A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data

A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data. Chirici, G., Mura, M., McInerney, D., Py, N., Tomppo, E. O., Waser, L. T., Travaglini, D., & McRoberts, R. E. Remote Sensing of Environment, 176:282-294, 2016.

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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.

@article{RN773,
   author = {Chirici, Gherardo and Mura, Matteo and McInerney, Daniel and Py, Nicolas and Tomppo, Erkki O. and Waser, Lars T. and Travaglini, Davide and McRoberts, Ronald E.},
   title = {A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data},
   journal = {Remote Sensing of Environment},
   volume = {176},
   pages = {282-294},
   abstract = {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.},
   keywords = {k-Nearest Neighbors
Forestry applications
Review
Meta-analysis},
   ISSN = {0034-4257},
   DOI = {10.1016/j.rse.2016.02.001},
   url = {http://www.sciencedirect.com/science/article/pii/S0034425716300293},
   year = {2016},
   type = {Journal Article}
}

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