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Inferences for forest-related spatial problems can be enhanced using remote sensing-based maps constructed with nearest neighbours techniques. The non-parametric k-nearest neighbours (k-NN) technique calculates predictions as linear combinations of observations for sample units that are nearest in a space of auxiliary variables to population units for which predictions are desired. Implementations of k-NN require four choices: a distance or similarity metric, the specific auxiliary variables to be used with the metric, the number of nearest neighbours, and a scheme for weighting the nearest neighbours. The study objective was to compare optimized k-NN configurations with respect to confidence intervals for airborne laser scanning-assisted estimates of mean volume or biomass per unit area for study areas in Norway, Italy, and the USA. Novel features of the study include a new neighbour weighting scheme, a statistically rigorous method for selecting feature variables, simultaneous optimization with respect to all four k-NN implementation choices and comparisons based on confidence intervals for population means. The primary conclusions were that optimization greatly increased the precision of estimates and that the results of optimization were similar for the k-NN configurations considered. Together, these two conclusions suggest that optimization itself is more important than the particular k-NN configuration that is optimized.

Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass