<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Robert H. Haas</dc:contributor>
  <dc:creator>David C. Johnston</dc:creator>
  <dc:date>1985</dc:date>
  <dc:description>&lt;p&gt;A range forage utilization study on the Crow Creek Indian Reservation in central South Dakota provided the opportunity to use Landsat multispectral scanner (MSS) data for examining range condition trends.  A procedure was developed to compare change in spectral reflectance over time for polygon areas, defined by resource type within management units.  A t-test was used to evaluate changes in brightness and greenness within pastures between September 27, 1978, and September 18, 1983.  The first principal component transformation from four-band MSS images for both dates was used as a measure of brightness.  Greenness was measure using the second principal component transformation for both dates.  Examination of the brightness date showed that the assumptions required for a valid t-test were met.  The greenness data violated the assumption of independence between dates and was not used for trend comparisons.  The t-values calculated from each polygon were coded into three groups: (1) those indicating significant brightness decrease, (2) those indicating significant brightness increase, and (3) those indicating no significant brightness change.  Significance was determine at the 5-percent level.  These results were formatted into an image, which is a preliminary product for evaluating range condition trends over a 5-year period.&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:language>en</dc:language>
  <dc:publisher>American Society for Photogrammetry and Remote Sensing</dc:publisher>
  <dc:title>Change detection in rangeland environments using Landsat MSS data: A quantitative approach</dc:title>
  <dc:type>text</dc:type>
</oai_dc:dc>