Light Detection and Ranging (LiDAR) is increasingly common in forestry applications, yet relatively little research has evaluated its use in quantifying carbon stocks in afforested bottomland hardwood forests. This study relates forest structural field measurements to metrics derived from low pulse density LiDAR data to assess the use of LiDAR in characterization of planted bottomland hardwood oak stands. Univariate and multivariate linear regressions were performed with field and LiDAR variables to determine relationships. The height-related field dependent variables average height, maximum height, and individual tree volume had the highest adjusted R-squared values of 0.5-0.6 (P<0.0001) for the univariate models and adjusted R-squared values of 0.70-0.79 for the multivariate models. These findings suggest that low-density LiDAR is capable of assessing forest structure and suggests that further research evaluating LiDAR quantification of bottomland hardwood carbon stocks is warranted.