Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Sources
2.2.1. Image Data
2.2.2. Sample Data
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
4. Results and Analysis
4.1. Single Wood Extraction Results
4.2. Biomass Estimation Results
5. Discussions
- (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
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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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/m2) | 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 | ||
---|---|---|---|---|
R2 | RMSE | R2 | 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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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
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 StyleLi, 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