Application of Ancillary Data In Post-Classification to Improve Forest Area Estimates In A Landsat TM Scene

dc.contributor.authorHoloviak, Brent Matthewen
dc.contributor.committeechairCarstensen, Laurence W.en
dc.contributor.committeememberWynne, Randolph H.en
dc.contributor.committeememberCampbell, James B. Jr.en
dc.description.abstractIn order to produce a more current inventory of forest estimates along with change estimates, the Forest Inventory Analysis (FIA) program has moved to an annual system in which 20% of the permanent plots in a state are surveyed. The previous system sampled permanent plots in 10-year intervals by sampling states sequentially in a cycle (Wayman 2001, USDA FIA). The move to an annual assessment has introduced the use satellite technology to produce forest estimates. Wayman et al (2001) researched the effectiveness of satellite technology in relation to aerial photo-interpretation, finding the satellite method to do an adequate job, but reporting over-estimations of forest area. This research extends the satellite method a step further, introducing the use of ancillary data in post-classification. The US Forest Service has well-defined definitions of forest and nonforest land-use in its (FIA) program. Using these definitions as parameters, post-classification techniques were developed to improve forest area estimates from the initial spectral classification. A goal of the study was to determine the accuracy of using readily available ancillary data. US Census data, TIGER street files, and local tax parcel data were used. An Urban Mask was created based on population density to mask out Forested pixels in a classified image. Logistic Regression was used to see if population density, street density, and land value were good predictors of forest/nonforest pixels. Research was also conducted on accuracy when using contiguity filters. The current filter used by the Virginia Department of Forestry (VDoF) was compared to functions available in ERDAS Imagine. These filters were applied as part of the post-classification techniques. Results show there was no significant difference in map accuracies at the 95% confidence interval using the ancillary data with filters in a post-classification sort. However, the use of ancillary data had liabilities depending on the resolution of the data and its application in overlay.en
dc.description.degreeMaster of Scienceen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.subjectAccuracy Assessmenten
dc.subjectUrban Masken
dc.subjectRemote Sensingen
dc.titleApplication of Ancillary Data In Post-Classification to Improve Forest Area Estimates In A Landsat TM Sceneen
dc.typeThesisen Polytechnic Institute and State Universityen of Scienceen


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