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    Description

    When a temporal trend in forest conditions is present, standard estimates from paneled forest inventories can be biased. Thus methods that use more recent remote sensing data to improve estimates are desired. Paneled inventory data from national forests in Oregon and Washington, USA, were used to explore three nearest neighbor imputation methods to estimate mean annual change of four forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha). The random Forest imputation method outperformed the other imputation approaches in terms of root mean square error. The imputed mean annual change was used to project all panels to a common point in time by multiplying the mean annual change with the length of the growth period between measurements and adding the change estimate to the previously observed measurements of the four forest attributes. The resulting estimates of the mean of the forest attributes at the current point in time outperformed the estimates obtained from the national standard estimator.

    Publication Notes

    • Visit PNW's Publication Request Page to request a hard copy of this publication.
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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Eskelson, Bianca N. I.; Barrett, Tara M.; Temesgen, Hailemariam. 2009. Imputing mean annual change to estimate current forest attributes. Silva Fennica. 43(4): 649-658.

    Keywords

    forest inventory and analysis, forest monitoring, national forest inventories, nearest neighbor imputation, Pacific Northwest, paneled inventory data

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https://www.fs.usda.gov/treesearch/pubs/37732