Skip to Main Content
-
A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data
Author(s): Gherardo Chirici; Matteo Mura; Daniel McInerney; Nicolas Py; Erkki O. Tomppo; Lars T. Waser; Davide Travaglini; Ronald E. McRoberts
Date: 2016
Source: Remote Sensing of Environment
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
PDF: View PDF (1.0 MB)Description
The k-Nearest Neighbors (k-NN) technique is a popular method for producing spatially contiguous predictions of forest attributes by combining field and remotely sensed data. In the framework of Working Group 2 of COST Action FP1001, we reviewed the scientific literature for forestry applications of k-NN. Information available in scientific publications on this topic was used to populate a database that was then used as the basis for a meta-analysis. We extracted qualitative and quantitative information from 260 experimental tests described in 148 scientific papers. The papers represented a geographic range of 26 countries and a temporal range from 1981 to 2013. Firstly, we describe the literature search and the information extracted and analyzed. Secondly, we report the results of the meta-analysis, especially with respect to estimation accuracies reported for k-NN applications for different configurations, different forest environments, and different input information. We also provide a summary of results that may reasonably be expected for those planning a k-NN application using remotely sensed data from different sensors and for different forest attributes. Finally, we identify some methodological publications that have advanced the state of the science with respect to k-NN.Publication Notes
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
Citation
Chirici, Gherardo; Mura, Matteo; McInerney, Daniel; Py, Nicolas; Tomppo, Erkki O.; Waser, Lars T.; Travaglini, Davide; McRoberts, Ronald E. 2016. A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data. Remote Sensing of Environment. 176: 282-294. https://doi.org/10.1016/j.rse.2016.02.001.Cited
Keywords
k-Nearest Neighbors, Forestry applications, Review, Meta-analysisRelated Search
- Multivariate inference for forest inventories using auxiliary airborne laser scanning data
- Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery
- Using genetic algorithms to optimize k-Nearest Neighbors configurations for use with airborne laser scanning data
XML: View XML
Show More
Show Fewer
https://www.fs.usda.gov/treesearch/pubs/56411