A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data
Chirici, Gherardo; Mura, Matteo; McInerney, Daniel; Py, Nicolas; Tomppo, Erkki O.; Waser, Lars T.; Travaglini, Davide; McRoberts, Ronald E. (2016)
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Chirici, Gherardo
Mura, Matteo
McInerney, Daniel
Py, Nicolas
Tomppo, Erkki O.
Waser, Lars T.
Travaglini, Davide
McRoberts, Ronald E.
Julkaisusarja
Remote sensing of environment
Volyymi
176
Sivut
282-294
Elsevier Science Inc.
2016
All rights reserved
Copyright: Elsevier Inc.
Copyright: Elsevier Inc.
Tiivistelmä
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.
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