Precise assessment of forest inventory can help optimize timber value and industrial forest-management planning. Light detection and ranging (LiDAR) technology has been recently introduced in forestry to effectively and precisely estimate and monitor forest inventories. In this study, we investigated the opportunities and limitations of integrated mobile laser scanning (MLS) and airborne laser scanning (ALS) LiDAR applications for estimating individual tree diameter at breast height (DBH) and tree height. Three circular fitting algorithms (integrated RANSAC and circle fitting, minimum enclosing circle, and least-squares ellipse fitting) were used to estimate DBH. Height was calculated using the crown height model (CHM). DBH was most accurately estimated by the ellipse-fitting algorithm. Integrated MLS and ALS performed better than previous circular fitting applications in estimating DBH values. Lastly, the tree height estimated using the CHM was compared with the ground truth, and R2 and the root mean square error (RMSE) of the height were 0.60 and 2.0 m, respectively. In addition, the trend of low accuracy for suppressed trees was identified from point cloud data (PCD) extraction because of overlapped PCD among the surrounding dominant trees. This result indicated that the segmentation of the top of suppressed trees was limited by overlap issues in high-density forests.