چکیده (انگلیسی):

Imputation provides a useful method for mapping forest attributes across broad geographic areas based on field plot measurements and Landsat multi-spectral data, but the resulting map products may be of limited use without corresponding analyses of uncertainties in predictions. In the case of k-nearest neighbor (kNN) imputation with k = 1, such as the Gradient Nearest Neighbor (GNN) approach, where the field plot with the most similar spectral signature is attributed to a given pixel, there has been limited guidance on methods of examining uncertainty. In this study, we use a bootstrapping method to assess the uncertainty associated with the imputation process on predictions of live tree structure (canopy cover, quadratic mean diameter, and aboveground biomass), dead tree structure (snag density and downed wood volume), and community composition (proportion hardwood) for a portion of the Cascade Mountains in Oregon, USA. We performed kNN with k = 1 imputation with 4000 bootstrap samples of the field plot data and examined three metrics of uncertainty: the width of 95% interpercentile ranges (IPR), the proportion of bootstrap samples with no tally (i.e., forest attribute was imputed as zero), and the imputation deviations (i.e., mean prediction from the bootstrap sample minus baseline GNN prediction [no bootstrapping]). Imputed values of dead tree components and species composition exhibited greater IPR, proportion no tally near 0.5, and greater magnitudes of imputation deviations compared to live tree components, indicating greater uncertainties. Our uncertainty metrics varied spatially with respect to environmental gradients and the variation was not consistent among metrics. Geographic patterns in prediction uncertainties implicated biogeography and disturbance as major factors influencing regional variation in imputation uncertainty. Spatial patterns differed not only by forest attribute, but by uncertainty metric, indicating that no single measure of uncertainty or forest structure provides a full description of imputation performance. Users of imputed map products need to consider the pattern of and the processes that contribute to uncertainty during the early stages of project development and execution.