Title page for ETD etd-05262000-13410035


Type of Document Master's Thesis
Author Wayman, Jared Paul
Author's Email Address jwayman@vt.edu
URN etd-05262000-13410035
Title Landsat TM-Based Forest Area Estimation Using Iterative Guided Spectral Class Rejection
Degree Master of Science
Department Forestry
Advisory Committee
Advisor Name Title
Wynne, Randolph H. Committee Chair
Campbell, James B. Jr. Committee Member
Oderwald, Richard G. Committee Member
Scrivani, John A. Committee Member
Keywords
  • Landsat TM
  • FIA
  • Forest Area Estimation
  • IGSCR
Date of Defense 2000-05-15
Availability unrestricted
Abstract
In cooperation with the USDA Forest Service Southern Research Station, an algorithm has been developed to replace the current aerial-photography-derived FIA Phase 1 estimates of forest/non-forest with a Landsat Thematic Mapper-based forest area estimation. Corrected area estimates were obtained using a new hybrid classifier called Iterative Guided Spectral Class Rejection (IGSCR) for portions of three physiographic regions of Virginia. Corrected area estimates were also derived using the Landsat Thematic Mapper-based Multi-Resolution Land Characteristic Interagency Consortium (MRLC) cover maps. Both satellite-based corrected area estimates were tested against the traditional photo-based estimates. Forest area estimates were not significantly different (at the 95% level) between the traditional FIA, IGSCR, and MRLC methods, although the precision of the satellite-based estimates was lower. The estimated percent forest area and the standard error (respectively) of the estimates for each region and method are as follows; Coastal Plain- Phase 1 66.06% and 1.08%, IGSCR 68.88% and 2.93%, MRLC 69.84% and 3.08%. Piedmont- Phase 1 63.87% and 1.91%, IGSCR 65.52% and 3.50%, MRLC 59.19% and 3.83%. Ridge and Valley- Phase 1 69.74% and 1.22%, IGSCR 70.02%, and 2.43%, MRLC 70.53% and 2.52%.

Map accuracies were not significantly different (at the 95% level) between the IGSCR method and the MRLC method. Overall accuracies ranged from 80% to 89% using FIA definitions of forest and non-forest land use. Given standardization of the image rectification process and training data properties, the IGSCR methodology is objective and repeatable across users, regions, and time and outperforms the MRLC for FIA applications.

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