Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data
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 po...
| Authors | Habib Pourdelan, Zhengkang Xiang, Hugh Stewart et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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