A Multi-Sensor Machine Learning Framework Integrating UAV Multispectral Imagery and LiDAR Data for Living Biomass Carbon Stock Estimation in Silviculturally Treated Forests
UAVマルチスペクトル画像とLiDARデータを統合したマルチセンサー機械学習フレームワークによる施業処理森林の現存量炭素ストック推定 (AI 翻訳)
Nyo Me Htun, Toshiaki Owari, Satoshi Suzuki, Songqiu Deng, Tetsuyuki Kobayashi, Sakura Asato, Akio Oshima, Mutsuki Hirama, Koichi Takahashi, Yasuo Isozaki, T. Okahira, Satoshi Kita, Ryota Konda, Manato Fushimi
🤖 gxceed AI 要約
日本語
北海道の施業処理森林を対象に、UAVマルチスペクトル画像とLiDARデータを統合した機械学習フレームワークを開発し、現存量炭素ストックを高精度に推定した。XGBoostが最高性能（R2=0.88）を示し、マルチスペクトルとLiDARの相補的役割を実証した。
English
This study develops a multi-sensor machine learning framework integrating UAV multispectral imagery and LiDAR data to estimate living biomass carbon stocks in managed forests in Hokkaido, Japan. XGBoost achieved the highest accuracy (R2=0.88), demonstrating the complementary roles of spectral and structural remote sensing for carbon mapping.
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
日本の森林炭素吸収源の算定やJ-クレジット制度における炭素ストック推定の高度化に寄与する。SSBJ開示における森林関連の炭素計測ニーズにも応える可能性がある。
In the global GX context
This work contributes to global carbon accounting and climate disclosure by providing a scalable method for forest carbon stock estimation, relevant to TCFD/ISSB reporting and nature-based solutions. It offers empirical evidence from Japanese forests, adding to international remote sensing scholarship.
👥 読者別の含意
🔬研究者:森林炭素計測におけるマルチセンサー統合とML手法の有効性を示す実証的知見を提供。
🏢実務担当者:森林管理やカーボンクレジット事業における炭素ストック推定の高精度化に活用可能。
🏛政策担当者:森林炭素モニタリングの政策立案やJ-クレジット制度の精度向上に示唆を与える。
📄 Abstract（原文）
The accurate and scalable estimation of carbon stocks in living biomass remains challenging in structurally heterogeneous forests subjected to different silvicultural treatments. This study presents a multi-sensor machine learning framework that integrates unmanned aerial vehicle (UAV)-derived multispectral imagery with UAV- and airborne light detection and ranging (LiDAR) data for spatially explicit carbon stock estimation in managed forests of central and eastern Hokkaido, northern Japan. Field measurements from 38 plots were used for model development and validation. Spectral features derived from UAV multispectral imagery and structural metrics derived from UAV and airborne LiDAR data were integrated within a multi-sensor framework and evaluated using Multiple Linear Regression (MLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), with MLR serving as a baseline model. A key objective was to quantify the relative contributions of spectral and structural sensing information for carbon stock estimation in silviculturally treated forests through the systematic comparison of canopy height model (CHM)-only, RGB + CHM, and multispectral + CHM datasets. The machine learning models consistently outperformed the baseline MLR model, with XGBoost generally outperforming RF and achieving a maximum validation R2 of 0.88 and root mean squared error (RMSE) of 27.41 Mg C ha−1. Although the improvement in plot-level prediction accuracy over the CHM-only configuration was modest, integrating multispectral imagery with LiDAR-derived structural metrics reduced prediction errors and systematic bias in wall-to-wall carbon stock mapping. These findings highlight the complementary roles of structural and spectral remote sensing information for spatially explicit carbon stock estimation in silviculturally treated forests.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/s26144496first seen 2026-08-05 04:57:57
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