  Systematic sampling is often used to select plot locations for forest
inventory estimation. However, it is not possible to derive a design-unbiased
variance estimator for a systematic sample using one random start. As a result,
many forest inventory analysts resort to applying variance estimators that are
design-unbiased following simple random sampling to their systematic samples
even though this typically leads to conservative estimates of error. We explore
the influence of spatial autocorrelation on variance estimation when systematic
sampling is employed using repeated sampling. We generate a sequence of 1000
synthetic populations with increasing spatial autocorrelation and repeatedly
sample from each to examine how the performance of estimators change as spatial
autocorrelation changes. We also repeatedly sample from a tree census plot in
Harvard Forest, Massachusetts and examine the performance of similar
estimators. Results indicate that applying variance estimators that are
unbiased following simple random sampling to systematic samples from
populations exhibiting stronger spatial autocorrelation tend to be more
conservative. We also find that incorporating ancillary wall-to-wall
covariates, e.g., remote sensing data, using generalized regression estimators
can reduce variance over-estimation by explaining some or all of the spatial
structure in the population.
