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

$29.50 plus tax (Refund Policy)

Buy Article:

Abstract:

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

More about this publication?
  • 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.
  • Membership Information
  • ingentaconnect is not responsible for the content or availability of external websites
Related content

Tools

Favourites

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more