Efficient k-nearest neighbor searches for multi-source forest attribute mapping

Abstract

In this study, we explore the utility of data structures that facilitate efficient nearest neighbor searches for application in multi-source forest attribute prediction. Our trials suggest that the kd-tree in combination with exact search algorithms can greatly reduce nearest neighbor search time. Further, given our trial data, we found that enormous gain in search time efficiency, afforded by approximate nearest neighbor search algorithms, does not result in compromised kNN prediction. We conclude that by using the kd-tree, or similar data structure, and efficient exact or approximate search algorithms, the kNN method, and variants, are useful tools for mapping large geographic areas at a fine spatial resolution.