The upper Colorado River drainage system yields approximately 104,000 acre-feet of silt annually to the Colorado River. In an attempt to reduce the silt load, federal land management agencies have installed numerous land surface treatments. A study was conducted to measure the economic benefits of the land treatments near Cisco, Utah, and to compare them to the treatment costs and to develop predictive criteria for estimating the optimum intensity of treatment. The economic evaluation was done in a benefit-cost framework and the criteria for estimating optimum intensity of treatment was done in a production-function framework. The land treatments were found to be effective in retaining silt, but treatment apparently resulted in decreased livestock carrying capacity. Over-all, the land treatments were found to be uneconomical.

Practical assessments of range production and utilization are based on forage weight estimates. In preparing these estimates moisture content in green vegetation offers some problems. The moisture component is not likely to be constant for a given species. Diurnal, seasonal and site variability have been well illustrated for agronomic and tree species (Salisbury, 1848; Jenkins, 1879; Miller, 1917; Pearson, 1924; Watkins, 1940; Parker, 1951; Ackley, 1954; Werner, 1954; Zohary and Orshan, 1956; Slatyer, 1959; Kozlowaki, 1965 and Jame son, 1966). Since variability is also likely for range plants, computations made on green weights are apt to be fallacious. It is a common practice, therefore, to express production on "water free" or "dry weight" basis. But the estimates of dry weight are made difficult by variations in herbage moisture. A variety of factors, relevant both to the vegetation and the site it occupies, would seem to account for variable moisture content. The prevalent methods for estimating moisture, however, seem to be more of a legacy from the past than an appreciation of ecological influences. Earlier investigators of pastures and fodder crops were largely agronomists interested in comparing yields. They were concerned primarily with irrigated crops where soil moisture is not a limiting factor and the ecological influences, such as humidity, rain, cloudy weather, dew, shade, exposure etc. are far from dominant (Atwater, 1869; Collier, 1881; Richardson , 1884; Ladd, 1888; Richardson, 1889; Morse, 1891 and Widstoe, 1897). The variations in water content and other components were accordingly related to stage of growth. Taking a cue from these studies agencies such as the United States Forest Service and Soil Conservation Service came to use certain reducing factors to convert green weight of range forage into dry weight. In developing these factors the type of vegetation and growth phases have been considered but ecological features and context have been neglected. The methodology adopted from pasture conditions became the accepted basis for making range management decisions (Range Memo, SCS-8, Soil Conservation Service, 1963; Range Analysis, Region IV, Forest Service, 1964). The influence of features of environment, particularly aspect, on growth differential, has long been recognized by foresters (Schlich, 1905; Champion, 1928 and Tourney, 1928). Plant physiologists have been aware of the significance of time-of-day on plant water for some time (Shreve, 1914; Miller, 1917). It is very probable that these influences express themselves in moisture content of herbage also. The investigations reported herein were conducted to define and assess the scope and intensity of some of these ecological features in modification of the moisture component of herbage. The objective is to determine whether differences in ecological context influence range herbage moisture to a sufficient extent to warrant consideration in developing conversion factors for deriving dry weights from green weights of vegetation samples. The appraisal should reaffirm present assumptions applied or yield more accurate adjustments for estimating forage production. In either circumstance the results should enhance the scientific basis of range management decisions.

Monitoring and identifying the state of rangelands on a landscape scale can be a time consuming process. In this thesis, remote sensing imagery has been used to show how the process of classifying different ecological sites and states can be done on a per pixel basis for a large landscape. Twenty-seven years' worth of remotely sensed imagery was collected, atmospherically corrected, and radiometrically normalized. Several vegetation indices were extracted from the imagery along with derivatives from a digital elevation model. Dominant vegetation components from five major ecological sites in Rich County, Utah, were chosen for study. The vegetation components were Aspen, Douglas-fir, Utah juniper, mountain big sagebrush, and Wyoming big sagebrush. Training sites were extracted from within map units with a majority of one of the five ecological sites. A Random Forests decision tree model was developed using an attribute table populated with spectral biophysical variables derived from the training sites. The overall out-of-bag accuracy for the Random Forests model was 97.2%. The model was then applied to the predictor spectral and biophysical variables to spatially map the five major vegetation components for all of Rich County. Each vegetation class had greater than 90% accuracies except for Utah juniper at 81%. This process is further explained in chapter 2. As a follow-on effort, we attempted to classify vegetation ecological states within a single ecological site (Wyoming big sagebrush). This was done using field data collected by previous studies as training data for all five ecological states documented for our chosen ecological site. A Maximum Likelihood classifier was applied to four years of Landsat 5 Thematic Mapper imagery to map each ecological state to pixels coincident to the map units correlated to the Wyoming big sagebrush ecological site. We used the Mahalanobis distance metric as an indicator of pixel membership to the Wyoming big sagebrush ecological site. Overall classification accuracy for the different ecological states was 64.7% for pixels with low Mahalanobis distance and less than 25% for higher distances.