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    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.

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    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.

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    Keywords

    k-Nearest Neighbors, Forestry applications, Review, Meta-analysis

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