Rapid classification of landsat TM imagery for phase 1 stratification using the automated NDVI threshold supervised classification (ANTSC) methodologyAuthor(s): William H. Cooke; Dennis M. Jacobs
Source: In: Proceedings of the Fourh Annual Forest Inventory and Analysis Symposium, 81-86
Publication Series: Miscellaneous Publication
PDF: View PDF (1.26 MB)
DescriptionFIA annual inventories require rapid updating of pixel-based Phase 1 estimates. Scientists at the Southern Research Station are developing an automated methodology that uses a Normalized Difference Vegetation Index (NDVI) for identifying and eliminating problem FIA plots from the analysis. Problem plots are those that have questionable land useiland cover information. Four Landsat TM scenes in Georgia have been classified using this inethodology. A cross-validation approach was used to assess accuracy. The results are comparect with an alternative methodology: the Iterative Guided Spectral Class Rejection (ICSCR) methodology.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationCooke, William H.; Jacobs, Dennis M. 2002. Rapid classification of landsat TM imagery for phase 1 stratification using the automated NDVI threshold supervised classification (ANTSC) methodology. In: Proceedings of the Fourh Annual Forest Inventory and Analysis Symposium, 81-86
- Rapid Classification of Landsat TM Imagery for Phase 1 Stratification Using the Automated NDVI Threshold Supervised Classification (ANTSC) Methodology
- Wall-to-wall Landsat TM classifications for Georgia in support of SAFIS using FIA plots for training and verification
- Landscape scale mapping of forest inventory data by nearest neighbor classification
XML: View XML