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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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    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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