## 4 Conclusions  
Accurate estimation of carbon storage of a forest is the basis of carbon sink trading. The main estimation methods of carbon stock have their advantages and disadvantages, which enable them to be applied according to the actual situation. In this paper, we construct point clouds of plantation forests and obtain tree parameters for carbon stock estimation by laser SLAM technique. Our experimental results show that:  
We tested three path plannings when using a ZEB-HORIZON handheld 3D laser scanner to obtain 3D point clouds of the plantation, and found the best path of the loop path. For a large range of plantation forests that do not allow the collection of the trees' 3D point clouds completely, we used a modified Livox Mid-40 LIDAR handheld through a straight line path to reconstruct 3D point clouds based on the LOAM-Livox algorithm. The two point clouds were fused by the ICP algorithm to construct a complete and highly accurate 3D point cloud of the plantation. And Lidar360 point cloud processing software was used for single tree segmentation and parameter extraction, which were compared with the manually measured quasi-true value to test its accuracy. The analysis results showed that the parameters obtained can meet the conditions of carbon storage estimation. Finally, the biomass was calculated according to the allometric growth equation of different tree species, and the total carbon storage was calculated according to the international common conversion rate of 0.50. The total carbon storage was 6692.66 kg, with carbon storage of 1452.06 kg and 5240.60 kg for camphor and cypress, respectively.  
The laser SLAM technology can quickly and accurately construct high-precision 3D point clouds of plantation forests. Point cloud processing software can automatically extract tree parameters, which could be further used to estimate carbon storage quickly. Since it is still difficult to classify tree species in the 3D point clouds, this method, in general, is only suitable for carbon storage estimation in single-species plantations or plantations with a uniform distribution of species. Our method has achieved a high accuracy of carbon storage estimation. This can make a reference to carbon storage estimation research in future.  
Conceptualization, H.T. and Y.X.; validation, H.T., C.L. and M.Y.; formal analysis, C.L.; resources, Y.X.; data curation, X.K.; writing--original draft preparation, H.T.; writing--review and editing, Y.X. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 41861054).  
Not applicable.  
The data used to support the findings of this study are presented in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **H/m** & **D/cm** & \(W_{T}\)**/kg** & \(W_{R}\)**/kg** & \(W\) & **CS/kg** \\ \hline
6 & 11.12 & 30.10 & 198.40 & 320.71 & 519.11 & 259.56 \\ \hline
7 & 9.83 & 53.20 & 445.89 & 1109.49 & 1555.38 & 777.69 \\ \hline
8 & 12.22 & 53.20 & 530.69 & 1351.61 & 1882\_30 & 941.15 \\ \hline \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline
29 & 10.98 & 28.30 & 178.03 & 273.89 & 451.92 & 225.96 \\ \hline
37 & 13.97 & 28.90 & 223.00 & 357.80 & 580.79 & 290.40 \\ \hline
39 & 13.08 & 12.10 & 52.77 & 42.61 & 95.38 & 47.69 \\ \hline Sum & & & 3645.84 & 6835.37 & 10481.21 & 5240.60 \\ \hline \hline \end{tabular} Notes: CS is Carbon Storage.  
\end{table}
Table 6: Calculation of cypress carbon reserves.