樹木単位のモニタリングから行動へ：森林炭素MRVと気候スマート森林管理のためのデジタル森林炭素ツイン枠組み
From tree-wise monitoring to action: a digital forest carbon twin framework for forest carbon MRV and climate-smart forest management (原題)
Evgeny Lopatin, Timo P. Pitkänen, Lauri Sikanen
🤖 gxceed AI 要約
日本語
本論文は、森林炭素のMRVを強化するデジタル森林炭素ツイン（DFCT）の概念枠組みを提案する。永続的な樹木単位の状態を核とし、炭素会計、不確実性伝播、検証証跡、意思決定支援を統合する6層アーキテクチャを定義。フィンランドのヨエンスー市でのパイロット実証を通じて、地上レーザーやUAVデータ等を統合した実装可能性を示す。
English
This paper proposes a conceptual framework for a Digital Forest Carbon Twin (DFCT) to enhance forest carbon MRV. It defines a six-layer architecture centered on persistent tree-wise state, integrating carbon accounting, uncertainty propagation, verification evidence, and decision support. A pilot in Joensuu, Finland demonstrates integration feasibility using ground laser scanning, UAV data, and web visualization.
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、森林吸収源のJ-クレジット制度やカーボンニュートラル目標の達成に向け、森林炭素の高精度なMRVが求められている。本枠組みは、樹木単位のモニタリングと検証可能な炭素会計を統合することで、日本の森林経営やクレジット創出の信頼性向上に寄与する可能性がある。
In the global GX context
Globally, forest carbon MRV is critical for climate mitigation and carbon markets. This framework addresses fragmentation and traceability gaps, aligning with international standards like IPCC guidelines and supporting verifier-ready systems. It offers a pathway for integrating remote sensing and field data into decision support, relevant for countries enhancing their forest carbon accounting.
👥 読者別の含意
🔬研究者:Provides a structured framework for developing digital twins for forest carbon MRV, highlighting key features like persistent tree identities and versioned state updating.
🏢実務担当者:Offers a blueprint for implementing forest carbon monitoring systems that can support carbon credit verification and climate-smart management decisions.
🏛政策担当者:Illustrates how digital infrastructure can enhance MRV credibility, potentially informing policy on forest carbon accounting and climate mitigation strategies.
📄 Abstract（原文）
Forests are increasingly expected to deliver climate mitigation together with productivity, resilience, and biodiversity outcomes, yet forest carbon monitoring, reporting, and verification (MRV) remain constrained by fragmented data workflows, weak traceability, and limited links between measurement and management action. This conceptual framework paper defines the Digital Forest Carbon Twin (DFCT) as a carbon-specialized digital twin whose minimum operational condition is a persistent tree-wise state that is repeatedly updated by observations and models. From this state, carbon accounting, uncertainty propagation, verification evidence, and decision support are generated through one versioned architecture. We propose a six-layer framework covering data acquisition, processing and integration, tree-wise state and carbon, MRV, decision support, and stakeholder/governance interfaces. The framework is operationalized through an auditor-facing verification workflow, a protocol-harmonization strategy, and a maturity pathway from pilot implementation to a scaled platform. An illustrative pilot in the City of Joensuu, Finland, demonstrates how ground laser scanning, UAV data, field measurements, tree-object reconstruction, and web visualization can be connected within one implementation pathway; the pilot is presented as evidence of integration feasibility rather than as a completed validation of carbon-accounting accuracy. Comparison with conventional inventories, remote-sensing workflows, MRV systems, generic digital twins, and decision-support systems shows that the defining DFCT features are persistent tree identities, versioned state updating, explicit uncertainty, reproducible lineage, and shared monitoring-to-action logic. The framework provides a practical research agenda for interoperable, verifier-ready, and climate-smart forest management systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.envc.2026.101634first seen 2026-09-01 05:14:25
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