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LULUCF規則を支える地理空間データセット（ETC DIレポート2026/7）

Geospatial datasets in support of the LULUCF regulation (ETC DI Report 2026/7) (原題)

Kleeschulte, Stefan, Peters, Andrea, Schmid, Carmen, Maucha, Gergely, Weiss, Michael, Moldaschl, Erwin, Ricci, Maria, Cattaneo, Marco, Frost, Alexander

Zenodoプレプリント2026-10-30#炭素会計Origin: EU
DOI: 10.5281/zenodo.23693175
原典: https://zenodo.org/records/23693175
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🤖 gxceed AI 要約

日本語

本レポートは、EUのLULUCF規則における「居住地」区分のバイオマス炭素蓄積・GHGフラックス推計のため、Copernicus Land Monitoring Service（CLMS）データセットの適合性を評価する。2006年IPCCガイドラインと2019年改良版の枠組みに沿い、加盟国がインベントリ手法をTier 2以上へ引き上げることを支援する。居住地域の輪郭 delineation と樹冠被覆の定量に最適なCLMSデータ組み合わせを国別に特定し、NIRの公式面積やLiu et al.(2023)の独立推計と比較した。

English

This report assesses the suitability of Copernicus Land Monitoring Service (CLMS) datasets for quantifying urban vegetation across the EU, supporting biomass carbon stock and GHG flux estimation under the LULUCF settlements sub-categories. Following the 2006 IPCC Guidelines and 2019 Refinement, it helps Member States upgrade LULUCF inventories to at least Tier 2. It identifies country-specific optimal CLMS dataset combinations for settlement delineation and crown cover, benchmarking against National Inventory Report areas and independent estimates.

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SSBJ・有報開示とは直接の接続は薄いが、土地利用変化に伴う炭素蓄積の計測手法やEOデータ活用は、国内の森林・土地利用系インベントリ高度化やScope 3土地利用変化算定の参考になる。

In the global GX context

Directly supports EU Governance Regulation (2018/1999) and LULUCF reporting obligations, showing how harmonised Earth observation data can raise inventory tiers—relevant to global disclosure scholarship on land-sector carbon accounting and ISSB/CSRD land-use metrics.

👥 読者別の含意

🔬研究者:EOデータとIPCC枠組みを組み合わせた土地利用炭素推計の実証的手法を学べる。

🏢実務担当者:土地利用変化（LUC）由来の炭素・Scope 3算定に関わる企業のデータ選定に示唆を与える。

🏛政策担当者:加盟国のLULUCFインベントリ高度化に向けたEOデータ活用と国別推奨の実務的知見を提供する。

📄 Abstract（原文）

This report assesses the suitability of Copernicus Land Monitoring Service (CLMS) datasets for quantifying urban vegetation across the European Union, in support of biomass carbon stock and GHG fluxes estimation under the LULUCF sub-categories s ettlements remaining settlements and land converted to settlements. It applies the methodological framework of the 2006 IPCC Guidelines and the 2019 Refinement and responds to the reporting requirements of the Governance Regulation (EU) 2018/1999, supporting Member States in upgrading their LULUCF GHG inventory methods to at least Tier 2. As its main output, the analysis identifies the most suitable combinations of CLMS datasets for delineating settlement area outlines and quantifying crown cover for each Member State. Settlement area figures derived from CLMS data are benchmarked against official areas reported in the National Inventory Reports (NIRs). The comparison reveals differences in dataset performance across Member States and shows that no single combination is optimal for all countries. Crown cover area figures, largely unreported in Member States’ LULUCF submissions, are compared with independent estimates from Liu et al. (2023). The report provides country-specific recommendations for quantifying urban vegetation using harmonised, openly available Earth observation (EO) data. The results of the analysis are available on the Eionet portal (and attached to this page): ·         Summary statistics of settlement area by country , and selection of the best-fitting CLMS data combination; ·         Share of urban tree crown cover by country using various CLMS datasets; ·         Comparison of CLMS-based urban tree crown cover estimates with Liu et al. (2023) . This report was developed together with the ETC CM Guidance for estimating GHG emissions and removals of biomass in Settlements , which focuses on the policy aspects and implementation of the LULUCF requirements, with recommendations for inventory compilers less familiar with EO and CLMS data. This report is its technical complement, providing an in-depth geospatial analysis of woody vegetation in settlements, with detailed statistical analysis and practical examples.

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