Abstract: Light Detection and Ranging (LiDAR) is one rapidly emerging type of active remote sensing at present. The laser pulse can partially penetrate the shelter of the forest canopy, further realizing the acquisition of three-dimensional structure characteristics for the whole forest. In this study, a systematic evaluation was made on the individual tree parameters of plantation forestry in Hainan Boao of China using the point cloud data. The pre-processing operation was carried out to implement the normalized point cloud data for the extraction of parameters. First, the outlier was used to remove the noise in the point cloud. The ground points were also separated by the Triangulated Irregular Network (TIN). Then, the Digital Elevation Model (DEM) and Digital Surface Model (DSM) were generated by the Kriging and TIN interpolation. First of all, different operations were selected to generate the Canopy Height Model (CHM). The elevation normalization was then performed on the point cloud data for subsequent segmentation and parameter extraction of the individual tree. K-means clustering was used to segment the images of the trees using different tree species, according to the actual topography and forest structure characteristics in the study area. The layer-by-layer clustering was used to extract the point cloud of the individual tree, the position of which was then compared with the measurement. The correct recognition rate and the recall rate of each sample plot were also calculated to analyze the position error of individual tree segmentation. Then, the local maximum method of the variable window was used to detect the vertex position of individual tree, where the pixel value of the tree vertex was taken as the estimated height of the individual tree. The average value of the individual tree canopy was calculated, according to the difference between the maximum and minimum of point cloud data for the individual tree in the east-west and north-south directions. Individual Diameter at Breast Height (DBH), volume, and aboveground biomass were calculated, according to the tree Height-DBH model, volume table, and aboveground biomass model, respectively. The results showed that the correct recognition rate of two tree species was above 85%, and the overall average correct recognition rate was above 89.98%. The decision coefficient reached 0.8 for the individual tree height, crown width, DBH, volume, and aboveground biomass. The root mean square error of individual tree height and crown width was less than 1m. Specifically, the error of individual tree DBH was less than 2 cm, while the DBH error of rubber tree was much larger than that of areca tree. A larger DBH error was attributed that there were significant differences in the tree height among different tree species when estimating DBH value using the tree Height-DBH model. The error of individual tree volume were 0.01 and 0.05 m3 respectively. Meanwhile, the error of aboveground biomass greatly varied in the two species, particularly relating to the forest layer structure and terrain factors under the forest. Consequently, the point cloud data can be expected to improve the accuracy of forest parameters estimation, while the laser equipment can have great application potential in forest resource inventory.