An assessment of methods for clearcut detection using Landsat satellite images of Nova Scotia

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dc.contributor.advisor Giles, Philip, 1967-
dc.coverage.spatial Nova Scotia
dc.creator Therrien, Marc Andre
dc.date.accessioned 2015-08-13T15:06:36Z
dc.date.available 2015-08-13T15:06:36Z
dc.date.issued 2015
dc.identifier.uri http://library2.smu.ca/xmlui/handle/01/26289
dc.description 1 online resource (ix, 86 p.) : col. ill., col. map
dc.description Includes abstract.
dc.description Includes bibliographical references (p. 83-86).
dc.description.abstract In an attempt to replicate Global Forest Watch Canada (2009) and Weeks, et al. (2013), this research project evaluated five core methods to determine which one should be used for clearcut detection. The five methods assessed were: Band 5 Differencing, Band 7 Differencing, Normalized Difference Vegetation Index Differencing, Enhanced Wetness Difference Index Differencing, and Unsupervised Classification. The project used three phases to determine which method was the most accurate, whether or not a supervised classification increased method accuracy, and whether an increase in the interval between images dates had an impact on method accuracy. It was hypothesized that Normalized Difference Vegetation Index Differencing would be the most accurate method, a supervised classification would increase accuracy, and an increase in time interval between image dates would decrease method accuracy. A set of study areas in Nova Scotia were selected for the project. Trout Lake Training Area was used to learn how clearcut pixels appeared on Landsat images by using a high resolution IKONOS satellite image as a reference. The five aforementioned methods, were then applied to the Long Lake Evaluation Area. A supervised classification was conducted prior to method implementation for Governors Lake Evaluation Area, in an attempt to determine if the methods’ accuracy would increase. Once the first two phases were completed, the methods were assessed for accuracy. The most accurate method was applied to Kelly Lake Application Area, Guysborough County, to determine if the accuracy would decrease with an increased time interval between image dates. Band 7 Differencing post-supervised classification was determined to be the most accurate method, contrary to the hypothesis. The supervised classification did increase accuracy of clearcut detection. When Band 7 Differencing with prior supervised classification was applied to images with an increased time interval, the accuracy decreased. en_CA
dc.language.iso en en_CA
dc.publisher Halifax, N.S. : Saint Mary's University
dc.title An assessment of methods for clearcut detection using Landsat satellite images of Nova Scotia en_CA
dc.type Text en_CA
thesis.degree.name Bachelor of Arts (Honours Geography)
thesis.degree.level Undergraduate
thesis.degree.discipline Geography and Environmental Studies
thesis.degree.grantor Saint Mary's University (Halifax, N.S.)


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