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Forest inventories were originally designed for the assessment of timber stocks over large areas. The large datasets gathered by these programs are becoming of increasing interest in other applications, particularly in ecosystem modeling. With inventory designs based on sampling proportional to size (angle-count plots) users should be cautious of using data pertaining to individual plots, as the plot-wise data is a statistical estimate rather than a true measurement. Estimates of per-plot basal area are mathematically unbiased, but the individual precision is extremely poor. Resampling of inventory datasets using multiple basal area factors can improve the precision of the estimates on single plots, thus providing better data for potential end users. Following two simulation studies to demonstrate our method we apply it to the sampling points of the Austrian National Forest Inventory, and show how the improved estimates of basal area give rise to more realistic estimates of basal area increment on individual points, reducing variance through the smoothing of extreme estimates. Our method will be useful in studies where angle count inventory data pertaining to individual plots is used to assess the precision of models or remote sensing methods.

Improved estimates of per-plot basal area from angle count inventories