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<p>Accurate estimation of individual tree aboveground biomass (AGB) is critical for improving our understanding of forest structure and function, especially in forests with diverse tree species spanning a wide range of sizes. Terrestrial laser scanning (TLS) has proven effective at the tree scale thanks to its ability to provide high-density point cloud data. However, most existing methods rely on high-quality multi-scan TLS data, which requires labor-intensive data collection and complicated preprocessing to achieve high precision, thereby limiting their practical applicability. In contrast, single-scan TLS data offers a simpler and more efficient alternative, though it presents significant challenges due to incomplete coverage, uneven point density, and frequent occlusions. This study presents a novel and effective framework for estimating individual tree AGB from single-scan TLS data by integrating a deep learning network (CoAtNet) with three key innovations: an optimized 3D-to-2D projection strategy, a 3D point cloud-based data augmentation, and an ensemble approach to enhance robustness under data-scarce conditions in local natural forest environments. The framework was evaluated in a mixed hardwood forest and demonstrated strong predictive performance with a coefficient of determination (R2) of 0.73, a median percentage error of 0.99%, and a median absolute percentage error of 25.06%, outperforming two comparison models, Random Forest and Point Transformer. The results indicate the potential of single-scan TLS data to support reliable individual tree biomass when combined with deep learning, even in the presence of data imperfections. The proposed framework provides a practical, scalable approach that contributes to the advancement of remote sensing methodologies for sustainable forest management and facilitates improved monitoring of ecological changes in the context of accelerating climate change.</p>

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Individual Tree Biomass Estimation using Single-Scan Terrestrial Laser Scanner with Efficient Projection-Based Deep Learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Minyoung Jung,
  • Jaewan Choi,
  • Joshua Carpenter,
  • Songlin Fei,
  • Jinha Jung

摘要

Accurate estimation of individual tree aboveground biomass (AGB) is critical for improving our understanding of forest structure and function, especially in forests with diverse tree species spanning a wide range of sizes. Terrestrial laser scanning (TLS) has proven effective at the tree scale thanks to its ability to provide high-density point cloud data. However, most existing methods rely on high-quality multi-scan TLS data, which requires labor-intensive data collection and complicated preprocessing to achieve high precision, thereby limiting their practical applicability. In contrast, single-scan TLS data offers a simpler and more efficient alternative, though it presents significant challenges due to incomplete coverage, uneven point density, and frequent occlusions. This study presents a novel and effective framework for estimating individual tree AGB from single-scan TLS data by integrating a deep learning network (CoAtNet) with three key innovations: an optimized 3D-to-2D projection strategy, a 3D point cloud-based data augmentation, and an ensemble approach to enhance robustness under data-scarce conditions in local natural forest environments. The framework was evaluated in a mixed hardwood forest and demonstrated strong predictive performance with a coefficient of determination (R2) of 0.73, a median percentage error of 0.99%, and a median absolute percentage error of 25.06%, outperforming two comparison models, Random Forest and Point Transformer. The results indicate the potential of single-scan TLS data to support reliable individual tree biomass when combined with deep learning, even in the presence of data imperfections. The proposed framework provides a practical, scalable approach that contributes to the advancement of remote sensing methodologies for sustainable forest management and facilitates improved monitoring of ecological changes in the context of accelerating climate change.