# Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images

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## Abstract

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^{2}= 0.9346, RMSE = 8.8399) and the test set (R

^{2}= 0.8670, RMSE = 15.0377). RF was more suitable for the biomass estimation of Eucalyptus in this study. The spatial resolution of Eucalyptus biomass distribution was 0.05 m in this study, which had higher accuracy and was more accurate. It can provide data reference for the details about biomass distribution of Eucalyptus in the majority of provinces, and has certain practical reference significance.

## 1. Introduction

## 2. Study Area and Data Sources

#### 2.1. Study Area

^{2}. The spring is cold and wet with more rainfall, and the summer is hot and muggy.

^{2}. The geographical location and orthophoto of the study area are shown in Figure 1.

#### 2.2. Data Sources

#### 2.2.1. Image Data

#### 2.2.2. Sample Data

^{2}= 0.953 (Equation (2)), as recorded in the manual on biomass modelling of major forest trees in China.

## 3. Research Methods

#### 3.1. Single Wood Extraction

#### 3.2. Biomass Estimation

#### 3.2.1. Variable Filtering

#### 3.2.2. Multiple Stepwise Regression Method

#### 3.2.3. Random Forest Algorithm

#### 3.2.4. Support Vector Machines

#### 3.2.5. Decision Trees

#### 3.3. Accuracy Evaluation

^{2}and the root mean square error RMSE to evaluate the prediction accuracy of the model. The formulae are as follows:

^{2}indicates the error between the estimated and actual results of the target parameter. The smaller the value of R

^{2}, the less accurate the model built is in representing the target, and the closer R

^{2}is to 1, the more accurate it is. RMSE indicates the distance between the target estimate and the actual result, and is the preferred performance measure when performing regression. The smaller the value of the RMSE, the better the fit of the model.

## 4. Results and Analysis

#### 4.1. Single Wood Extraction Results

#### 4.2. Biomass Estimation Results

^{2}of 0.9375 and the RMSE of 9.8024; followed by the SVR model with the R

^{2}of 0.7173 and the RMSE of 14.1331; the decision tree model ranked third with the R

^{2}of 0.5722 and the RMSE of 16.7032; the multiple linear regression model was the worst fit with the R

^{2}of 0.4132 and the RMSE of 19.4161. The smaller the value of the coefficient of determination R

^{2}, the lower the degree of model fit, and the closer the coefficient of determination R

^{2}is to 1, the better the fit of the model and the higher the model accuracy. The smaller the value of the root mean square error RMSE, the better the fit of the model. The results of these two metrics demonstrated that the RF model had the highest overall accuracy on the training data; overall, all three approaches to machine learning built models with higher accuracy than the linear regression model.

^{2}of 0.7855 and a root mean square error RMSE of 13.0377. The SVR model performed second best, with a coefficient of determination R

^{2}of 0.4822 and a root mean square error RMSE of 17.1953. Multiple regression model number three, with a coefficient of determination R

^{2}of 0.3490 and a root mean square error RMSE of 20.2352. The decision tree model was the least effective, with a coefficient of determination R

^{2}of 0.2856 and a root mean square error RMSE of 22.3906.

## 5. Discussions

^{2}= 0.41, RMSE = 23.0 mg-hm

^{−2}) [26]; Xu used waveform data to invert forest leaf area index and single wood biomass estimation in (R

^{2}= 0.708, RMSE = 142.664 kg) [27]; Liu estimated the above-ground biomass of single wood in Changbai Larch plantation in Changbai Mountain area (R

^{2}= 0.799, RMSE = 0.93 kg) [28]; Zhang estimated the above-ground biomass of single wood based on canopy height model data, combined with the biomass estimation was based on the canopy height model data combined with the measured data from the Penobscot Experimental Forest (R

^{2}= 0.90, RMSE = 54.46 kg) [13]; and the R

^{2}index and RMSE index of random forest were better in this paper.

- (1)
- In terms of single wood segmentation in dense vegetation cover areas, how to determine whether there are small trees below the dense canopy and how to identify and segment these small trees are issues that need to be studied in depth in the next step. The further inclusion of data sources, such as ground-based radar data, on top of fused data can be considered in order to obtain more information on tree structure and location, and thus improve the accuracy of the Eucalyptus biomass estimation model.
- (2)
- Data such as tree age and storage volume were not used in this study, and further consideration can be given to adding data such as the age of Eucalyptus trees and the storage volume of the area in which they are located in subsequent studies to further improve the accuracy of biomass modelling.
- (3)
- The combination of multi-source data and machine learning algorithms can provide accurate and rapid biomass measurements at the single-wood scale, which can provide more accurate information for the management of regional biomass resources statistics. Deep learning algorithms may also be considered in the future to explore the performance of different algorithms for biomass estimation of eucalypts over large areas.

## 6. Conclusions

^{2}= 0.9375, RMSE = 9.8024); the SVR model was the second best (R

^{2}= 0.7173, RMSE = 14.1331); on the test set, the RF model also performed the best (R

^{2}= 0.7855, RMSE = 13.0377); the SVR model was the second best (R

^{2}= 0.4822, RMSE = 17.1953). RMSE = 17.1953. Overall, the RF algorithm was more suitable for predicting the biomass of eucalypts.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 6.**The optimisation process of CHM. (

**a**) raw images; (

**b**) low pass filtering; (

**c**) gaussian law pass filtering; (

**d**) median filtering; (

**e**) invalid value; (

**f**) optimised CHM.

Parameters | Specification |
---|---|

Flight height/m | 500 |

Ground speed/kn | 60 |

Mapping bandwidth/m | 175 |

Laser wavelength/nm | 1064 |

Pulse repetition rate/kHz | 50~1000 |

Scanning view/(°) | 10~60 |

Average point cloud density/(pts/m^{2}) | 180 |

Positioning and orientation systems | POS AV™ AP60 (OEM); 220-channel dual-frequency GNSS receiver; GNSS airborne antenna with iridium filter; Highly accurate AIMU (Type 57); |

Tree Number | Longitude | Latitude | DBH/cm | Height/m |
---|---|---|---|---|

1 | 113°47′22″ E | 23°19′53″ N | 35.00 | 23.80 |

2 | 113°47′28″ E | 23°19′42″ N | 20.30 | 17.00 |

3 | 113°47′29″ E | 23°19′43″ N | 7.50 | 7.80 |

4 | 113°47′29″ E | 23°19′53″ N | 18.40 | 14.80 |

5 | 113°47′25″ E | 23°19′51″ N | 4.00 | 3.10 |

… | … | … | … | … |

98 | 113°47′24″ E | 23°19′51″ N | 19.00 | 16.20 |

99 | 113°47′28″ E | 23°19′50″ N | 13.60 | 14.20 |

100 | 113°47′22″ E | 23°19′49″ N | 23.50 | 19.00 |

Methods | Training Set | Test Set | ||
---|---|---|---|---|

R^{2} | RMSE | R^{2} | RMSE | |

MLR | 0.4132 | 19.4161 | 0.3490 | 20.2352 |

RF | 0.9375 | 9.8024 | 0.7855 | 13.0377 |

SVR | 0.7173 | 14.1331 | 0.4822 | 17.1953 |

CART | 0.5722 | 16.7032 | 0.2856 | 22.3906 |

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**MDPI and ACS Style**

Li, Y.; Wang, R.; Shi, W.; Yu, Q.; Li, X.; Chen, X.
Research on Accurate Estimation Method of *Eucalyptus* Biomass Based on Airborne LiDAR Data and Aerial Images. *Sustainability* **2022**, *14*, 10576.
https://doi.org/10.3390/su141710576

**AMA Style**

Li Y, Wang R, Shi W, Yu Q, Li X, Chen X.
Research on Accurate Estimation Method of *Eucalyptus* Biomass Based on Airborne LiDAR Data and Aerial Images. *Sustainability*. 2022; 14(17):10576.
https://doi.org/10.3390/su141710576

**Chicago/Turabian Style**

Li, Yiran, Ruirui Wang, Wei Shi, Qiang Yu, Xiuting Li, and Xingwang Chen.
2022. "Research on Accurate Estimation Method of *Eucalyptus* Biomass Based on Airborne LiDAR Data and Aerial Images" *Sustainability* 14, no. 17: 10576.
https://doi.org/10.3390/su141710576