## 3 Result and Discussion Section  
To achieve carbon neutrality in the face of global climate change, forestry carbon sinks are the most economically feasible and promising approach at this stage. Accurate estimation of forest carbon storage in forestry carbon sink measurement is the basis of the entire carbon sink trade, and it is of great importance to study the role of forests in regional and global carbon cycles [27]. At present, the main methods for estimating forest carbon storage include sample plot inventory, the micrometeorological method, the remote sensing estimation method, etc. In this paper, carbon storage was estimated based on the sample plot inventory method by extracting the tree parameters of the plantation point clouds. As the name implies, the sample plot inventory method is to select typical sample plots in the inventory area, conduct continuous biomass observations on the sample plots, and apply different methods (such as all harvesting methods, volume-derived biomass method, dimensional analysis method, etc.) to derive carbon storages. The volume-derived biomass method calculates the biomass of a single tree by the allometric growth equation of tree species biomass that uses parameters such as tree height and diameter at breast height (DBH). The product of the calculated biomass and carbon content is the carbon storage of a single tree, and the carbon storage of a regional plantation is obtained by superimposing the carbon storage of every single tree. For tree 3D point cloud processing, tree parameters were extracted based on Lidar360 point cloud processing software. The forestry module based on the single tree segmentation algorithm [28] was used to obtain the tree height (referring to the distance from the lowest point of the rhizome at the bottom to the top of the tree), diameter at breast height (DBH is one of the most basic factors in the determination of standing trees, and was calculated by the diameter of the main branch at 1.3 m above breast height, and when this section was a deformed section, the minimum and maximum values were averaged [29]), crown diameter, volume (referring to the diameter of the main trunk of the tree and auxiliary branches above the main trunk), etc. The point cloud processing software, based on Lidar360, firstly denoised the point cloud model after fusion. The denoised point clouds then classified the ground points, and changed the sparsity of the extracted ground key points by setting the upper and lower boundary thresholds, aiming to retain the sparse point set of ground key points and improve the speed of building the digital elevation model (DEM). After classification, a DEM was generated and normalized. Later, point cloud filtering was performed on the ground points with the final result shown in Figure 15. After ground classification, single  
Figure 14: Data fusion diagram. ((**a**) shows path fusion; (**b**) shows point cloud fusion). tree segmentation was performed based on the canopy height model (CHM) segmentation algorithm. The segmentation results are shown in Figure 16.  
After the single tree segmentation was completed, the point clouds of each tree were exported. Lidar360 point cloud processing software automatically extracted the center coordinates, tree height, canopy diameter, canopy area and canopy volume for each tree that was granted a unique ID number. The principle is to extract the point clouds near the 1.3 m height of each tree trunk at breast height, and use the least-squares method to fit a circle shape with the center of the single tree as its center and the diameter of the tree at breast height as its diameter. The point clouds of the single tree canopy were obtained by setting the threshold. And the obtained point clouds were used to construct the canopy model, where we obtained the diameter, projected area, and canopy volume of the single tree canopy. The extraction results of the experimental area are shown in Table 1.  
Figure 15: Classification and normalization of ground points.