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Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data

Habib Pourdelan, Zhengkang Xiang, Hugh Stewart, Cam Nicholson, Martin Tomko, Kourosh Khoshelham

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.

Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data

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.
Paper Structure (28 sections, 9 equations, 8 figures, 8 tables)

This paper contains 28 sections, 9 equations, 8 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Materials and Methods
  4. Study area
  5. Synthetic data generation
  6. Creating 3D synthetic forest plot
  7. Volume calculation of each plot
  8. Converting 3D plots to point cloud using laser scanning simulation
  9. Data preparation
  10. Deep learning models for regression
  11. PointNet
  12. PointNet++
  13. DGCNN
  14. PointConv
  15. Model training and evaluation
  16. ...and 13 more sections

Figures (8)

  • Figure 1: Schematic comparison of conventional indirect AGB estimation (segmentation + allometry + aggregation) versus the proposed direct plot-level regression approach.
  • Figure 2: Overview of the proposed methodolog
  • Figure 3: Lidar data and field measurement of AGB were collected in two study areas, Knewleave and Jigsaw Farms, both located in Victoria, Australia.
  • Figure 4: Synthetic data generation process. (a) 3D eucalyptus tree model, (b) Synthetic forest plot built in Blender, (c) Simulated point cloud.
  • Figure 5: Learning curves of the deep learning models trained on synthetic point cloud data downsampled using the farthest point sampling method: (a) PointNet, (b) PointNet++, (c) DGCNN, and (d) PointConv.
  • ...and 3 more figures