Applying an Efficient k-Nearest Neighbor Search to Forest Attribute Imputation

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This article explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multisource kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby decreasing the time needed to discover the NN subset. Results of five trials show gains in NN search efficiency ranging from 75 to 98% for k = 1. The search algorithm can be easily incorporated into routines that optimize feature subsets or weights, values of k, distance decomposition coefficients, and mapping.

Keywords: k-nearest neighbor; mapping; multisource; remote sensing

Document Type: Research Article

Publication date: April 1, 2006

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
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