EVALUATION OF ALTERNATIVE METHODS FOR USING LIDAR TO PREDICT ABOVEGROUND BIOMASS IN MIXED SPECIES AND STRUCTURALLY COMPLEX FORESTS IN NORTHEASTERN NORTH AMERICA.

Light detection and ranging (LiDAR) has become a common means for predicting key forest structural attributes, but comparisons of alternative statistical methods and the spatial extent of LiDAR metrics extraction on independent datasets have been minimal. The primary objective of this study was to assess the performance of local and non-local LiDAR aboveground biomass (AGB) prediction models at two locations in the Acadian Forest. Two common statistical techniques, nonlinear mixed e ects (NLME) and random forest (RF), were used to t the prediction models and compared. Finally, this study evaluated the inuence of alternative plot radii for LiDAR metrics extraction on model t and prediction accuracy. AGB models were independently developed at each forest and tested both locally (model applied to same forest used for development) and non-locally (model applied to di erent forest) using an extensive network of ground-based plots. In general, RF was found to outperform NLME when applied locally, but the di erences between the approaches were negligible when applied to the non-local dataset. NLME was found to perform equally well locally and non-locally. LiDAR extraction radius had very little inuence on model performance as well. Minimal di erences between models developed using xed- and variable-radius methods were found, while the optimal LiDAR extraction radius was not consistent among forests, statistical technique, or local vs. non-local. Overall, the results highlight the importance of a robust calibration dataset that covers the full range of observed variation for developing accurate prediction models based on remote sensing data. [ABSTRACT FROM AUTHOR]