian systems along ephemeral, intermittent, and perennial streams in the Southwest is well established. In Pinal County, Arizona the existing datasets available to environmental managers and governing bodies drastically underestimate the extent and presence of riparian zones. This study addresses the issue through the use of remote sensing land cover classification techniques. Landsat 8 data, topographic data, and high-resolution color infrared (CIR) imagery, and several derived vegetation indices are used to construct a classification and regression tree (CART) model. Using training data, the CART model is used for the identification and delineation of basic land cover classes across the County. Woody annual and perennial species are identified and associated to riparian zones using a valley bottom model (VBM) developed by the United States Department of Agriculture.
The CART model (kappa value of 0.76) found that 929 square-miles of annual vegetation and 651 square-miles of perennial vegetation are present across Pinal County. Annual and perennial vegetation classifications are assessed for density using a 0.33 acre moving window. The density values for both classes are then used in conjunction to differentiate upland, xeroriparian, mesoriparian, and hydropriarian vegetation zones. Vegetation zones are clipped to regions where the VBM identifies valley bottom probability to be 62 percent or greater. The results generated provide a sufficiently comprehensive dataset that gives County managers and environmental professionals improved insight into the presence and distribution of important riparian habitats.
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