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Precision Estimation of Aboveground Carbon Stock in Acidosasa edulis Bamboo Forests: A Fusion Approach with UAV-LiDAR, Allometric Equations, and Machine Learning

UAV-LiDAR、アロメトリ式、機械学習を融合したアシドササ竹林の地上部炭素貯留量の精密推定 (AI 翻訳)

Xiaoyu Guo, Weisen Wang, Zhanghua Xu, Mingjing Li, Kele Yang, Yan Tan, Ze Shi, Haohao Yue, Junhua Zhang

Remote Sensing📚 査読済 / ジャーナル2026-05-04#AI×ESGOrigin: CN対象セクター: agriculture
DOI: 10.3390/rs18091431
原典: https://doi.org/10.3390/rs18091431

🤖 gxceed AI 要約

日本語

本研究は、UAV-LiDARと機械学習を組み合わせ、竹林の器官別地上部炭素（AGC）を高精度に推定する手法を開発した。XGBoostとRandom Forestモデルが最高精度（幹R2=0.82、葉R2=0.73）を示し、コスト効率の高い炭素推定を実現。炭素中立管理への示唆を提供する。

English

This study develops a UAV-LiDAR and machine learning framework for organ-level aboveground carbon (AGC) estimation in bamboo forests. XGBoost and Random Forest achieved highest accuracy (stem R2=0.82, leaf R2=0.73), offering a cost-effective solution for precise carbon estimation and insights for carbon neutrality management.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本では竹林の放置が問題となっており、炭素貯留機能の評価は温暖化対策や里山管理に有用。SSBJ開示における森林炭素算定の高度化にも寄与する可能性がある。

In the global GX context

Globally, bamboo forests are recognized for high carbon sequestration potential. This UAV-LiDAR-ML framework provides a scalable, cost-effective method for carbon accounting, relevant to climate mitigation strategies and nature-based solutions reporting.

👥 読者別の含意

🔬研究者:Provides a novel fusion approach for precise bamboo carbon estimation, useful for carbon cycle research and remote sensing methodology.

🏢実務担当者:Offers a cost-effective tool for bamboo forest carbon monitoring, aiding in carbon credit projects and sustainability reporting.

🏛政策担当者:Supports evidence-based policy for bamboo forest management and carbon neutrality targets.

📄 Abstract（原文）

As a fast-growing and multifunctional crop, bamboo plays a pivotal role in food security and climate change mitigation by leveraging its high carbon sequestration potential. Monitoring aboveground carbon (AGC) stock in bamboo forests is crucial for guiding field management, growth observation, and yield prediction. Unmanned aerial vehicle (UAV)-based point cloud sensors offer a rapid and scalable solution for measuring bamboo AGC. This study evaluates the potential of UAV-LiDAR and machine learning (ML) for organ-level AGC estimation in bamboo forests. From LiDAR point clouds, we extracted structural features—including height, density, canopy, and intensity metrics—aggregated by mean plot-level metric (Mean-PM) and maximum plot-level metric (Max-PM) values at a 1 m2 grid scale. Key predictors were selected using ML-based recursive feature elimination (ML-RFE) to develop organ-specific AGC inversion models. Results showed that organ-specific carbon content and allometric equations effectively eliminated biases associated with a uniform coefficient. Max-PM features outperformed Mean-PM features in stem and leaf AGCs, with the XGBoost and Random Forest models achieving the highest accuracy (R2 = 0.82 for stems, 0.73 for leaves). Height percentiles and canopy structural metrics emerged as dominant predictors. This UAV-LiDAR-ML framework provides a cost-effective solution for precise bamboo carbon estimation, offering critical insights for carbon neutrality management and informed decision-making in bamboo forest ecosystems.

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