use of Landsat digital data and computer-implemented techniques for an agricultural application by Armond T Joyce

Cover of: use of Landsat digital data and computer-implemented techniques for an agricultural application | Armond T Joyce

Published by National Aeronautics and Space Administration, Scientific and Technical Information Office, For sale by the National Technical Information Service] in Washington, D.C, [Springfield, Va .

Written in English

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  • Landsat satellites -- Remote sensing,
  • Agriculture -- Data processing,
  • Agriculture -- Mississippi -- Washington County

Edition Notes

Book details

StatementArmond T. Joyce and R.H. Griffin II
SeriesNASA reference publication -- 1016
ContributionsGriffin, R. H, United States. National Aeronautics and Space Administration. Scientific and Technical Information Office, Lyndon B. Johnson Space Center
The Physical Object
Paginationv, 42 p. :
Number of Pages42
ID Numbers
Open LibraryOL14933706M

Download use of Landsat digital data and computer-implemented techniques for an agricultural application

Get this from a library. The use of Landsat digital data and computer-implemented techniques for an agricultural application. [Armond T Joyce; R H Griffin; United States.

National Aeronautics and Space Administration. Scientific and Technical Information Office.; Lyndon B. Johnson Space Center.]. THE USE OF LANDSAT DIGITAL DATA AND COMPUTER-IMPLEMENTED TECHNIQUES FOR AN AGRICULTURAL APPLICATION By Armond T. Joyce* and R. Griffin II* Lyndon B.

Johnson Space Center SUMMARY This report describes the use of a resource inventory system in. The use of Landsat digital data and computer-implemented techniques for an agricultural application, Armond T.

Joyce, R. Griffin, United States. National Aeronautics and Space Administration. Scientific and Technical Information Office, Lyndon B. Johnson Space Center,Technology &. CROP IDENTIFICATION AND AREA ESTIMATION BY COMPUTER-AIDED ANALYSIS OF LANDSAT DATA MARVIN E.

BAUER J MARILYN M. HIXSON J BARBARA J. DAVIS J AND JEANNE B. ETHERIDGE Purdue University ABSTRACT This report describes the results of a study involving the use of computer-aided analysis techniques applied to Landsat MSS data for iden­.

Two of the most popular satellite remote sensing data sources currently in use are Landsat Thematic Mapper (TM) and SPOT (Le Système Pour l'Observation de la Terre). The TM sensor is sensitive to blue wavelength light (band 1) that is valuable for its ability to travel through the water column.

Crop area estimation: a comparison of remote sensing and census methods containing the Landsat T M digital data for t to use Landsat data and computer-implemented pattern recognition to.

Archaeologists have successfully used aerial photography since the turn of the century and it continues to be an important research tool today. Multispectral scanners and computer-implemented analysis techniques extend the range of human vision and provide the investigator with innovative research designs at scales previously by: The application of a ratio estimator to determine confidence limits on land use change mapping Article in International Journal of Remote Sensing 27(10) · May with 9 Reads.

Spatiotemporal Hydrological Modelling with GIS for the Upper Mahaweli Catchment, Sri Lanka. Effective Use of Landsat Data in Hydrologic Models, Procedures for Gathering Ground Truth Information for a Supervised Approach to a Computer Implemented Land Cover Classification of Landsat Acquired Multi Spectral data, National Aeronautics Author: Ranjith Premalal de Silva.

Systems and methods for generation of images of a particular type from images of a different type are disclosed.

In an embodiment, an agricultural intelligence computer system receives a first plurality of images of a first type and a second plurality of images of a second type. The first and second types may refer to variances in resolution, frequency ranges of frequency bands, and/or types.

A system for identifying ponding water located on a field from image data is described. In an approach, an image of an agricultural field is analyzed using a classifier that has been trained based on the spectral bands of labeled image pixels to identify a probability for each pixel within the image that the pixel corresponds to water.

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In particular, data from the MODIS sensor are available every days, although at a much coarser spatial resolution than the Landsat data used in the creation of the NLCD (m vs. 30m). In this study, the effectiveness of strata created by classifying MODIS satellite imagery is compared to that of strata created from the NLCD.

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An originally non-georeferenced raster map, which may have resulted from scanning and storing a non-georeferenced paper map, is georeferenced with reference to a georeferenced map, such as a vector map. Points denoting common features, such as road intersections, on each map are captured as point-pairs.

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