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At least 271 records · Page 15

Detection of soil moisture and snow characteristics from Skylab

The author has identified the following significant results. The most significant result is the good response of the passive radiometers, particularly the L-band radiometer, to changing soil moisture conditions near the surface of the earth. Radiometer response was very good for the five complete data sets consisting of three passes across Texas and two passes across Kansas. When data from the five different passes were combined, the correlation between the S194 radiometric temperature and soil moisture content remained high with a value of -0.96. The performance of the S193 passive radiometer was less consistent; however, one data set gave a very high correlation of -0.95. The scatterometer response to soil moisture at incidence angles near 30 deg was not as good as for the radiometers.

Eagleman, J. R.↗

Skylab study of water quality

The author has identified the following significant results. Apparent reflectance levels in the Skylab S190A and S192 bands, from one pass over three Kansas reservoirs, exhibit good statistical correlation with suspended solids. Band ratios appear to yield the best results. The concentration of suspended solids, mostly inorganic sediment, has the most effect on the reflected energy. Dissolved solids concentrations up to 200 ppm were not detectable by the Skylab sensors.

Yarger, H. L.↗

Utilization of LANDSAT imagery for mapping vegetation on the millionth scale

A series of test sites were examined to determine if the information content of the LANDSAT imagery that may be obtained of these sites is sufficient to permit their mapping according to the vegetation classification system recently published by Unesco. These sites include examples from the humid tropics, arid and semi-arid subtropics and temperature zones: Western Highlands of Papua New Guinea, Mindoro Island in the Philippines, Great Smoky Mountains of the southeastern United States, East Tennessee Valley, interior of Western Australia, northeastern Uganda, and south-central Kansas. The results of the experiment were presented in the form of vegetation maps and annotated images which serve to illustrate the detectability of various formations. It was concluded that, for the test sites examined, the formations of the Unesco vegetation classification can be satisfactorily distinguished on LANDSAT MSS images, especially when used as color composites and judiciously chosen as to season.

Williams, D. L.↗

Quantitative water quality with LANDSAT and Skylab

Correlation studies were completed between LANDSAT Multispectral Scanner (MSS) band ratios derived from computer compatible tape (CCT) and 170 water samples taken from three large Kansas reservoirs, coincident with 16 different LANDSAT passes over a 13 month period. The following conclusions were obtained: (1) LANDSAT MSS reflectance levels are useful for quantitative measurement of suspended solids up to at least 900 ppm, (2) MSS band ratios derived from CCT can measure suspended solids with 67% confidence level accuracy of 12 ppm over the range 0-80 ppm and 35 ppm over the range 0900 ppm, (3) suspended solids contour maps can be easily constructed from CCT for water bodies larger than approximately 100 acres, (4) rationing suppresses MSS reflectance level dependence on seasonal sun angle variation and permits measurement of suspended load the year round in the middle latitudes. SKYLAB imagery from a single pass over three reservoirs compares favorably to LANDSAT results up to 100 ppm.

Yarger, H. L.↗

Soil moisture detection from Skylab

An investigation was designed for the Skylab satellite to determine the feasibility of remote sensing of the soil moisture content of the surface from various microwave sensors. Skylab data for the experiment were collected during passes 5, 10, 16, and 38 across the two test sites selected in eastern Kansas and western Texas. Pass 38 covered both test sites giving five data sets for the analysis. As Skylab data were being taken the moisture content of the soil was sampled by ground crews for each 2.5 centimeter depth from the surface to 15 centimeters at interval of about six kilometers along two different routes along the test sites. This resulted in a total of 2250 soil moisture samples corresponding to different locations and six different depths. Skylab data were collected by passive microwave radiometers at wavelengths of 2.1 and 21 centimeters by the S193 and S194 microwave sensors. An active microwave system also collected scatterometer data at a wavelength of 2.1 centimeters. The analysis of microwave data has revealed that the longer wavelength L-Band passive radiometer gives the best correlation with soil moisture content of the upper 2.5 centimeter depth of soil.

Eagleman, J. R.↗

Investigation of remote sensing to detect near-surface groundwater on irrigated lands

The application of remote sensing techniques was studied for detecting areas with high water tables in irrigated agricultural lands. Aerial data were collected by the LANDSAT-1 satellite and aircraft over the Kansas/Bostwick Irrigation District in Republic and Jewell Counties, Kansas. LANDSAT-1 data for May 12 and August 10, 1973, and aircraft flights (midday and predawn) on August 10 and 11, 1973, and June 25 and 26, 1974, were obtained. Surface and water table contour maps and active observation well hydrographs were obtained from the Bureau of Reclamation for use in the analysis. Results of the study reveal that LANDSAT-1 data (May MSS band 6 and August MSS band 7) correlate significantly (0.01 level) with water table depth for 144 active observation wells located throughout the Kansas/Bostwick Irrigation District. However, a map of water table depths of less than 1.83 meters prepared from the LANDSAT-1 data did not compare favorably with a map of seeped lands of less than 1.22 m (4 feet) to the water table. Field evaluation of the map is necessary for a complete analysis. Analysis of three fields on a within or single-field basis for the 1973 LANDSAT-1 data also showed significant correlation results.

Ryland, D. W.↗

Crop identification and acreage estimation over large geographic areas using LANDSAT MSS data

The author has identified the following significant results. The comparison between the acreage estimates for the April LANDSAT data and the USDA Statistical Reporting Service estimates show no significant difference for the south central crop reporting district in Kansas. A paired-t test with an alpha = .05 was run comparing the percentages of wheat in each county. The results of their test showed no significant difference between the two estimates for wheat.

Bauer, M. E.↗

Technology and politics: The regional airport experience

The findings of a comparative study of the following six regional airports were presented: Dallas/Fort Worth, Kansas City, Washington, D.C., Montreal, Tampa, and St. Louis. Each case was approached as a unique historical entity, in order to investigate common elements such as: the use of predictive models in planning, the role of symbolism to heighten dramatic effects, the roles of community and professional elites, and design flexibility. Some of the factors considered were: site selection, consolidation of airline service, accessibility, land availability and cost, safety, nuisance, and pollution constraints, economic growth, expectation of regional growth, the demand forecasting conundrum, and design decisions. The hypotheses developed include the following: the effect of political, social, and economic conflicts, the stress on large capacity and dramatic, high-technology design, projections of rapid growth to explain the need for large capital outlays.

Starling, J. D.↗

Extracting scene feature vectors through modeling, volume 3

The remote estimation of the leaf area index of winter wheat at Finney County, Kansas was studied. The procedure developed consists of three activities: (1) field measurements; (2) model simulations; and (3) response classifications. The first activity is designed to identify model input parameters and develop a model evaluation data set. A stochastic plant canopy reflectance model is employed to simulate reflectance in the LANDSAT bands as a function of leaf area index for two phenological stages. An atmospheric model is used to translate these surface reflectances into simulated satellite radiance. A divergence classifier determines the relative similarity between model derived spectral responses and those of areas with unknown leaf area index. The unknown areas are assigned the index associated with the closest model response. This research demonstrated that the SRVC canopy reflectance model is appropriate for wheat scenes and that broad categories of leaf area index can be inferred from the procedure developed.

Berry, J. K.↗

Lacie phase 1 Classification and Mensuration Subsystem (CAMS) rework experiment

An experiment was designed to test the ability of the Classification and Mensuration Subsystem rework operations to improve wheat proportion estimates for segments that had been processed previously. Sites selected for the experiment included three in Kansas and three in Texas, with the remaining five distributed in Montana and North and South Dakota. The acquisition dates were selected to be representative of imagery available in actual operations. No more than one acquisition per biophase were used, and biophases were determined by actual crop calendars. All sites were worked by each of four Analyst-Interpreter/Data Processing Analyst Teams who reviewed the initial processing of each segment and accepted or reworked it for an estimate of the proportion of small grains in the segment. Classification results, acquisitions and classification errors and performance results between CAMS regular and ITS rework are tabulated.

Chhikara, R. S.↗

Applications of Skylab EREP photographs to mapping landforms and environmental geomorphology in the Great Plains and Midwest

The following evaluations of Skylab photographs were undertaken: (1) the 1290 Skylab S190A and S190B photographs of Illinois, Iowa, Kansas, Missouri, Nebraska, and South Dakota were evaluated in detail in terms of coverage, cloud cover, photographic quality, endlap, detectability of roads and stereorelief, and utility for geomorphologic mapping, and (2) the utility of the Skylab photos were tested for interpretive analytic mapping of geomorphologic features over large areas representative of different parts of this region. Photointerpretative maps of analytic geomorphology were obtained for various test areas representative of the varied landscapes in the region. These maps are useful for regional land-use planning, ground-water exploration, and other environmental geomorphologic-geologic applications. Compared with LANDSAT-1 MSS images, Skylab photos afford almost as extensive overviews of large areas but in considerably greater detail, and for many SL photos, moderate stereorelief. However, repetitive multiseasonal, cloud-free coverage by high-quality photos is very limited and many areas have no coverage at all.

Morrison, R. B.↗

Remote sensing of soil moisture by a 21-cm passive radiometer

Microwave sensors on Skylab collected data as part of the Earth Resources Experiment Package. An investigation was designed to obtain field observations of soil moisture content for comparison with data from Skylab. The 21-cm radiometer has been shown to be highly responsive to the moisture content of the upper 2.5-cm layer of soil. A composite relationship between the radiometric temperature and soil moisture content has been determined from five data sets obtained over Kansas and Texas. This relationship, having a correlation index of -0.96, has been used as a basis for calculating the soil moisture content of large areas across the United States.

Eagleman, J. R.↗

Evaluation of algorithms for estimating wheat acreage from multispectral scanner data

The author has identified the following significant results. Fourteen different classification algorithms were tested for their ability to estimate the proportion of wheat in an area. For some algorithms, accuracy of classification in field centers was observed. The data base consisted of ground truth and LANDSAT data from 55 sections (1 x 1 mile) from five LACIE intensive test sites in Kansas and Texas. Signatures obtained from training fields selected at random from the ground truth were generally representative of the data distribution patterns. LIMMIX, an algorithm that chooses a pure signature when the data point is close enough to a signature mean and otherwise chooses the best mixture of a pair of signatures, reduced the average absolute error to 6.1% and the bias to 1.0%. QRULE run with a null test achieved a similar reduction.

Nalepka, R. F.↗

Signature extension for spectral variation in soils, volume 4

The reduced 1975-1976 field data at Garden City, Kansas are presented. These data are being used to evaluate the SRVC model predictions, to compare the ERIM-SUITS model with both the SRVC results and field data, and finally, to provide a data base for reviewing multitemporal trajectories. In particular, the applicability of the tasselled cap transformation is reviewed. The first detailed verification of this approach utilizing actual field measured data from the LACIE field measurement program, rather than LANDSAT data, is given.

Berry, J. K.↗

Crop identification and area estimation over large geographic areas using LANDSAT MSS data

The author has identified the following significant results. LANDSAT MSS data was adequate to accurately identify wheat in Kansas; corn and soybean estimates in Indiana were less accurate. Computer-aided analysis techniques were effectively used to extract crop identification information from LANDSAT data. Systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county, district, and state levels. Training statistics were successfully extended from one county to other counties having similar crops and soils if the training areas sampled the total variation of the area to be classified.

Bauer, M. E.↗

Wheat signature modeling and analysis for improved training statistics

The author has identified the following significant results. The spectral, spatial, and temporal characteristics of wheat and other signatures in LANDSAT multispectral scanner data were examined through empirical analysis and simulation. Irrigation patterns varied widely within Kansas; 88 percent of wheat acreage in Finney was irrigated and 24 percent in Morton, as opposed to less than 3 percent for western 2/3's of the State. The irrigation practice was definitely correlated with the observed spectral response; wheat variety differences produced observable spectral differences due to leaf coloration and different dates of maturation. Between-field differences were generally greater than within-field differences, and boundary pixels produced spectral features distinct from those within field centers. Multiclass boundary pixels contributed much of the observed bias in proportion estimates. The variability between signatures obtained by different draws of training data decreased as the sample size became larger; also, the resulting signatures became more robust and the particular decision threshold value became less important.

Nalepka, R. F.↗

Applications of remote sensing, volume 2

The author has identified the following significant results. The overall spectral response of the strata measured by a mean vector and covariance matrix for each stratum did not show differences among the LACIE phase 3 strata using the machine clustering procedures. This was expected since the large strata gave rise to broad normal distributions with a great deal of overlap. The static stratification of Kansas contains strata which are small in size. The distributions for these strata are not as broad as those based on the LACIE phase 3 partitions, but there is still some confusion since strata from different categories are not spectrally distinct.

Landgrebe, D. A.↗

Selecting class weights to minimize classification bias in acreage estimation

Preliminary results of experiments being performed to select optimal class weights for use with the maximum likelihood classifier in acreage estimation using remote sensor imagery are presented. These weights will be optimal in the sense that the bias will be minimized in the proportion estimate obtained from the classification results by sample counting. The procedure was tested using Landsat MSS data from an 8 by 9.6 km area of ground truth in Finney County, Kansas.

Belcher, W. M.↗