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dc.contributor.authorMusy, Rebecca Foresten_US
dc.date.accessioned2014-03-14T20:37:48Z
dc.date.available2014-03-14T20:37:48Z
dc.date.issued2003-04-17en_US
dc.identifier.otheretd-05212003-031835en_US
dc.identifier.urihttp://hdl.handle.net/10919/33053
dc.description.abstractThe goal of this project was to develop an operational Landsat TM image classification protocol for FIA forest area estimation. A hybrid classifier known as Iterative Guided Spectral Class Rejection (IGSCR) was automated using the ERDAS C Toolkit and ERDAS Macro Language. The resulting program was tested on 4 Landsat ETM+ images using training data collected via region-growing at 200 random points within each image. The classified images were spatially post-processed using variations on a 3x3 majority filter and a clump and eliminate technique. The accuracy of the images was assessed using the center land use of all plots, and subsets containing plots with 50, 75 and 100% homogeneity. The overall classification accuracies ranged from 81.9-95.4%. The forest area estimates derived from all image, filter and accuracy set combinations met the USDA Forest Service precision requirement of less than 3% per million acres timberland. There were no consistently significant filtering effects at the 95% level; however, the 3x3 majority filter significantly improved the accuracy of the most fragmented image and did not decrease the accuracy of the other images. Overall accuracy increased with homogeneity of the plots used in the validation set and decreased with fragmentation (estimated by % edge; R2 = 0.932). We conclude that the use of random points to initiate training data collection via region-growing may be an acceptable and repeatable addition to the IGSCR protocol, if the training data are representative of the spectral characteristics of the image. We recommend 3x3 majority filtering for all images, and, if it would not bias the sample, the selection of validation data using a plot homogeneity requirement rather than plot center land use only. These protocol refinements, along with the automation of IGSCR, make IGSCR suitable for use by the USDA Forest Service in the operational classification of Landsat imagery for forest area estimation.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartRFM_thesis.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectFIAen_US
dc.subjectLandsat ETM+en_US
dc.subjectIGSCRen_US
dc.subjectForest Area Estimationen_US
dc.subjectPostprocessingen_US
dc.titleRefinement of Automated Forest Area Estimation via Iterative Guided Spectral Class Rejectionen_US
dc.typeThesisen_US
dc.contributor.departmentForestryen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineForestryen_US
dc.contributor.committeechairWynne, Randolph H.en_US
dc.contributor.committeememberPrisley, Stephen P.en_US
dc.contributor.committeememberOderwald, Richard G.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05212003-031835/en_US
dc.date.sdate2003-05-21en_US
dc.date.rdate2003-06-30
dc.date.adate2003-06-30en_US


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