Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method
Tóm tắt
Forest resource management and ecological assessment have been recently supported by emerging technologies. Terrestrial laser scanning (TLS) is one that can be quickly and accurately used to obtain three-dimensional forest information, and create good representations of forest vertical structure. TLS data can be exploited for highly significant tasks, particularly the segmentation and information extraction for individual trees. However, the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness, and hence do not lead to satisfactory results for natural forests in complex environments. In this paper, we propose a trunk-growth (TG) method for single-tree point-cloud segmentation, and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan, China. First, the point normal vector and its Z-axis component are used as trunk-growth constraints. Then, the points surrounding the trunk are searched to account for regrowth. Finally, the nearest distributed branch and leaf points are used to complete the individual tree segmentation. The results show that the TG method can effectively segment individual trees with an average F-score of 0.96. The proposed method applies to many types of trees with various growth shapes, and can effectively identify shrubs and herbs in complex scenes of natural forests. The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.
Tài liệu tham khảo
Asadilla YSP, Ümüt HLK, Abdulla A, Maierdang KYM (2020) Terrestrial laser scanning for retrieving the structural parameters of Populus euphratica riparian forests in the lower reaches of the Tarim River, NW China. Acta Ecol Sin 40:4555–4565
Burt A, Disney M, Calders K (2018) Extracting individual trees from LiDAR point clouds using treeseg. Methods Ecol Evol 10:1–8
Cabo C, Ordóñez C, López-Sánchezb CA, Armesto J (2018) Automatic dendrometry: tree detection, tree height and diameter estimation using terrestrial laser scanning. Int J Appl Earth Obs Geoinf 69:164–174
Erwig M (2000) The graph Voronoi diagram with applications. Netw: Int J 36(3):156–163
FAO (Food and Agriculture Organization of the United Nations) (2003). Global forest resources assessment update: terms and definitions (final version). Forest Resources Assessment Programme, Rome, pp 1–34
Guo QH, Su Y, Hu T, Liu J (2018) LiDAR principles, processing and applications in forest ecology. Higher Education Press, Beijing, pp 111–131
Harikumar A, Bovolo F, Bruzzone L (2017) An internal crown geometric model for conifer species classification with high-density LiDAR data. IEEE Trans Geoence Remote Sens 55:2924–2940
Henning JG, Radtke PJ (2006) Detailed stem measurements of standing trees from ground-based scanning LiDAR. For Sci 52:67–80
Jin YG (2010) Botany, 2nd edn. Science Press, Beijing, pp 124–149
Li D, Pang Y, Yue CR, Zhao D, Xu GC (2012a) Extraction of individual tree DBH and height based on terrestrial laser scanner data. J Beijing For Univ 34:79–86 ((in Chinese))
Li WK, Guo QH, Jakubowski MK, Kelly M (2012b) A new method for segmenting individual trees from the LiDAR point cloud. Photogrammetric Engineering and Remote Sensing 78:75–84
Liang XL, Kankare V, Hyyppä J, Wang YS, Kukko A, Haggrén H, Yu XW, Kaartinen H, Jaakkola A, Guan FY, Holopainen M, Vastaranta M (2016) Terrestrial laser scanning in forest inventories. ISPRS J Photogramm Remote Sens 115:63–77
Liang XL, Litkey P, Hyyppä J, Kaartinen H, Vastaranta M, Holopainen M (2012) Automatic stem mapping using single-scan terrestrial laser scanning. IEEE Trans Geosci Remote Sens 50:661–670
Liu GLG, Ramakrishnan KG (2001) A*Prune: an algorithm for finding K shortest paths subject to multiple constraints. In: Proceedings IEEE INFOCOM 2001. Conference on computer communications. 20th annual joint conference of the IEEE computer and communications society, (Cat. No. 01CH37213), vol 2, pp 743–749
Liu LX, Pang Y, Li ZY (2016) Individual tree DBH and height estimation using terrestrial laser scanning (TLS) in a subtropical Forest. Sci Silvae Sin 52:26–37 ((in Chinese))
Liu XL, Sui LC, Bai YF, Zhao D, Zhao YJ, Liu YS, Zhai QP (2020) Biomass estimation of Caragana microphylla in the shrub-encroached grassland based on terrestrial laser scanning. J Remote Sens 24:894–903 ((in Chinese))
Liu GJ, Wang JL, Dong PL, Chen Y, Liu ZY (2018) Estimating individual tree height and diameter at breast height (DBH) from terrestrial laser scanning (TLS) data at plot level. Forests 9:398–417
Lou YB, Huang HY, Tang LY, Chen CC, Zhang H (2019) Tree height and diameter extraction with 3D reconstruction in a forest based on TLS. Remote Sens Technol Appl 34:243–252 ((in Chinese))
Lu JB, Wang H, Qin SH, Cao L, Pu RL, Li GL, Sun J (2020) Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River delta based on UAV and Backpack LiDAR point clouds. Int J Appl Earth Obs Geoinf 86:102014
Ma ZY, Pang Y, Li ZY, Lu H, Liu LX, Chen BW (2019) Fine classification of near-ground point cloud based on terrestrial laser scanning and detection of forest fallen wood. J Remote Sens 23:743–755 ((in Chinese))
Ma ZY, Pang Y, Wang D, Liang XJ, Chen BW, Lu H, Weinacker H, Koch B (2020) Individual tree crown segmentation of a larch plantation using airborne laser scanning data based on region growing and canopy morphology features. Remote Sens 12:1078
Maas HG, Bienert A, Scheller S, Keane E (2008) Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 29:1366–5901
Oveland I, Hauglin M, Gobakken T, Næsset E, Maalen-Johansen I (2017) Automatic estimation of tree position and stem diameter using a moving terrestrial laser scanner. Remote Sens 9:350
Rushdi AA, Swiler LP, Phipps ET, D’Elia M, Ebeida MS (2017) VPS: Voronoi piecewise surrogate models for high-dimensional data fitting. Int J Uncertain Quantif 7(1):1–21
Shugart HH, Saatchi S, Hall FG (2010) Importance of structure and its measurement in quantifying function of forest ecosystems. J Geophys Res 115(G2):1–16
Strîmbu VF, Strîmbu BM (2015) A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data. ISPRS J Photogramm Remote Sens 104:30–43
Tao SL, Wu FF, Guo QH, Wang YC, Li WK, Xue BL, Hu XY, Li P, Tian D, Li C, Yao H, Li YM, Xu GC, Fang JY (2015) Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories. ISPRS J Photogramm Remote Sens 110:66–76
Unger DR, Hung IK, Brooks R, Williams H (2014) Estimating number of trees, tree height and crown width using LiDAR data. Gisci Remote Sens 51:227–238
Wang JM, Chen XX, Cao L, An F, Chen BQ, Xue LF, Yun T (2019) Individual rubber tree segmentation based on ground-based LiDAR data and faster R-CNN of deep learning. Forests 10:793–813
Windrim L, Bryson M (2020) Detection, segmentation, and model fitting of individual tree stems from arborne laser scanning of forests using deep learning. Remote Sens 12:1469
Xiao W, Zaforemska A, Smigaj M, Wang YS, Gaulton R (2019) Mean shift segmentation assessment for individual forest tree delineation from airborne LiDAR data. Remote Sens 11:1263
Xing WL, Xing YQ, Huang Y, Qu L, You HT (2017) Individual tree segmentation of TLS point cloud data based on clustering of voxels layer by layer. J Cent South Univ For Technol 37:58–6471 ((in Chinese))
Yang QY, Dong ZY, Ma ZY, Wu Y, Cui Q, Lu H (2020) Coniferous forest crown segmentation algorithm of UAV images based on SFM. Trans Chin Soc Agric Mach 51:181–190 ((in Chinese))
Zhang JP, Wang JL, Liu GJ (2020) Vertical structure classification of a forest sample plot based on point cloud data. J Indian Soc Remote Sens 48(8):1215–1222
Zhang WM, Wan P, Wang TJ, Cai SS, Chen YM, Jin XL, Yan GJ (2019) A novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data. Remote Sens 11:211
Zhang WM, Wu X, Gao YK, Li HB (2018) A simplification algorithm for point cloud based on extraction. Opt Tech 44:733–735
Zhao J, Li J, Liu QH (2013) Review of forest vertical structure parameter inversion based on remote sensing technology. J Remote Sens 17:697–716 ((in Chinese))
Zhao XQ, Guo QH, Su YJ, Xue BL (2016) Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas. ISPRS J Photogramm Remote Sens 117:79–91
Zhong LS, Cheng L, Xu H, Wu Y, Chen YM, Li MC (2017) Segmentation of individual trees from TLS and MLS data. IEEE J Sel Top Appl Earth Obs Remote Sens 10:774–787