Carbon Sequestration Assessment in Napier Grass Plantations Using UAV-Based Imagery and Blockchain-Enabled Carbon Trading
UAV画像とブロックチェーンを活用したネピアグラス植林地の炭素隔離評価 (AI 翻訳)
Varot Soonthornnont, Supachate Innet
🤖 gxceed AI 要約
日本語
本研究は、UAV画像とブロックチェーン台帳を組み合わせ、エネルギー用グラス(ネピアグラス)の炭素隔離量を推定・記録する概念実証を提示。タイの5ライの試験地で4ヶ月間のデータを用い、アロメトリ回帰モデルにより地上部・地下部の炭素量を推定し、実験室測定値と2〜9%の誤差で一致。ブロックチェーンにより改ざん防止の証明を提供。単一栽培品種・サイト・季節のため、一般化には限界があるが、炭素クレジット市場向けの低コスト計測手法として有望。
English
This study presents a proof-of-concept workflow combining UAV imagery and blockchain ledger to estimate and record carbon sequestration in Napier grass plantations. Using monthly orthomosaics and allometric models calibrated with bomb calorimetry, carbon mass estimates agreed within 2-9% MAPE. Blockchain provides tamper-evident provenance. Limited to a single cultivar, site, and season, but offers a low-cost measurement approach for carbon credit markets.
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、J-クレジット制度やブルーカーボンなど、バイオマス由来の炭素隔離の計測手法が注目されている。本手法は、UAVとブロックチェーンを組み合わせることで、計測コストと信頼性の課題を解決する可能性があり、日本の地域バイオマス活用やカーボンクレジット創出に示唆を与える。ただし、日本の気候や栽培条件への適用には追加検証が必要。
In the global GX context
Globally, voluntary carbon markets demand frequent and reliable carbon quantification. This study demonstrates a scalable, low-cost method using UAV and blockchain, which could enhance transparency and trust in carbon credit issuance. It aligns with ISSB and CSRD disclosure trends by providing verifiable data. However, the single-site, short-term nature limits generalizability, necessitating further validation across diverse conditions.
👥 読者別の含意
🔬研究者:Provides a novel integration of UAV remote sensing and blockchain for carbon accounting, with methodological insights on allometric model stability.
🏢実務担当者:Offers a potential low-cost, verifiable approach for monitoring carbon sequestration in energy crops, useful for carbon credit projects.
🏛政策担当者:Highlights the need for standardized protocols and validation frameworks for technology-based carbon measurement to ensure market integrity.
📄 Abstract(原文)
Quantifying carbon sequestration in energy-grass plantations at the frequency demanded by voluntary carbon markets remains challenging, since destructive sampling and bomb calorimetry are analytically reliable but labour-intensive. This paper presents a pilot-scale workflow that pairs UAV imagery and image processing with a blockchain ledger to estimate and record above- and below-ground carbon mass in Pak Chong 1 Napier grass (Pennisetum purpureum × P. americanum) over a single four-month growing season at a 5-rai site in Pong Daeng, Nakhon Ratchasima, Thailand. Monthly UAV orthomosaics were segmented into per-hill tiles, from which canopy area and height were extracted and used as inputs to allometric regression models calibrated against laboratory bomb-calorimetry data (BS EN 14918:2009; ASTM D7582 ); the resulting carbon-mass estimates were committed to an Ethereum-compatible test ledger to provide tamper-evident provenance. Predicted carbon mass agreed with laboratory measurements within 2–9 % mean absolute percentage error. The canopy-area-to-biomass and stem carbon-fraction regressions were comparatively stable (R = 0.914 and R = 0.816 respectively), whereas a second-order polynomial model for root carbon fraction (R = 0.9015) proved unstable under leave-one-out cross-validation and bootstrap resampling, a consequence of the small calibration set (n = 4 monthly time-points) that is discussed as a primary limitation. Total above- and below-ground carbon mass at month 4, expressed as CO₂-equivalent using a standard stoichiometric conversion, reached 0.441 × 10⁻³ t CO₂-eq. Because the study draws on a single cultivar, site and season, these findings are presented as a proof-of-concept demonstration of UAV-based estimation combined with blockchain-anchored record-keeping, rather than as a generalised or market-ready carbon-accounting method.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.envc.2026.101600first seen 2026-08-07 04:58:45
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。