Fig. 1. Delineation effect of individual tree in an example area of Nanjing Forestry University

Fig. 2. Scanned point clouds of sample trees. (a) Metasequoia; (b) palm; (c) spaindus; (d) rubber tree; (e) bamboo

Fig. 3. Overview of work flow of tree species identification

Fig. 4. Schematic of definition of partial parameters associated with apparent characteristics of trees. (a) Side view of sample tree (rubber tree); (b) top view of sample tree (rubber tree)

Fig. 5. Schematic of acquisition of features related to grids in apparent characteristics of trees. (a) Top view of sample tree (rubber tree) divided into 8 segements and central vertical section of each segement; (b) schematic for meshing central vertical section

Fig. 6. Sample trees (rubber tree and metasequoia) described based on results of V-feature. (a) Scanned point clouds of rubber tree and point cloud projections in the second and eighth rectangular layers; (b) scanned point clouds of metasequoia and point cloud projections in the second and eighth rectangular layers; (c) line chart indicating V-feature of each layer for rubber tree and metasequoia

Fig. 7. Sample trees (rubber tree and metasequoia) described based on results of L-feature. (a) Point cloud projections and coordinate system representation regarding to L_k(r) function for rubber tree and metasequoia in the second rectangular layer; (b) point cloud projections and coordinate system representation regarding to L_k(r) function for rubber tree and metasequoia in the eighth rectangular

Fig. 8. Boxplots of classification accuracies based on relative clustering characteristics. (a) Based on V-feature; (b) based on L_max-feature; (c) based on L_min-feature; (d) based on r_max-feature; (e) based on r_min-feature

Fig. 9. Boxplots of classification accuracies based on features of point cloud distribution

Fig. 10. Boxplots of classification accuracies based on apparent features

Fig. 11. Boxplots of classification accuracies based on optimal features

Fig. 12. Classification map of individual tree species in an example area of Nanjing Forestry University based on optimal features

Table 1. Characterization of basic structures of sample trees in statistics

Table 2. Parameters associated with relative clustering characteristics of trees

Table 3. Classification results of cross-validation based on relative clustering features of {V₂,V₄,V₈,L

Dataset Metasequoia Palm Sapindus Rubbertree Bamboo Total Misjudgementrate /% Leakagerate /% Metasequoia 10 1 1 1 0 13 23.1 37.5 Palm 1 11 1 2 1 16 31.3 38.9 Sapindus 2 2 8 4 2 18 55.6 42.9 Rubber tree 2 2 3 9 3 19 52.6 47.1 Bamboo 1 2 1 1 9 14 35.7 40 Correct number 10 11 8 9 9 47 Number of training samples 16 18 14 17 15 80 Accuracy /% 62.5 61.1 57.1 52.9 60 58.8

Table 4. Classification results of cross-validation based on features of point cloud distribution of {P_H,R_H40%-60%,R_H60%-80%}

Dataset Metasequoia Palm Sapindus Rubbertree Bamboo Total Misjudgementrate /% Leakagerate /% Metasequoia 12 0 1 1 1 15 20 25 Palm 1 11 3 1 1 17 35.3 38.9 Sapindus 1 2 9 2 1 15 40 42.9 Rubber tree 2 2 1 10 3 18 44.5 47.1 Bamboo 0 3 0 3 9 15 40 40 Correct number 12 11 9 10 9 51 Number of training samples 16 18 14 17 15 80 Accuracy /% 75 61.1 64.3 58.8 60 63.8

Table 5. Classification results of cross-validation based on apparent features of {R_C/D,η_crown,Q_L}

Dataset Metasequoia Palm Sapindus Rubbertree Bamboo Total Misjudgementrate /% Leakagerate /% Metasequoia 29 2 0 0 0 31 6.5 9.4 Palm 2 31 2 1 0 36 13.9 13.9 Sapindus 0 2 24 1 1 28 14.3 14.3 Rubber tree 1 1 2 29 2 35 17.1 14.7 Bamboo 0 0 0 3 27 30 10 10 Correct number 29 31 24 29 27 140 Number of training samples 32 36 28 34 30 160 Accuracy /% 90.6 86.1 85.7 85.3 90 87.5

Table 6. Classification results of cross-validation based on optimal features of {V₂,V₄,V₈,L_{max_2}