Airborne LiDAR for Basal Area Estimation: Accuracy Assessment and Improvement in Eastern Canada's Mixed Temperate Forests

Sustainable forest management requires current, territory-wide data, which is difficult to obtain in vast regions like Quebec, Canada. To complement ground inventories and photo-interpretation, the province developed an ALS-based model that performs well in coniferous stands, but its accuracy in hardwood stands remains untested. This study aims to evaluate the precision of the ALS-based prediction of stand basal area and then test new approaches to increase its performance. Airborne LiDAR data from 2011 to 2020 and 12 506 validation plots from sample plots were used. The ALS model precision was initially compared across the stand types, revealing lower accuracy in shade-tolerant deciduous stands. Three inputs were found to increase prediction accuracy: proportion of each species basal area in the stand, geographical coordinates, and meteorological data associated with location. Parametric and auto machine learning method (AutoML) were employed using those inputs to improve precision, with Auto ML achieving the highest improvement with initial R² of 27%, 47% and 54% and after correction R² of 31%, 56% and 67% respectively for shade-tolerant deciduous, shade-intolerant deciduous, and coniferous stand. Even with the advancements made, further improvements will be necessary to consider using an ALS-based model for shade-tolerant deciduous species.