The reflectance of radiant energy from the earth's surface in sparsely vegetated arid rangelands is determined by the characteristics of the soil and geologic material on the land's surface. This study measured the color characteristics of earth surface materials collected from a semiarid rangeland in southeastern Arizona and compared these colors to digital numbers recorded by Landsat. Other parameters including particle size, slope, and vegetation were also evaluated, but the color characteristics of the fine earth soil and rock fragments measured with a colorimeter were most strongly correlated to Landsat digital numbers. The numerical values of the color components (hue, value, and chroma) for three different soil and rock fragment size fractions were related in a multiple linear regression equation to Landsat digital reflectance numbers. The R2 for Band 4 (0.5–0.6 µm), Band 5 (0.6–0.7 µm), Band 6 (0.7–0.8 µm), Band 7 (0.8–1.1 µm), and the sum of the four bands were 0.85, 0.69, 0.71, 0.68, and 0.75, respectively. The color of earth surface features in sparsely vegetated land areas should be precisely and accurately determined because of its very strong correlation with remotely sensed spectral data. The use of colorimeters to quantify the color of earth surface features will significantly help in evaluating remotely sensed data, particularly for land‐scapes in arid regions.
Pedon descriptions, vegetation transect information, and Landsat digital data were obtained for 110 sites on the Tonto National Forest in central Arizona. Using the field and satellite data, 33 variables were evaluated and prediction models were generated using stepwise multiple regression techniques. The following six factors explained 84% of the variability within the sum of the values for the four Landsat spectral bands: sum of brush and forest crown densities, elevation, surface color, rock type, cobbles on the surface of the site, and grass cover. Seven factors explained 81% of the variability for the ratio of Bands 4 plus 5 to Bands 6 plus 7: percent clay in the surface horizon, percent fragments > 2 mm in the surface horizon, the sum of forest and brush crown densities, pH of the surface horizon, color of the surface horizon, litter cover, and site aspect.
The intent of this publication is twofold: (1) to serve as a user guide for soil scientists and others interested in learning about the value and use of digital elevation model (DEM) data in making soil surveys and (2) to provide documentation of the Soil Landscape Analysis Project (SLAP). This publication provides a step-by-step guide on how digital slope-class maps are adjusted to topographic maps and orthophotoquads to obtain accurate slope-class maps, and how these derivative maps can be used as a base for soil survey premaps. In addition, guidance is given on the use of aspect-class maps and other resource data in making pre-maps. The value and use of tabular summaries are discussed. Examples of the use of DEM products by the authors and by selected field soil scientists are also given. Additional information on SLAP procedures may be obtained from USDA, Soil
Soil information is an essential theme in a digital information base for land management and resource monitoring, but public land management agencies seldom have detailed soil maps available for all of the area under their administration. Most of these agencies conduct soil surveys on a scheduled basis, but escalating costs and declining budgets are reducing the number of surveys that can be scheduled.Digital elevation and satellite spectral data are available or are obtainable for all areas in the continental United States and may be used as an aid to produce soils data. A study was conducted in the Grass Creek Resource Area in north-central Wyoming to assess the utility of incorporating digital elevation and Landsat data into an information base for soil survey and to evaluate the usefulness of these data as an input to an order-three soil survey.Slope-interval maps were produced from digital elevation data and topographic maps of three 7.5-minute quadrangle areas. These slope-interval maps were then overlaid on orthophotoquadrangles and used to produce photo-interpreted physiographic maps. These physiographic maps were digitized into a data base and used with Landsat multispectral scanner data to produce tabular summaries that describe each map polygon in terms of physiographic unit, slope, aspect, elevation, area, and spectral values. A good relationship was found between the physiographic units and soil mapping units
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