Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data

This study proposed modifying the conceptual approach that is commonly used to model development of stand attribute estimates using airborne LiDAR data. New models were developed using an area-based approach to predict wood volume, stem volume, aboveground biomass, and basal-area across a wide range of canopy structures, sites and LiDAR characteristics. This new modeling approach does not adopt standard approaches of stepwise regression using a series of height metrics derived from airborne LiDAR. Rather, it used four metrics describing complementary 3D structural aspects of the stand canopy. The first three metrics were related to mean canopy height, height heterogeneity, and horizontal canopy distribution. A fourth metric was calculated as the coefficient of variation of the leaf area density profile. This fourth metric provided information on understory vegetation. The models that were developed with the four structural metrics provided higher estimation accuracy on stand attributes than models using height metrics alone, while also avoiding data over-fitting. Overall, the models provided prediction error levels ranging from 12.4% to 24.2%, depending upon forest type and stand attribute. The more homogeneous coniferous stand provided the highest estimation accuracy. Estimation errors were significantly reduced in mixed forest when separate models were developed for individual stand types (coniferous, mixed and deciduous stands) instead of a general model for all stand types. Model robustness was also evaluated in leaf-off and leaf-on conditions where both conditions provided similar estimation errors.