The challenge of estimating a residual spatial autocorrelation from forest inventory data

Estimates of stand averages are needed by forest management for planning purposes. In forest enterprise inventories supported by remotely sensed auxiliary data, these estimates are typically derived exclusively from a model that does not consider stand effects in the study variable. Variance estimators for these means may seriously underestimate uncertainty, and confidence intervals may be too narrow when a model used for computing a stand mean omits a non-trivial stand-effect in one or more of the model-parameters, a non-trivial spatial distance dependent autocorrelation in the model residuals, or both. In simulated sampling from 36 populations with stands of different sizes, and differing with respect to: i) the correlation between a study variable (Y) and two auxiliary variables (X); ii) the magnitude of stand effects in the intercept of a linear population model linking X to Y; and iii) a first-order autoregression in Y and X â we learned that none of the tested designs provided reliable estimates of the within-stand autocorrelation among model residuals. More reliable estimates were possible from stand-wide predictions of Y. The anticipated bias in an estimated autoregression parameter had a modest influence on estimates of variance and coverage of nominal 95% confidence intervals for a synthetic stand mean.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author