Enhancing the irish NFI using k-nearest neighbors and a genetic algorithm

Daniel McInerney*, Frank Barrett, Ronald E. McRoberts, Erkki Tomppo

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1482-1494
Number of pages13
JournalCanadian Journal of Forest Research
Volume48
Issue number12
DOIs
Publication statusPublished - 1 Jan 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • Forest inventory
  • Genetic algorithm
  • K-NN
  • Nearest neighbors
  • Remote sensing

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