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dc.contributor.authorHayashi, Rei
dc.date.accessioned2014-03-04T02:27:04Z
dc.date.available2014-03-04T02:27:04Z
dc.date.issued2006-12
dc.identifier.otherhayashi_rei_200612_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/hayashi_rei_200612_ms
dc.identifier.urihttp://hdl.handle.net/10724/23637
dc.description.abstractThe objective of this study was to assess the applicability of using TM imagery for estimating above ground biomass (AGB) in a small forestland. Based on a single Landsat TM image, spectral reflectance from six TM bands and three vegetation indices was correlated to ground-based AGB measurements using regression analysis to develop AGB models. The fit of 2models was evaluated using the coefficient of determination (R). An accuracy assessment using independent test points was performed for the best model. Overall this study found that lower 22numbers of training points resulted in higher R; however, models with high R did not show high accuracy levels when validated against an independent sample of data. Predicted values for AGB models were consistent among models that modeled: all tree species; all tree species in interior forest stands; hardwood stands; or pine stands.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectLandsat TM
dc.subjectabove ground biomass
dc.subjectregression analysis
dc.subjecttraining points
dc.subjectregression models
dc.subjectremote sensing
dc.subjectKyoto Protocol
dc.subjectcarbon budget trading
dc.titleAbove ground biomass estimation in a 285-hectare site of mixed pine-hardwood using landsat thematic mapper imagery and regression analysis
dc.typeThesis
dc.description.degreeMS
dc.description.departmentForest Resources
dc.description.majorForest Resources
dc.description.advisorPete Bettinger
dc.description.committeePete Bettinger
dc.description.committeeNathan Nibbelink
dc.description.committeeJeff Hepinstall


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