Individual tree parameter extraction and biomass estimation based on the quantitative structure model

YU Lei; XU Jiajia; NI Chen; KONG Lingyuan; HUANG Qingfeng; LIU Hua; TANG Xuehai

doi:10.7671/j.issn.1001-411X.202406034

Journal of South China Agricultural University > 2025 > 46(3): 379-389. > DOI: 10.7671/j.issn.1001-411X.202406034

YU Lei, XU Jiajia, NI Chen, et al. Individual tree parameter extraction and biomass estimation based on the quantitative structure model[J]. Journal of South China Agricultural University, 2025, 46(3): 379-389. DOI: 10.7671/j.issn.1001-411X.202406034

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Individual tree parameter extraction and biomass estimation based on the quantitative structure model

YU Lei^1,,
XU Jiajia¹,
NI Chen¹,
KONG Lingyuan¹,
HUANG Qingfeng^{1, 2},
LIU Hua^{1, 2},
TANG Xuehai^{1, 2, ,}

1.
School of Forestry and Landscape Architecture, Anhui Agricultural University/Anhui Provincial Key Laboratory of Forest Resouecrs and Silviculture, Hefei 230036, China
2.
Anhui Dabie Mountain Forest Ecosystem National Positioning Observation and Research Station, Lu’an 237300, China

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Received Date: June 22, 2024
Available Online: March 02, 2025
Published Date: March 03, 2025

Graphical Abstract

Abstract

Abstract

Objective
This article takes the deciduous broad-leaved forest in the Tianma National Nature Reserve in Anhui Province as the research subject, exploring the application potential of the quantitative structure model in biomass estimation of individual tree in complex environment using unmanned aerial vehicle laser scanning data.
Method
Through ground surveys and the use of unmanned aerial vehicles, plot data and LiDAR point cloud data were collected, with the ground data serving as the reference measurements. The comparative shortest-path algorithm was used for point cloud segmentation. Subsequently, tree parameters (such as diameter at breast height, trunk volume, branch volume, number of branches, canopy base height, canopy area, canopy volume, and crown width) were extracted from the segmented individual tree point clouds using the quantitative structure model. Pearson correlation coefficient and variance inflation factor were then employed for variable selection of the parameters. Finally, an individual tree biomass estimation model was constructed based on the three machine learning algorithms.
Result
Among the biomass models, the one based on random forest (RF) achieved the best training performance (R² = 0.880 0, RMSE = 192.81 kg, rRMSE = 29.88%). The performance of the multilayer perceptron (MLP) model (R² = 0.820 0, RMSE = 233.62 kg, rRMSE = 36.65%) was quite similar to that of the support vector machine (SVM) model (R² = 0.810 0, RMSE = 243.67 kg, rRMSE = 37.77%).
Conclusion
This article confirms that the tree parameters extracted from the quantitative structure model can be used to construct a high-precision biomass estimation model for deciduous broadleaf species, providing a new method for resource surveys in complex forest environment.
- Unmanned aerial vehicle laser scanning,
- Quantitative structure model,
- Biomass estimation,
- Tree parameter

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References (42)

References

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Individual tree parameter extraction and biomass estimation based on the quantitative structure model

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