LiDAR technology has a great advantage in forest resources investigation,but the data collected by a single platform often has scanning blind spots,which makes it difficult to obtain complete forest structure information.For this reason,we take the Pinus massoniana forests as the research object and explore the suitability algorithm for individual tree segmentation of Pinus massoniana forests based on fusion point cloud data.We first proposed a method for fusing forest-sample point cloud data.Then,we adopted the marker-controlled watershed algorithm,distance-based clustering algorithm,and the layer stacking algorithm for the Pinus massoniana forests for individual tree segmentation.Finally,tree heights were extracted to verify the applicability of the fusion point cloud for estimating forest structural parameters.The experimental results are as follows:1)The proposed fusion point cloud method can effectively fuse airborne lidar point cloud and hand-held lidar point cloud with a registration error of 0.054 m.2)Among the three tree segmentation algorithms,the marker-controlled watershed algorithm has the highest segmentation accuracy,with an overall accuracy of 0.88,which is higher than the distance-based clustering algorithm and the layer stacking algorithm.3)The extracted tree height of individual tree segmented using the marker-controlled watershed algorithm has an R² of 0.983 7 and an RMSE of 0.759 6 m based on the fusion point cloud data,which is a significant improvement in accuracy compared to single point cloud data.The results of the study can provide technical support for the application of multi-source LiDAR in forestry field and the management of forest resources in Pinus massoniana forests.