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  • 1
    Publication Date: 2011-08-18
    Keywords: EARTH RESOURCES AND REMOTE SENSING
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  • 2
    Publication Date: 2011-08-18
    Description: Principal component and factor analysis techniques were applied to the spectral data collected over 27 field plots of various crops under varying agronomic conditions. The spectral data was integrated over the proposed thematic mapper bands and Landsat MSS spectral bands. The results were examined to compare the discrimination power of the thematic mapper. Previously announced in STAR as N81-33549
    Keywords: EARTH RESOURCES AND REMOTE SENSING
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  • 3
    Publication Date: 2011-08-18
    Description: A model, utilizing a direct relationship between remotely sensed spectral data and soybean development stage, has been proposed. The model is based upon transforming the spectral data in Landsat bands to greenness values over time and relating the area of this curve to soybean development stage. Soybean development stages were estimated from data acquired in 1978 from research plots at the Purdue University Agronomy Farm as well as Landsat data acquired over sample areas of the U.S. Corn Belt in 1978 and 1979. Analysis of spectral data from research plots revealed that the model works well with reasonable variation in planting date, row spacing, and soil background. The R-squared of calculated U.S. observed development stage exceeded 0.91 for all treatment variables. Using Landsat data the calculated U.S. observed development stage gave an R-squared of 0.89 in 1978 and 0.87 in 1979. No difference in the models performance could be detected between early and late planted fields, small and large fields, or high and low yielding fields.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
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  • 4
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    In:  Other Sources
    Publication Date: 2019-06-28
    Description: Digitally processed Seasat SAR imagery of the Denver, Colorado, area is analyzed with regard to the types of urban data that can be detected and/or inferred from satellite-borne L-band systems. Black-and-white images of the scene were generated at three scales to determine the advantages and detail discernible at each level of display. The large-scale imagery was density-sliced to evaluate the feasibility of producing a semiautomated land-cover classification from the SAR data. Gray level classes were assigned colors to aid interpretation and subsequently compared with the black-and-white images to assess the contribution of each technique and benefits of combining the data from both procedures.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Remote Sensing of Environment; 12; Dec. 198
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