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  • 1
    Publication Date: 2019-06-27
    Description: Several considerations of winter wheat yield prediction using LANDSAT data were discussed. In addition, a simple technique which permits direct early season forecasts of wheat production was described.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Proc. of the 11th Intern. Symp. on Remote Sensing of Environment, Vol. 2; p 1245-1254
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  • 2
    Publication Date: 2019-06-27
    Description: Inappropriate spatial resolution and corresponding data processing techniques may be major causes for non-optimal forest classification results frequently achieved from multispectral scanner (MSS) data. Procedures and results of empirical investigations are studied to determine the influence of MSS spatial resolution on the classification of forest features into levels of detail or hierarchies of information that might be appropriate for nationwide forest surveys and detailed in-place inventories. Two somewhat different, but related studies are presented. The first consisted of establishing classification accuracies for several hierarchies of features as spatial resolution was progressively coarsened from (2 meters) squared to (64 meters) squared. The second investigated the capabilities for specialized processing techniques to improve upon the results of conventional processing procedures for both coarse and fine resolution data.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Proc. of the 11th Intern. Symp. on Remote Sensing of Environment, Vol. 2; p 1279-1288
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  • 3
    Publication Date: 2019-06-27
    Description: Empirical evidence was provided for the definition of system specifications for the LANDSAT Follow-On Thematic Mapper (TM) and other future space Multispectral Sensor (MSS) systems. Specific sensor parameters addressed were spatial resolution, radiometric sensitivity, and to a lesser degree spectral bandwidths and locations. The study used selected available aircraft MSS data, characterized by narrow spectral bands, fine spatial resolution, and high signal-to-noise, as the basis for simulating spacecraft TM data of various spatial resolutions, radiometric sensitivities, and sets of spectral bands. The primary measure used in evaluating the effects of varying spatial and radiometric resolutions was agricultural crop mensuration accuracy using automatic (computer) information extraction techniques.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: ERIM Proc. of the 11th Intern. Symp. on Remote Sensing of Environment, Vol. 1; p 693-701
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  • 4
    Publication Date: 2019-06-27
    Description: A detailed interpretation and analysis of selected corn blight data set was undertaken in order to better define the present capabilities and limitations of agricultural remote multispectral sensing and automatic processing techniques and to establish the areas of investigation needing futher attention in the development of operational survey systems. While the emphasis of this effort was directed toward the detection of various corn blight levels, problems related to the more general task of crop identification were also investigated. Since the analog recognition computer (SPARC) was fully committed to the more routine aspects of processing and since the detailed interpretation and analysis required more in the way of quantitative information, the CDC 1604 digital computer was employed.
    Keywords: GEOPHYSICS
    Type: NASA. Manned Spacecraft Center 4th ann. Earth Resources Program Review, Vol. 5; 26 p
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  • 5
    Publication Date: 2019-06-27
    Description: A proportion estimation technique for classification of multispectral scanner images is reported that uses data point averaging to extract and compute estimated proportions for a single average data point to classify spatial unresolved areas. Example extraction calculations of spectral signatures for bare soil, weeds, alfalfa, and barley prove quite accurate.
    Keywords: INSTRUMENTATION AND PHOTOGRAPHY
    Type: NASA. Manned Spacecraft Center 4th Ann. Earth Resources Program Rev., Vol. 2; 13 p
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  • 6
    Publication Date: 2019-06-27
    Description: Two data processing techniques were suggested as applicable to the large area survey problem. One approach was to use unsupervised classification (clustering) techniques. Investigation of this method showed that since the method did nothing to reduce the signal variability, the use of this method would be very time consuming and possibly inaccurate as well. The conclusion is that unsupervised classification techniques of themselves are not a solution to the large area survey problem. The other method investigated was the use of signature extension techniques. Such techniques function by normalizing the data to some reference condition. Thus signatures from an isolated area could be used to process large quantities of data. In this manner, ground information requirements and computer training are minimized. Several signature extension techniques were tested. The best of these allowed signatures to be extended between data sets collected four days and 80 miles apart with an average accuracy of better than 90%.
    Keywords: GENERAL
    Type: NASA-CR-134254 , ERIM-31650-152-T
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  • 7
    Publication Date: 2019-06-27
    Description: Conventional automatic data processing and information extraction techniques fall short of providing the information required by the user in some applications. For those cases, advanced techniques are needed to permit the extraction of the necessary information. Advanced techniques are described and examples of their application to ERTS-1 MSS data are provided. The techniques are designed to help overcome problems in location, mensuration, and classification accuracies which result from geometric distortions of the ERTS MSS data, the relatively coarse resolution of the sensor, and variations in atmospheric state over the region to be surveyed. It is shown that each of these factors can seriously degrade one's ability to extract necessary information. Further, it is shown that advanced techniques can alleviate the effects of these factors.
    Keywords: COMPUTERS
    Type: PAPER-I5 , NASA. Goddard Space Flight Center 3d ERTS-1 Symp., Vol. 1, Sect. B; p 1743-1772
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  • 8
    Publication Date: 2019-06-27
    Description: The author has identified the following significant results. Photointerpretation of S190A and S190B imagery showed significantly better resolution with the S190B system. A small tendancy to underestimate acreage was observed. This averaged 6 percent and varied with field size. The S190B system had adequate resolution for acreage measurement but the color film did not provide adequate contrast to allow detailed classification of ground cover from imagery of a single date. In total 78 percent of the fields were correctly classified but with 56 percent correct for the major crop, corn.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: E77-10010 , NASA-CR-150976
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  • 9
    Publication Date: 2019-06-28
    Description: Crop area and yield for winter wheat is found by analysis of LANDSAT pictures. Correlation between measure and yield is established from measurements on known wheatfields with known yields.
    Keywords: MATHEMATICS AND INFORMATION SCIENCES
    Type: GSC-12379 , NASA Tech Briefs (ISSN 0145-319X); 3; 4; P. 626
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  • 10
    Publication Date: 2019-07-13
    Description: Low-altitude aircraft scanner data were employed in simulating the spatial resolution, radiometric sensitivity and spectral bandwidth parameters of the proposed Landsat Thematic Mapper (TM). The 30 to 40 m resolution of the TM was found to provide significant improvement over current Landsat resolution (50 to 60 m) in crop mensuration, especially for Western Europe and India where field sizes average from one to four hectares. In terms of radiometric sensitivity, a noise equivalent reflectance value of 0.5% was held to be necessary for discrimination of spectrally similar data; in addition, all of the six proposed TM spectral bands were shown to be necessary for monitoring at some point during the growing season.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: International Symposium on Remote Sensing of Environment; Apr 25, 1977 - Apr 29, 1977; Ann Arbor, MI
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