Updating Canada’s National Forest Inventory with multiple imputations of missing contemporary data

Abstract

Canada’s National Forest Inventory (NFI) is facing an issue of spatial imbalance in photo interpreted data from 400 ha photo-plots available for estimation of state and change. Multiple imputations (MI) of missing data is therefore considered as a means to mitigate a potential bias arising from spatial imbalance, and—to a lesser degree— improve the precision relative to what can be achieved with the subset of plots having current data. In this study we explored MI with data from three study sites located in the provinces of Quebec, Ontario, and Saskatchewan. Specifically, we looked at state at time T2 and change between T1 and T2 in cover-type area proportions and in per unit area stem volume. At each location we found significant T1 differences in these attributes between plots with and without T2 data. A MI procedure with 20 replications of stochastic model-based imputations of missing data was therefore effective as a way to mitigate a bias that would arise if T2 inference was based exclusively on plot...