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K-Nearest Neighbor Estimation of Forest Attributes: Improving Mapping EfficiencyAuthor(s): Andrew O. Finley; Alan R. Ek; Yun Bai; Marvin E. Bauer
Source: In: Proceedings of the fifth annual forest inventory and analysis symposium; 2003 November 18-20; New Orleans, LA. Gen. Tech. Rep. WO-69. Washington, DC: U.S. Department of Agriculture Forest Service. 222p.
Publication Series: General Technical Report (GTR)
Station: Washington Office
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DescriptionThis paper describes our efforts in refining k-nearest neighbor forest attributes classification using U.S. Department of Agriculture Forest Service Forest Inventory and Analysis plot data and Landsat 7 Enhanced Thematic Mapper Plus imagery. The analysis focuses on FIA-defined forest type classification across St. Louis County in northeastern Minnesota. We outline three steps in the classification process that highlight improvements in mapping efficiency: (1) using transformed divergence for spectral feature selection, (2) applying a mathematical rule for reducing the nearest neighbor search set, and (3) using a database to reduce redundant nearest neighbor searches. Our trials suggest that when combined, these approaches can reduce mapping time by half without significant loss of accuracy.
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CitationFinley, Andrew O.; Ek, Alan R.; Bai, Yun; Bauer, Marvin E. 2005. K-Nearest Neighbor Estimation of Forest Attributes: Improving Mapping Efficiency. In: Proceedings of the fifth annual forest inventory and analysis symposium; 2003 November 18-20; New Orleans, LA. Gen. Tech. Rep. WO-69. Washington, DC: U.S. Department of Agriculture Forest Service. 222p.
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