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    Author(s): Daniel McInerney; Frank Barrett; Ronald E. McRoberts; Erkki Tomppo
    Date: 2018
    Source: Canadian Journal of Forest Research
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (6.0 MB)


    This paper presents a nationwide application of k-nearest neighbors (k-NN) to estimate growing stock volume per hectare for the Irish National Forest Estate using optical satellite imagery and field inventory data from the second National Forest Inventory (NFI). Two approaches are tested: an unweighted k-NN and an improved version (ik-NN) that is optimised using a genetic algorithm. The performance of the models is assessed in terms of the root mean square error (RMSE) and prediction error. From the simulations, it was found that the optimal value of k was 3, and the smallest pixel-level RMSE for growing stock was 126 m3·ha–1 when ik-NN was used. Comparisons with estimates from the NFI show that the ik-NN technique can enhance the Irish NFI. These improvements include a total estimate of growing stock volume of 102 million m3 with a confidence interval of ±3%, which is smaller than the NFI-reported confidence interval of ±5%. In addition, while total county-level estimates of growing volume estimated using ik-NN were consistent with those published from the NFI, their corresponding confidence intervals were much narrower, in the range of a two- to four-fold reduction in the width of the confidence interval.

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    McInerney, Daniel; Barrett, Frank; McRoberts, Ronald E.; Tomppo, Erkki. 2018. Enhancing the Irish NFI using k -nearest neighbors and a genetic algorithm . Canadian Journal of Forest Research. 48(12): 1482-1494.


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    forest inventory, remote sensing, k-NN, nearest neighbors, genetic algorithm

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