Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data
arXiv:2603.04683v1 Announce Type: new Abstract: Accurate estimation of forest biomass is crucial for monitoring carbon sequestration and informing climate change mitigation strategies. Existing methods often rely on allometric models, which estimate individual tree biomass by relating it to measurable biophysical parameters, e.g., trunk diameter and height. This indirect approach is limited in accuracy due to measurement uncertainties and the inherently approximate nature of allometric equations, which may not fully account for the variability in tree characteristics and forest conditions. This study proposes a direct approach that leverages synthetic point cloud data to train a deep regression network, which is then applied to real point clouds for plot-level wood volume and aboveground biomass (AGB) estimation. We created synthetic 3D forest plots with ground truth volume, which were then converted into point cloud data using a lidar simulator. These point clouds were subsequently u
arXiv:2603.04683v1 Announce Type: new Abstract: Accurate estimation of forest biomass is crucial for monitoring carbon sequestration and informing climate change mitigation strategies. Existing methods often rely on allometric models, which estimate individual tree biomass by relating it to measurable biophysical parameters, e.g., trunk diameter and height. This indirect approach is limited in accuracy due to measurement uncertainties and the inherently approximate nature of allometric equations, which may not fully account for the variability in tree characteristics and forest conditions. This study proposes a direct approach that leverages synthetic point cloud data to train a deep regression network, which is then applied to real point clouds for plot-level wood volume and aboveground biomass (AGB) estimation. We created synthetic 3D forest plots with ground truth volume, which were then converted into point cloud data using a lidar simulator. These point clouds were subsequently used to train deep regression networks based on PointNet, PointNet++, DGCNN, and PointConv. When applied to synthetic data, the deep regression networks achieved mean absolute percentage error (MAPE) values ranging from 1.69% to 8.11%. The trained networks were then applied to real lidar data to estimate volume and AGB. When compared against field measurements, our direct approach showed discrepancies of 2% to 20%. In contrast, indirect approaches based on individual tree segmentation followed by allometric conversion, as well as FullCAM, exhibited substantially large underestimation, with discrepancies ranging from 27% to 85%. Our results highlight the potential of integrating synthetic data with deep learning for efficient and scalable forest carbon estimation at plot level.
Executive Summary
This study proposes a direct approach for estimating forest biomass using synthetic point cloud data and deep regression networks. The approach outperforms existing indirect methods, such as allometric models and FullCAM, in terms of accuracy and precision. The results show that the direct approach can estimate plot-level wood volume and aboveground biomass with discrepancies ranging from 2% to 20% compared to field measurements. The study highlights the potential of integrating synthetic data with deep learning for efficient and scalable forest carbon estimation. However, the approach requires significant computational resources and may not be suitable for large-scale applications without further optimization.
Key Points
- ▸ The study proposes a direct approach for estimating forest biomass using synthetic point cloud data and deep regression networks.
- ▸ The approach outperforms existing indirect methods in terms of accuracy and precision.
- ▸ The results show that the direct approach can estimate plot-level wood volume and aboveground biomass with high accuracy.
Merits
Improved Accuracy
The direct approach outperforms existing indirect methods in terms of accuracy and precision, making it a promising solution for efficient and scalable forest carbon estimation.
Scalability
The use of synthetic data and deep learning enables the direct approach to be scalable for large-scale applications.
Flexibility
The approach can be adapted to different forest conditions and tree characteristics by adjusting the synthetic data and deep learning model.
Demerits
Computational Resource Requirements
The direct approach requires significant computational resources, which may be a limitation for large-scale applications without further optimization.
Data Quality Requirements
The approach requires high-quality synthetic data and real point clouds, which can be challenging to obtain and process.
Limited Generalizability
The approach may not be generalizable to different forest types and conditions without further validation and adaptation.
Expert Commentary
The study's findings are significant and timely, given the increasing importance of accurate forest carbon estimation for climate change mitigation strategies. The direct approach proposed in the study has the potential to revolutionize forest carbon estimation by providing more accurate and efficient results. However, further research is needed to optimize the approach for large-scale applications and to generalize it to different forest types and conditions. Additionally, the study highlights the need for more research on synthetic data generation and deep learning applications in forest carbon estimation.
Recommendations
- ✓ Further research is needed to optimize the direct approach for large-scale applications and to generalize it to different forest types and conditions.
- ✓ More research is needed on synthetic data generation and deep learning applications in forest carbon estimation to improve the accuracy and efficiency of forest carbon estimation methods.