In this study, we develop a system for efficiently measuring detailed information of trees in a forest environment using a small unmanned aerial vehicle (UAV) equipped with light detection and ranging (lidar). The main purpose of forest measurement is to predict the volume of wood for harvesting and delineating forest boundaries by tree location. Herein, we propose a method for extracting the position, number of trees, and vertical height of trees from a set of three-dimensional (3D) point clouds acquired by a UAV lidar system. The point cloud obtained from a UAV is dense in the tree’s crown, and the trunk 3D points are sparse because the crown of the tree obstructs the laser beam. Therefore, it is difficult to extract single-tree information from 3D point clouds because the characteristics of 3D point clouds differ significantly from those of conventional 3D point clouds using ground-based laser scanners. In this study, we segment the forest point cloud into three regions with different densities of point clouds, i.e., canopy, trunk, and ground, and process each region individually to extract the target information. By comparing a ground laser survey and the proposed method in an actual forest environment, it is discovered that the number of trees in an area measuring 100 m × 100 m is 94.6% of the total number of trees. The root mean square error of the tree position is 0.3 m, whereas that of the vertical height is 2.3 m, indicating that single-tree information can be measured with sufficient accuracy for forest management.

1. [1] L. Tang and G. Shao, “Drone remote sensing for forestry research and practices,” J. of Forestry Research, Vol.26, No.4, pp. 791-797, 2015.
2. [2] J. Kilian, N. Haala, M. Englich et al., “Capture and evaluation of airborne laser scanner data,” Int. Archives of Photogrammetry and Remote Sensing, Vol.31, pp. 383-388, 1996.
3. [3] S. Ganz, Y. Käber, and P. Adler, “Measuring tree height with remote sensing – A comparison of photogrammetric and LiDAR data with different field measurements,” Forests, Vol.10, No.8, 694, 2019.
4. [4] W. Xu, Z. Su, Z. Feng, H. Xu, Y. Jiao, and F. Yan, “Comparison of conventional measurement and LiDAR-based measurement for crown structures,” Computers and Electronics in Agriculture, Vol.98, pp. 242-251, 2013.
5. [5] A. Persson, J. Holmgren, and U. Soderman, “Detecting and measuring individual trees using an airborne laser scanner,” Photogrammetric Engineering and Remote Sensing, Vol.68, No.9, pp. 925-932, 2002.
6. [6] S. C. Popescu, R. H. Wynne, and R. F. Nelson, “Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass,” Canadian J. of Remote Sensing, Vol.29, No.5, pp. 564-577, 2003.
7. [7] W. Yao, P. Krzystek, and M. Heurich, “Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data,” Remote Sensing of Environment, Vol.123, pp. 368-380, 2012.
8. [8] C. Zhang, Y. Zhou, and F. Qiu, “Individual tree segmentation from LiDAR point clouds for urban forest inventory,” Remote Sensing, Vol.7, No.6, pp. 7892-7913, 2015.
9. [9] T. Takahashi, K. Yamamoto, Y. Senda, and M. Tsuzuku, “Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR,” J. of Forest Research, Vol.10, No.2, pp. 135-142, 2005.
10. [10] T. Takahashi, K. Yamamoto, Y. Senda, and M. Tsuzuku, “Predicting individual stem volumes of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR,” J. of Forest Research, Vol.10, No.4, pp. 305-312, 2005.
11. [11] S. Kameyama and K. Sugiura, “Estimating Tree Height and Volume Using Unmanned Aerial Vehicle Photography and SfM Technology, with Verification of Result Accuracy,” Drones, Vol.4, No.2, 19, 2020.
12. [12] S. Krause, T. G. Sanders, J.-P. Mund, and K. Greve, “UAV-based photogrammetric tree height measurement for intensive forest monitoring,” Remote Sensing, Vol.11, No.7, 758, 2019.
13. [13] D. Panagiotidis, A. Abdollahnejad, P. Surovỳ, and V. Chiteculo, “Determining tree height and crown diameter from high-resolution UAV imagery,” Int. J. of Remote Sensing, Vol.38, No.8-10, pp. 2392-2410, 2017.
14. [14] K. Liu, X. Shen, L. Cao, G. Wang, and F. Cao, “Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.146, pp. 465-482, 2018.
15. [15] O. Mian, J. Lutes, G. Lipa, J. Hutton, E. Gavelle, and S. Borghini, “Direct georeferencing on small unmanned aerial platforms for improved reliability and accuracy of mapping without the need for ground control points,” The Int. Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.40, No.1, pp. 397-402, 2015.
16. [16] D. Yin and L. Wang, “Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges,” Remote Sensing of Environment, Vol.223, pp. 34-49, 2019.
17. [17] A. P. Dalla Corte, F. E. Rex, D. R. A. de Almeida, C. R. Sanquetta, C. A. Silva, M. M. Moura, B. Wilkinson, A. M. A. Zambrano, E. M. da Cunha Neto, H. F. Veras et al., “Measuring individual tree diameter and height using GatorEye High-Density UAV-Lidar in an integrated crop-livestock-forest system,” Remote Sensing, Vol.12, No.5, 863, 2020.
18. [18] A. Yamaba, T. Sano, Y. Watanabe, and S. Futatsuya, “Extraction of forested terrain and tree height by using a 3D laser scanner mounted on a drone in comparison to TLS,” J. of the Japan Forest Engineering Society, Vol.33, No.3, pp. 169-174, 2018.
19. [19] T. Suzuki, T. Akehi, T. Massubuchi, and Y. Amano, “Attitude Determination using Single Frequency GNSS Receivers for Small UAVs,” Proc. of the Int. Conf. on Advanced Mechatronics (ICAM) 2015, p. 112, 2015.
20. [20] T. Suzuki, D. Inoue, and Y. Amano, “Robust UAV Position and Attitude Estimation using Multiple GNSS Receivers for Laser-based 3D Mapping,” Proc. of the 2019 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 4402-4408, 2019.
21. [21] K. Zhang, S.-C. Chen, D. Whitman, M.-L. Shyu, J. Yan, and C. Zhang, “A progressive morphological filter for removing nonground measurements from airborne LIDAR data,” IEEE Trans. on Geoscience and Remote Sensing, Vol.41, No.4, pp. 872-882, April 2003.
22. [22] L. Piegl and W. Tiller, “The NURBS book,” Springer Science & Business Media, 1996.
23. [23] W. Wang, H. Pottmann, and Y. Liu, “Fitting B-spline curves to point clouds by curvature-based squared distance minimization,” ACM Trans. on Graphics (ToG), Vol.25, No.2, pp. 214-238, 2006.
24. [24] A. Richtsfeld, T. Mörwald, J. Prankl, M. Zillich, and M. Vincze, “Learning of perceptual grouping for object segmentation on RGB-D data,” J. of Visual Communication and Image Representation, Vol.25, No.1, pp. 64-73, 2014.
25. [25] R. B. Rusu and S. Cousins, “3d is here: Point cloud library (pcl),” Proc. of the 2011 IEEE Int. Conf. on Robotics and Automation, pp. 1-4, 2011.
26. [26] M. Dassot, A. Colin, P. Santenoise, M. Fournier, and T. Constant, “Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment,” Computers and Electronics in Agriculture, Vol.89, pp. 86-93, 2012.
27. [27] R. B. Rusu, “Semantic 3d object maps for everyday manipulation in human living environments,” KI-Künstliche Intelligenz, Vol.24, No.4, pp. 345-348, 2010.
28. [28] M. Kolahdouzan and C. Shahabi, “Voronoi-based k nearest neighbor search for spatial network databases,” Proc. of the 30th Int. Conf. on Very large data bases, Vol.30, pp. 840-851, 2004.