Laser & Optoelectronics Progress, Volume. 56, Issue 12, 122801(2019)
Classification of Tree Species Based on LiDAR Point Cloud Data
Figures & Tables(16)
Fig. 1. Examples of point cloud sample trees based on LiDAR system. (a) Metasequoia glyptostroboides; (b) Salix babylonica; (c) Ligustrum lucidum; (d) bamboo; (e) Malus pumila Mill.tree
Fig. 3. Preprocessing diagram for point cloud of Qianjiang new town Forest Park in Hangzhou collected by LiDAR
Fig. 4. Preprocessing diagram for point cloud of Hongqipo farm in Akesu Prefecture, Xinjiang Uygur Autonomous Region collected by LiDAR
Fig. 5. Location relationship between central voxels and adjacent voxels in 13 directions of 3D space
Fig. 6. Segmentation diagram. (a) Initial scanned data of sample tree; (b) extracted leaf points using branch leaf segment algorithm; (c) voxelization for scanned leaf points; (d) top view for leaf point cloud divided into 8 parts; (e) 1st part and 8th part after division
Table 1. 0 Classification results of tree species based on all parameters
View tableTable 1. 0 Classification results of tree species based on all parameters
Tree specie Metasequoia glyptostroboides Salix babylonica Ligustrum lucidum Bamboo Malus pumila Mill.Leakage rate /% Misjudged rate /% Metasequoia glyptostroboides 15 0 2 0 0 16.67 11.76 Salix babylonica 1 11 0 1 2 21.43 26.67 Ligustrum lucidum 1 2 15 1 0 25 21.05 Bamboo 0 1 1 25 0 7.41 7.41 Malus pumila Mill.1 0 2 0 19 9.52 13.63 Total 18 14 20 27 21 — — Correct rate /% 83.33 78.57 75 92.59 90.48 — — Overall accuracy /% 85 — —
Table 1. Parameter statistics for sample trees
View tableTable 1. Parameter statistics for sample trees
Specie Number of trees Average tree height /m Average crown width /m Average crown volume /m3 Average number of scanned points Metasequoia glyptostroboides 54 25.09±2.36 6.67±0.64 468.85±38.49 42884 Salix babylonica 42 12.97±1.17 9.11±1.18 430.24±36.34 39861 Ligustrum lucidum 60 10.31±1.08 5.26±0.63 131.22±12.45 12854 Bamboo 81 12.95±1.53 2.42±0.41 33.37±3.36 4324 Malus pumila Mill.63 8.76±0.74 7.62±0.86 233.97±21.17 24339
Table 3. Number of training samples and validation samples
View tableTable 3. Number of training samples and validation samples
Specie Number of training samples Number of validation samples Total number Metasequoia glyptostroboides 36 18 54 Salix babylonica 28 14 42 Ligustrum lucidum 40 20 60 Bamboo 54 27 81 Malus pumila Mill.42 21 63 Total 200 100 300
Table 4. Classification results of tree species based on SF parameters
View tableTable 4. Classification results of tree species based on SF parameters
Tree specie Metasequoia glyptostroboides Salix babylonica Ligustrum lucidum Bamboo Malus pumila Mill.Leakage rate /% Misjudged rate /% Metasequoia glyptostroboides 10 2 2 0 3 44.44 41.18 Salix babylonica 2 10 7 3 4 28.57 61.54 Ligustrum lucidum 0 0 6 3 0 70 33.33 Bamboo 2 2 0 18 0 33.33 18.18 Malus pumila Mill.4 0 5 3 14 33.33 46.15 Total 18 14 20 27 21 — — Correct rate /% 55.56 71.43 30 66.67 66.67 — — Overall accuracy /% 58 — —
Table 5. Classification results of tree species based on TF parameters
View tableTable 5. Classification results of tree species based on TF parameters
Tree specie Metasequoia glyptostroboides Salix babylonica Ligustrum lucidum Bamboo Malus pumila Mill.Leakage rate /% Misjudged rate /% Metasequoia glyptostroboides 11 2 2 0 3 38.89 38.89 Salix babylonica 1 9 5 4 3 35.71 59.09 Ligustrum lucidum 1 3 11 4 0 45 42.11 Bamboo 3 0 0 18 0 33.33 14.29 Malus pumila Mill.2 0 2 1 15 28.57 25 Total 18 14 20 27 21 — — Correct rate /% 61.11 64.29 55 66.67 71.43 — — Overall accuracy /% 64 — —
Table 6. Classification results of tree species based on CF parameters
View tableTable 6. Classification results of tree species based on CF parameters
Tree specie Metasequoia glyptostroboides Salix babylonica Ligustrum lucidum Bamboo Malus pumila Mill.Leakage rate /% Misjudged rate /% Metasequoia glyptostroboides 11 1 2 3 1 38.89 38.89 Salix babylonica 3 8 5 2 3 42.86 61.9 Ligustrum lucidum 3 3 10 4 1 50 52.38 Bamboo 0 0 2 17 2 37.04 19.05 Malus pumila Mill.1 2 1 1 14 33.33 26.32 Total 18 14 20 27 21 — — Correct rate /% 61.11 57.14 50 62.96 66.67 — — Overall accuracy /% 60 — —
Table 7. Classification results of tree species based on SF and TF parameters
View tableTable 7. Classification results of tree species based on SF and TF parameters
Tree specie Metasequoia glyptostroboides Salix babylonica Ligustrum lucidum Bamboo Malus pumila Mill.Leakage rate /% Misjudged rate /% Metasequoia glyptostroboides 14 1 1 3 1 22.22 30 Salix babylonica 2 10 3 0 2 28.57 41.18 Ligustrum lucidum 2 2 12 2 0 40 33.33 Bamboo 0 0 3 21 2 22.22 19.23 Malus pumila Mill.0 1 1 1 16 23.81 15.79 Total 18 14 20 27 21 — — Correct rate /% 77.78 71.43 60 77.78 76.19 — — Overall accuracy /% 73 — —
Table 8. Classification results of tree species based on SF and CF parameters
View tableTable 8. Classification results of tree species based on SF and CF parameters
Tree specie Metasequoia glyptostroboides Salix babylonica Ligustrum lucidum Bamboo Malus pumila Mill.Leakage rate /% Misjudged rate /% Metasequoia glyptostroboides 14 0 2 2 1 22.22 26.32 Salix babylonica 2 9 3 1 3 37.71 50 Ligustrum lucidum 1 2 11 3 0 45 35.29 Bamboo 0 1 2 21 2 22.22 19.23 Malus pumila Mill.1 2 2 0 15 28.57 25 Total 18 14 20 27 21 — — Correct rate /% 77.78 62.29 55 77.78 71.43 — — Overall accuracy /% 70 — —
Table 9. Classification results of tree species based on TF and CF parameters
View tableTable 9. Classification results of tree species based on TF and CF parameters
Tree specie Metasequoia glyptostroboides Salix babylonica Ligustrum lucidum Bamboo Malus pumila Mill.Leakage rate /% Misjudged rate /% Metasequoia glyptostroboides 15 0 3 0 2 16.67 25 Salix babylonica 1 10 2 0 1 28.57 28.57 Ligustrum lucidum 1 2 13 2 0 35 27.77 Bamboo 0 0 1 23 2 14.81 11.54 Malus pumila Mill.1 2 1 2 16 23.81 27.27 Total 18 14 20 27 21 — — Correct rate /% 83.33 71.43 65 85.19 76.19 — — Overall accuracy /% 77 — —
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Xiangyu Chen, Ting Yun, Lianfeng Xue, Ying'an Liu. Classification of Tree Species Based on LiDAR Point Cloud Data[J]. Laser & Optoelectronics Progress, 2019, 56(12): 122801
Paper Information
Category: Remote Sensing and Sensors
Received: Nov. 23, 2018
Accepted: Jan. 9, 2019
Published Online: Jun. 13, 2019
The Author Email: Chen Xiangyu (1016733396@qq.com), Liu Ying'an (lyastat@163.com)
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