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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

An analysis of palustrine forested wetland compensation effectiveness in Virginia

Atkinson, Robert B. 19 October 2005 (has links)
Plans to construct a wetland to replace wetland losses has become a common feature of permit requests. The purpose of this project is to suggest a methodology for quantifying the effectiveness of palustrine forested wetland construction in Virginia. Wetlands constructed by ~ne Virginia Department of Transportation and the U.S. Army Corps of Engineers were surveyed and Wagner Road constructed wetland in Petersburg, Virginia was selected as the primary study site. Chapter One of the present study suggests a method for early assessment of revegetation success utilizing weighted averages of colonizing vegetation. An adjacent reference site was chosen that was in close proximity to the constructed site and was used for comparison. Results from the Wagner Road site and the reference wetland indicated that colonizing vegetation weighted averages provide a more sensitive measure of revegetation success than the methods described in the federal wetland delineation manual. / Ph. D.
2

The effect of thresholding a maximum likelihood classifier on the accuracy of a landsat classification of a forested wetland

Agnello, Jennie M. 14 November 2012 (has links)
Although the maximum likelihood classifier is a popular classification technique, there is an inherent problem associated with the 100% classification of a scene. This is because there will inevitably be pixels within a study area that have a low probability of belonging to any of the predefined categories. The focus of this research was to locate these low probability pixels and observe their affect on classification accuracy. This was done by performing supervised classifications at various threshold levels using two methods of classification training combined category training site statistics and separated category training site statistics. In general, it was found that a majority of the scene was classified at very low probabilities but the accuracy of the resulting classifications was much greater than the low probabilities would suggest. / Master of Science

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