Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    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)

    Description

    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.

    Publication Notes

    • Check the Northern Research Station web site to request a printed copy of this publication.
    • Our on-line publications are scanned and captured using Adobe Acrobat.
    • During the capture process some typographical errors may occur.
    • Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    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. https://doi.org/10.1139/cjfr-2018-0011.

    Cited

    Google Scholar

    Keywords

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

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/58868