Commonly, forested lands are divided into polygons based on forest type. Information for each polygon often includes variables that are measured on aerial photographs (e.g., species composition, height class), and additional variables derived from the aerial attributes using yield or other models (e.g., estimated volume per ha). For detailed information, such as the amount of coarse woody debris, stand structure, or tree-lists (stems per ha by species and diameter), ground sampling of every polygon is usually not possible. However, this information would be useful to represent the current inventory, and as model inputs to project future conditions. In stands that are sampled, detail at lower scales is often also of interest, but this may be available only for some of the sampled stands because of high measurement costs. Also, for recently cut stands, particularly, partially cut stands, estimates of future regeneration are needed as inputs to growth models. For estimating variables of interest, imputation approaches are used as alternative to regression approaches. Imputation involves substituting, plausible measurements from one or more selected units with similar characteristics to units lacking these measures (Rubin 1987, Ek et al. 1997, McRoberts 2001). Data with all variables measured are termed “reference data”, whereas data with some variables missing are termed “target data”. If only one selected unit is used in the substitution, the variability of the missing variables as represented in the reference data will be preserved in the estimates imputed to the target data (Moeur an
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