K-Nearest Neighbor Estimation of Forest Attributes: Improving Mapping Efficiency

Abstract

This 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.

Parent Publication

• Proceedings of the fifth annual forest inventory and analysis symposium

Citation

Finley, 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.