Canadian Forest Service Publications
An estimation strategy to protect against over-estimating precision in a LiDAR-based prediction of a stand mean. 2018. Magnussen, S. Journal of Forest Science, 64, (12): 497–505.
Available from: Pacific Forestry Centre
Catalog ID: 39536
CFS Availability: PDF (request by e-mail)
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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.
Plain Language Summary
To provide an estimation strategy that protects against over-estimating the precision of a prediction of a forest stand mean derived from LiDAR metrics and a model that does not include stand effects and spatial autocorrelation in model residuals. The strategy is developed in response to simulations that clearly demonstrates the strong impact of spatial confounding on estimates of uncertainty. The proposed strategy provides an effective protection against over-estimating precision, but it may also provide conservative estimates of variance when protection is less needed. The intended impact is to encourage practitioners and analysts involved with enhanced forest inventories to assess the risk of serious over-estimation of precision and take remedial actions (either by adopting the proposed strategy) or develop/adopt other measures as they become available. Over-estimating precision in a prediction of a stand mean can lead to poorer forest management stewardship.
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