Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation
arXiv:2607.05260v1 Announce Type: cross Abstract: Conventional modeling approaches for LiDAR-based above-ground biomass (AGB) estimation rely on discrete plot-level inventory aggregates. This methodology introduces boundary-effect uncertainties that may severely degrade model performance within small field plots. To solve this limitation, we evaluate a Horizontal Biomass Distribution (HBD) reference mapped continuously from Quantitative Structure Models (QSMs). We trained a sparse 3D U-Net on simulated broadleaved forest structures using three AGB reference types: a standard forest inventory (
The continuous improvement in LiDAR technology and computational methods allows for more granular and accurate biomass estimation, which is critical for climate and environmental modeling.
Improved biomass estimation provides more precise data for carbon accounting, environmental policy, and sustainable resource management, impacting sectors reliant on ecological accuracy.
The shift from discrete to continuous reference data for biomass estimation enhances model performance and reduces boundary-effect uncertainties in ecological assessments.
- · Environmental monitoring agencies
- · Climate science researchers
- · Forestry management
- · Remote sensing companies
- · Traditional plot-based inventory methods
More accurate global carbon sequestration data becomes available.
Policy decisions related to climate change and land use gain a stronger data foundation.
New markets emerge for high-precision environmental data services and AI-driven ecological modeling.
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Read at arXiv cs.AI