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

TL;DR

This paper introduces a novel deep learning method trained on synthetic lidar data for direct estimation of forest biomass and volume, outperforming traditional allometric approaches in accuracy and scalability.

Contribution

It presents a synthetic data-driven deep regression approach using PointNet architectures for direct forest biomass estimation, reducing reliance on allometric models.

Findings

Deep networks achieved 1.69% to 8.11% MAPE on synthetic data.

Applied to real data, discrepancies ranged from 2% to 20%.

Outperformed traditional methods with 27% to 85% underestimation in allometric approaches.

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.