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Wiegand, C. L.

Publications and source records attributed to Wiegand, C. L..

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Reflectance of vegetation, soil, and water

The author has identified the following significant results. A study was conducted in a 340-acre (139 hectares) field of grain sorghum (Sorghum bicolor (L.) Moench) to determine if multispectral data from ERTS-1 could be used to detect differences in chlorophyll concentration between iron-deficient (chlorotic) and apparently normal (green) grain sorghum. Chlorotic sorghum areas 2.8 acres (1.1 hectares) or larger in size were identified on a computer printout of band 5 data which contains the chlorophyll absorption band at the 0.65 micron wavelength. ERTS resolution is sufficient for practical applications in detecting iron-deficient sorghum in otherwise uniform fields. The first classification map of the study county has been produced. Vegetation (crops), rangeland, bare soil, water, and an undefined (all other) category occupied 15.2, 45.0, 19.1, 0.02, and 20.6% of the land area, respectively.

Wiegand, C. L.↗
NASA NTRS1973

Reflectance of vegetation, soil, and water

The author has identified the following significant results. A ratio of MSS channels 5 and 7 (5/7) and 5 to 6 (5/6) signals resulted in a correct recognition of 86.9% of the members of representative crop and soil conditions, compared with recognitions of 60.0, 64.1, 74.1, and 81.4% for channels 4, 5, 6, and 7 taken individually. Based on this result a satellite channel ratio procedure has been developed that enhances line printer gray maps for more efficient experimental test site location in the CCT data. Because independent estimates are not available to judge acreage estmates derived from ERTS-1 data against, except for a few crops, an interpenetrating sample constituting 3.5% of the county is ground truthed periodically. The crop of land uses and their acreages, respectively, as estimated from the interpenetrating samples, are: cotton, 129, 714; sorghum, 182,783; mixed citrus, 53,954; oranges, 16,929; grapefruit, 13,863; rangeland, 137,845; and, improved pastures, 57.169.

Wiegand, C. L.↗
NASA NTRS1973

Reflectance of vegetation, soil, and water

The author has identified the following significant results. The ability to read the 24-channel MSS CCT tapes, select specified agricultural land use areas from the CCT, and perform multivariate statistical and pattern recognition analyses has been demonstrated. The 5 optimum channels chosen for classifying an agricultural scene were, in the order of their selection the far red visible, short reflective IR, visible blue, thermal infrared, and ultraviolet portions of the electromagnetic spectrum, respectively. Although chosen by a training set containing only vegetal categories, the optimum 4 channels discriminated pavement, water, bare soil, and building roofs, as well as the vegetal categories. Among the vegetal categories, sugar cane and cotton had distinctive signatures that distinguished them from grass and citrus. Acreages estimated spectrally by the computer for the test scene were acceptably close to acreages estimated from aerial photographs for cotton, sugar cane, and water. Many nonfarmable land resolution elements representing drainage ditch, field road, and highway right-of-way as well as farm headquarters area fell into the grass, bare soil plus weeds, and citrus categories and lessened the accuracy of the farmable acreage estimates in these categories. The expertise developed using the 24-channel data will be applied to the ERTS-1 data.

Wiegand, C. L.↗
NASA NTRS1973

Reflectance of vegetation, soil, and water

There are no author-identified significant results in this report. This report deals with the selection of the best channels from the 24-channel aircraft data to represent crop and soil conditions. A three-step procedure has been developed that involves using univariate statistics and an F-ratio test to indicate the best 14 channels. From the 14, the 10 best channels are selected by a multivariate stochastic process. The third step involves the pattern recognition procedures developed in the data analysis plan. Indications are that the procedures in use are satsifactory and will extract the desired information from the data.

Wiegand, C. L.↗
NASA NTRS1973

Temporal analysis of multispectral scanner data.

Multispectral scanner reflectance data were sampled for bare soil, cotton, sorghum, corn, and citrus at four dates during a growing season (April, May, June, and July 1969) to develop a time-dependent signature for crop and soil discrimination. Discrimination tests were conducted for single-date and multidate formats using training and test data sets. For classifications containing several crops, the multidate or temporal approach improved discrimination compared with the single-date approach. The multidate approach also preserved recognition accuracy better in going from training fields to test fields than the single-date analysis. The spectral distinctiveness of bare soil versus vegetation resulted in essentially equal discrimination using single-date versus multidate data for those two categories.

Richardson, A. J.↗
NASA NTRS1972

Discriminant analyses of Bendix scanner data

Flights over Weslaco, Texas are discussed, using the 9-channel Bendix scanner, providing calibrated data in the 380 to 1000 nm wavelength interval. These flights were at 2000 ft. These data gave seasonal coverage from the time signals, representing mainly the soil background. The ground truth data are provided; signature processing studies relating scanner data to ground truth were also carried out.

Richardson, A. J.↗
NASA NTRS1972

Physiological factors and optical parameters as bases of vegetation discrimination and stress analysis.

The successful application of photographic, optical-mechanical scanning, and other sensing techniques to vegetation must be based on an understanding of the biological and physical principles involved. The objective is to review the biological and optical characteristics of vegetation and to discuss how knowledge of them can support operational programs by aiding in wavelength selection, timing of missions, and discriminations between vegetation types as well as between healthy and stressed plants of the same type. Both laboratory situations, in which relatively pure spectral data are obtained, and field situations are considered. In the latter, sensor performance and background variables affect data quality.

Wiegand, C. L.↗
NASA NTRS1971

Leaf light reflectance, transmittance, absorptance, and optical and geometrical parameters for eleven plant genera with different leaf mesophyll arrangements.

Review of research on radiation interactions within plant canopies and communities and interactions of various leaf structures (mesophyll arrangements) with electromagnetic radiation involved in the interpretation of data sensed from air or spacecraft. The hypothesis underlying the research reported is that leaf mesophyll arrangements influence spectral energy measurements of leaves.

Gausman, H. W.↗