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An estimation strategy to protect against over-estimating precision in a LiDAR-based prediction of a stand mean

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  • Steen MAGNUSSEN

    (Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, Canada)

Abstract

A prediction of a forest stand mean may be biased and its estimated variance seriously underestimated when a model fitted for an ensemble of stands (stratum) does not hold for a specific stand. When the sampling design cannot support a stand-level lack-of-fit analysis, an analyst may opt to seek a protection against a possibly serious over-estimation of precision in a predicted stand mean. This study propose an estimation strategy to counter this risk by an inflation of the standard model-based estimator of variance when model predictions suggest non-trivial random stand effects, a spatial distance-dependent autocorrelation in model predictions, or both. In a simulation study, the strategy performed well when it was most needed, but equally over-inflated variance in settings where less protection was appropriate.

Suggested Citation

  • Steen MAGNUSSEN, 2018. "An estimation strategy to protect against over-estimating precision in a LiDAR-based prediction of a stand mean," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 64(12), pages 497-505.
    • Handle: RePEc:caa:jnljfs:v:64:y:2018:i:12:id:120-2018-jfs
    DOI: 10.17221/120/2018-JFS
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    References listed on IDEAS

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    1. Andrew O. Finley & Sudipto Banerjee & Patrik Waldmann & Tore Ericsson, 2009. "Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets," Biometrics, The International Biometric Society, vol. 65(2), pages 441-451, June.
    2. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
    3. Agnan Kessy & Alex Lewin & Korbinian Strimmer, 2018. "Optimal Whitening and Decorrelation," The American Statistician, Taylor & Francis Journals, vol. 72(4), pages 309-314, October.
    4. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    5. F. Mauro & I. Molina & A. García‐Abril & R. Valbuena & E. Ayuga‐Téllez, 2016. "Remote sensing estimates and measures of uncertainty for forest variables at different aggregation levels," Environmetrics, John Wiley & Sons, Ltd., vol. 27(4), pages 225-238, June.
    6. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258.
    7. Viana, H. & Aranha, J. & Lopes, D. & Cohen, Warren B., 2012. "Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models," Ecological Modelling, Elsevier, vol. 226(C), pages 22-35.
    8. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
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    Cited by:

    1. Steen Magnussen & Johannes Breidenbach, 2020. "Retrieval of among-stand variances from one observation per stand," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 66(4), pages 133-149.

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