Applying Ratio-of-Means Estimation for Annualized Components of Volume Change in Forest Resource Monitoring

Abstract

Forest inventory estimates of annualized net growth, removals, and mortality provide a standardized metric for a wide range of management and policy assessments. Commonly, plot-level annualized values are determined by dividing the periodic change by the length of the time interval. Subsequent estimation of means constitutes a mean-of-ratios (MOR) estimation approach. However, due to potential bias concerns for the MOR estimator, the ratio-of-means (ROM) estimator is generally preferred by forestry practitioners. National forest inventory data from six states in the United States were used to compare MOR and ROM annualized change estimation. Generally, MOR and ROM performed similarly when there was little variation among plot measurement intervals. Differences between MOR and ROM increased as variability among measurement intervals increased, with the largest observed differences being in the 3%–4% range. The ROM estimator also resulted in more precise estimates than MOR, although in many cases the differences were trivial. ROM estimation can be negatively affected if the mean of the measurement intervals assigned to unvisited nonforest plots is incongruent with the mean for forested field–visited plots. Nonetheless, if this complication is not present or can be ameliorated, the ROM estimator appears to perform better than MOR across various populations. Study Implications: Forest inventory volume change results are usually reported on a per-year basis to make them more interpretable by data users. This study compared the use of the typical mean-of-ratios (MOR) approach with an alternative ratio-of-means (ROM) concept. In a simulation study that examined six different populations of forest inventory plots, the ROM method generally had smaller bias and uncertainty statistics than the MOR approach. Thus, the ROM estimation offers forest inventory practitioners a more robust method for calculating annualized change statistics. The use of accurate estimations to inform management and policy decisions is critical to effective stewardship of forest resources.