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

- This is a review of the scientific literature for forestry applications of the k-NN.
- This is a meta-analysis of 260 experimental tests described in 148 scientific papers.
- These are the experiences from 26 countries since 1981.
- k-NN results were useful and well-affirmed in all vegetation zones and at all scales.
- Values of k of 3-10 produce an RMSE level usually less than 30%.

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.