Influence of Prediction Cell Size on LiDAR-Derived Area-Based Estimates of Total Volume in Mixed-Species and Multicohort Forests in Northeastern North America

LiDAR has become a common means for providing area-based estimates of forest attributes. An important, yet understudied, factor influencing forest attribute estimation is the prediction cell size. In this analysis, the influence of 6 prediction cell sizes (2-, 4-, 10-, 25-, 50-, and 100-m squares) and 2 prediction methods (parametric vs. nonparametric) on LiDAR-derived, area-based, stand- and forest-level estimates of total volume at 2 separate locations in the Acadian Forest were evaluated and compared to field-based measurements. Statistically significant differences (p < 0.05) were observed between the field-based and some of the LiDAR-derived volumes. The stand-level percent differences between the various cell sizes ranged from < 0.1 to >38%, and were generally greatest at the 2 extreme cell sizes examined (2 m and 100 m). At the forest-level, all LiDAR-estimates were within the 95% confidence interval for the ground-based estimate, except for a few values from smaller cell sizes. There was also a general trend of smaller cell sizes producing lower values of volume, whereas larger cell sizes tended to give higher estimates. Overall, this study highlights the importance cell size influence on stand- and forest-level estimates and indicates that additional work is needed to better understand the trends identified in this analysis.