Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data

Abstract

Light detection and ranging (LiDAR) has greatly improved the spatial resolution and accuracy of operational forest inventories. However, a cost-effective method to impute species-specific tree-level inventory is needed, to be used as input to tree or stand growth models to project single-point-in-time LiDAR estimates. We evaluated a method to match stand structural variables estimated from LiDAR to those in a library of over 5,500 sample plot measurements to impute tree lists for LiDAR grid cells across 83,000 ha of spruce (Picea sp.) plantations. Matches were determined based on planted species and minimum sum of squared difference between 6 inventory variables. Forest inventory variables obtained by the plot matches were highly correlated (r = 0.91–0.99) with those measured on 98 validation plots. Basal area distributions derived from plot matching were statistically equivalent to those observed on the validation plots 86% of the time (α = 0.05). When we aggregated the predictions for all validation plots, there was minimal difference between predicted and actual basal area distributions by planted species and species compositions were similar. Plot matching is a valid method to impute tree lists for LiDAR cells that combine the wealth of existing plot data with high resolution LiDAR-derived variables.