# Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Site and Data Collection

#### 2.2. Wood-leaf Separation

#### 2.3. Individual Leaf Segmentation

#### 2.3.1. Extracting the Central Area Points of Individual Leaves

#### 2.3.2. Clustering of Leaf Central Area Points

#### 2.3.3. Individual Leaf Segmentation

#### 2.4. Leaf Attributes Calculation Based on Classified Leaf Points

## 3. Results

#### 3.1. Plant Leaf Classification

#### 3.2. Leaf Segmentation

#### 3.3. Leaf Attribute Calculation

## 4. Discussion

#### 4.1. Tree Species Effects

#### 4.2. Sensitivity Analysis of Search Radius (${r}_{1}$)

#### 4.3. Recommendations

## 5. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Experimental tree selection and scanning data collection. Terrestrial laser scanning (TLS) was used to scan the different tree species, namely (

**a**) Ehretia macrophylla, (

**b**) crape myrtle and (

**c**) Fatsia japonica; (

**d**–

**f**) show the scanned point clouds and structural features of the experimental trees.

**Figure 2.**Box plot showing the distributions of the true leaf inclination angles and leaf azimuth angles of the three experimental trees. (

**a**–

**c**): the distributions of the true leaf inclination angles of three experimental trees with proportion of sampled leaves number increasing. (

**d**–

**f**): the distributions of the true leaf azimuth angles of three experimental trees with proportion of sampled leaves number increasing. The plots show that when the number of leaves analysed reaches 40% of the total number of leaves in the tree crown, the leaf inclination angle and leaf azimuth angle values tend to be relatively stable. Therefore, in the verification below, the analytic dataset accounted for 40% of the total number of leaves in the experimental tree.

**Figure 3.**Results of wood-leaf separation with a series of features for each point, i.e., the normal vector, the structure tensor and the distribution of the point normal vector, for Ehretia macrophylla (first row), crape myrtle (second row) and Fatsia japonica (third row). (

**a**) Initial point clouds, (

**b**) wood points and (

**c**) leaf points.

**Figure 4.**Flowchart of the proposed algorithm of individual leaf segmentation. The entire experimental method consists of three stages. (

**a**) The first stage is the extraction of the central area points of each leaf using a sphere neighbourhood model with an adaptive radius and four auxiliary criteria. The black dots extracted from the first stage comprise the central area points of each leaf. (

**b**–

**d**) In the second stage, the central area points of each leaf are clustered using the density-based spatial clustering of applications with noise algorithm (DBSCAN) algorithm, and the centre point of each leaf is obtained, as labelled by the red star. (

**d**–

**e**) In the third stage, individual leaf separation is achieved using a spatial watershed algorithm based on the centre point of each extracted leaf surface and the two morphology-related parameters.

**Figure 5.**Schematic illustrating the extraction of the centre point of each leaf. (

**a**) The radius of the sphere is ${r}_{1}=\mathrm{width}/4$. Black dots indicate the central area points extracted from each leaf. (

**a2**–

**a4**) show that criteria 2 and 3 are satisfied, i.e., the distance of point ${p}_{i}$ and its neighbourhood points ${p}_{i,j}$ to the fitting plane $\overline{{S}_{i}}$ is less than the thresholds. Criterion 4 guarantees that the neighbourhood points ${p}_{i,j}^{\prime}$ are uniformly distributed around point ${p}_{i}^{\prime}$ on the great circle plane ${S}_{i}^{c}$. (

**b1**) shows a case that satisfies criterion 4; (

**b2**) shows a case that does not satisfy criterion 4.

**Figure 6.**Figure 6 depicts the role of Equation (8) in the segmentation of an individual leaf, where the distance (i.e., the Euclidean distance between the centre point and the non-central area point) and the included angle (i.e., the included angle between the vector composed of the centre point and the non-central area point and the normal vector of the leaf blade) are used for leaf segmentation. (

**a**) The distance is the main contributing factor to completing individual leaf segmentation, which means that the non-central area point $\tilde{{p}_{l}}$ is closer to the real leaf centre point ${c}_{k}$. (

**b**) The included angle between the vector $\overrightarrow{\left({c}_{k},\tilde{{p}_{l}}\right)}$ and the normal vector $\overrightarrow{{c}_{k}}$ of each leaf is the main contributing factor to completing individual leaf segmentation. The included angle of the two vectors noted above composed of points belonging to the same leaf surface is nearly 90 degrees, which decreases the value of the second term on the right-hand side of Equation (8).

**Figure 7.**Scatter plots of point cloud canopy data obtained using our proposed method to automatically segment and randomly colour leaves. The obtained individual foliage points were prepared for leaf parameter estimates. (

**a**) Ehretia macrophylla; (

**b**) crape myrtle; and (

**c**) Fatsia japonica. (

**a1**,

**b1**,

**c1**): The black dots indicate the central area points of each leaf extracted by the sphere neighbourhood model, and the green dots represent the entire leaf points of each leaf. (

**a2**,

**b2**,

**c2**): The central area points were clustered using density-based spatial clustering with the noise algorithm and visualized with random colours. (

**a3**,

**b3**,

**c3**): The leaf segmentation results, with partial close-up images presented in (

**a4**,

**b4**,

**c4**), respectively.

**Figure 8.**Comparisons of the estimated leaf attributes between our algorithm and the manual method for the three target trees: (

**a1**,

**b1**,

**c1**): scatter plots of the reference values obtained using LI-3000C versus the LiDAR-estimated individual tree leaf areas obtained using our method; (

**a2**,

**b2**,

**c2**): scatter plots of the reference values obtained by manual measurements and individual tree leaf inclination angles obtained using our method; (

**a3**,

**b3**,

**c3**): scatter plots of the reference values obtained by manual measurements and the individual tree leaf azimuth angles obtained using our method.

**Figure 9.**Sensitivity analysis of the effect of the search radius on the segmentation accuracy of individual leaves for the three experimental tree plots: (

**a**) Ehretia macrophylla; (

**b**) crape myrtle; and (

**c**) Fatsia japonica. The intersection of two lines in each figure represents the optimal results for the detected leaf number, indicating the value that was closest to the true value. The experiments show that an ${r}_{1}$ value of one quarter of the average leaf width of each tree crown, and a $MaxR$ value of half of the average leaf width of each tree crown obtain the optimal individual leaf segmentation precision.

Tree Species | Ehretia Macrophylla | Crape Myrtle | Fatsia Japonica |
---|---|---|---|

Total number of points | 36,604 | 107,914 | 247,133 |

Tree height (m) | 3.5 | 3.42 | 1.6 |

Crown projection area (m^{2}) | 1.274 | 1.975 | 3.126 |

Average spatial sampling (cm) | 0.55 | 0.45 | 0.42 |

Leaf point number (classified/actual) | 27,841/29,387 | 72,639/78,564 | 184,721/201,648 |

Branch point number (classified/actual) | 8763/7217 | 35,275/29,350 | 62,412/45,485 |

Overall accuracy | 94.73% | 92.46% | 91.61% |

**Table 2.**Statistics of leaf attributes estimate for the three individual trees using our method and manual measurement.

Tree Species | Ehretia Macrophylla | Crape Myrtle | Fatsia Japonica | |||
---|---|---|---|---|---|---|

Upper | Middle | Lower | Upper | Lower | Tree Crown | |

NP | 12,477 | 14,316 | 3438 | 35,028 | 37,639 | 103,177 |

PD (pts·m^{−2}) | 4320 | 6260 | 6531 | 5844 | 9410 | 51,484 |

NLUM/ NLMM | 44/ 43 | 87/ 96 | 27/ 29 | 501/ 543 | 440/ 495 | 187/ 166 |

RELN | 2.33% | 9.38% | 6.90% | 7.73% | 11.11% | 12.65% |

RELL | 5.57% | 3.48% | 1.16% | 1.75% | 15.1% | 1.44% |

RELW | 2.75% | 10.85% | 3.55% | 0.55% | 0.93% | 0.93% |

RELA | 2.67% | 6.98% | 4.67% | 2.26% | 16.12% | 7.7% |

LIAUM/ LIAMM (°) | 28.4–88.0/ 25.1–86.8 | 9.1–89.8/ 7.5–89.0 | 9.3–81.2/ 2.7–77.8 | 7.9–89.7/ 11.7–88.2 | 5.2–84.0/ 1.2–89.8 | 9.6–87.8/ 6.8–89.2 |

LAAUM/ LAAMM (°) | 44.8–322.0/ 31.3–313.5 | 11.8–349.6/ 3.3–353.6 | 14.7–324.5/ 15.6–323.8 | 18.6–227.0/ 12.7–269.4 | 19.7–295.7/ 23.0–309.3 | 53.3–268.8/ 50.9–266.3 |

**Table 3.**Statistics for the overall results of the leaf morphological features (leaf area, leaf azimuthal angle and leaf inclination angle).

Tree species | Position | R^{2} | RMSE | ||||
---|---|---|---|---|---|---|---|

LA | LIA | LAA | LA (cm^{2}) | LIA (°) | LAA (°) | ||

Ehretia macrophylla | Upper | 0.987 | 0.894 | 0.990 | 8.805 | 5.686 | 7.709 |

Middle | 0.959 | 0.914 | 0.978 | 7.596 | 6.489 | 7.316 | |

Lower | 0.888 | 0.831 | 0.982 | 9.357 | 7.841 | 8.151 | |

All | 0.971 | 0.908 | 0.981 | 8.508 | 6.806 | 7.680 | |

Crape myrtle | Upper | 0.934 | 0.864 | 0.941 | 7.042 | 8.459 | 7.011 |

Lower | 0.801 | 0.905 | 0.934 | 4.579 | 8.260 | 8.180 | |

All | 0.873 | 0.901 | 0.938 | 6.001 | 8.365 | 7.573 | |

Fatsia japonica | All | 0.899 | 0.849 | 0.947 | 5.744 | 6.158 | 3.946 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Xu, Q.; Cao, L.; Xue, L.; Chen, B.; An, F.; Yun, T. Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data. *Remote Sens.* **2019**, *11*, 15.
https://doi.org/10.3390/rs11010015

**AMA Style**

Xu Q, Cao L, Xue L, Chen B, An F, Yun T. Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data. *Remote Sensing*. 2019; 11(1):15.
https://doi.org/10.3390/rs11010015

**Chicago/Turabian Style**

Xu, Qiangfa, Lin Cao, Lianfeng Xue, Bangqian Chen, Feng An, and Ting Yun. 2019. "Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data" *Remote Sensing* 11, no. 1: 15.
https://doi.org/10.3390/rs11010015