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    This report presents the full simulation results of the work described in Roesch (2014), in which multiple levels of simulation were used to test the robustness of estimators for the components of forest change. In that study, a variety of spatial-temporal populations were created based on, but more variable than, an actual forest monitoring dataset, and then those populations were sampled under four sets of sampling error structure. An estimator modification was shown, to be used when extraneously obtained information indicated that a deviation to the assumed population model existed. The extraneous information was also incorporated into a mixed estimator. The first three approaches, without the incorporation of extraneous information, are compatible with large monitoring efforts that require intervention-free results. The mixed estimation approach accounts for model assumptions that sometimes remain latent in other approaches and is amenable to the formal incorporation of the extraneously obtained information. All four approaches were shown to work well when the sampling error structure was unbiased, while some notable differences in performance were observed at the temporal extremities of observation, in the presence of temporal anomalies, and in the presence of biased sampling error structures. Only those results necessary to make the salient points were presented in Roesch (2014). Full results are presented here both for full disclosure and for the reader interested in a more detailed understanding of the effects of realistic sampling errors on temporal estimates.

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    Roesch, Francis A. 2014. Toward robust estimation of the components of forest population change: simulation results. e-Gen. Tech. Rep. SRS-GTR-194. Asheville, NC: USDA-Forest Service, Southern Research Station. 79 p.


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    Annual inventories, components of change, forest monitoring, sampling error, spatial-temporal sample design

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