Lidar (light detection and ranging) has been used for mapping fuel loads in Longleaf Pine (Pinus palustris Mill.) forests ecosystems. However, there are sources of bias and uncertainty associated with estimating crown-bulk density (CBD) from either Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) data. Therefore, the aim of this study was to assess the utility of ALS and TLS systems and their combination (ALS+TLS) in predicting CBD in a longleaf pine forest ecosystem in Florida. In the field, tree attributes, such as tree height (HT), crown width (CW), crown base height (CBH) and diameter at breast height (DBH) in three plots of ~ 0.19 ha were measured and CBD (kg/m³) was calculated. Individual trees were detected from ALS, TLS and ALS+TLS, and lidar-derived crown-level metrics were computed for CBD modeling. The results show that CBD can be accurately predicted from ALS, TLS and ALS+TLS. However, the ALS + TLS improved CBD prediction accuracy only slightly. Given that ALS+TLS fusion is less practical and more expensive, our comparison suggests that either ALS or TLS measurements are still reasonable for CBD prediction and their usefulness is justified.