Multivariate estimation for accurate and logically-consistent forest-attributes maps at macroscales

Spatially explicit wall-to-wall forest-attributes information is critically important for designing management strategies resilient to climate-induced uncertainties. Multivariate estimation methods that link forest attributes and auxiliary variables at full-information locations can be used to estimate the forest attributes for locations with auxiliary-variables information only. However, trade-offs between estimation accuracies versus logical consistency among estimated attributes may occur. This is particularly likely for macroscales (i.e., ≥ 1 Mha) with large forest-attributes variances and wide spacing between full-information locations. We examined these trade-offs for ~390 Mha of Canada’s boreal zone using variable-space nearest neighbours imputation versus two modelling methods (i.e., a system of simultaneous nonlinear models and kriging with external drift). We found logical consistency among estimated forest attributes (i.e., crown closure, average height and age, volume per ha, species percentag...