From boreal forests to boreal cities—assessing the applicability of a forest growth and carbon balance model to tree-covered urban green spaces
北方林から北方都市へ―森林成長・炭素収支モデルの樹木被覆都市緑地への適用可能性評価 (AI 翻訳)
Esko Karvinen, Francesco Minunno, Eero Ahokas, Matti Hyyppä, Leif Backman, Olli Nevalainen, Hannakaisa Lindqvist, Veera Vasenkari, Liisa Kulmala
🤖 gxceed AI 要約
日本語
本研究では、フィンランド南部の都市緑地を対象に、非都市部の森林成長・炭素収支モデルPREBASOの適用可能性を検証した。航空レーザ測量データによる樹冠構成の推定や土壌条件の不確実性を考慮し、モデルが炭素吸収のフェノロジーを捉えることを確認したが、ピーク葉面積指数の過小評価や樹高成長の過大評価などの課題も明らかになった。若い林分ほど将来の炭素吸収増加が期待されることから、都市緑地の炭素管理への有用性を示した。
English
This study evaluated the applicability of the PREBASO forest growth and carbon balance model to tree-covered urban green spaces in southern Finland. Using airborne laser scanning for tree stand composition and considering soil uncertainty, the model captured phenology of carbon sequestration but underestimated peak leaf area index and overestimated height growth. Younger stands showed higher future sequestration, demonstrating promise for urban carbon 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
日本の都市緑地政策（例：グリーンインフラ、カーボンニュートラルシティ）において、炭素吸収量の定量的評価は重要である。本モデルは気候変動下での都市樹木の成長予測に活用でき、SSBJや自治体の排出量算定に資する可能性がある。
In the global GX context
This work provides a modeling framework for urban carbon sequestration that can inform city-level climate action plans globally. While focused on boreal conditions, the methodology—integrating remote sensing and process-based modeling—is transferable to other regions, supporting ISSB-aligned reporting on nature-based solutions.
👥 読者別の含意
🔬研究者:Offers a validated model for urban forest carbon dynamics under climate change, with insights on data limitations and parameter sensitivity.
🏢実務担当者:Provides a tool to estimate and project carbon sequestration in urban green spaces, aiding municipal climate planning and green infrastructure management.
🏛政策担当者:Demonstrates the potential of urban forests as carbon sinks, supporting policy for nature-based climate solutions and urban sustainability targets.
📄 Abstract（原文）
Abstract Urban environments expand in both population and area, stressing the need for means to study and understand urban green spaces (UGS) in a comprehensive manner. Especially tree-covered UGS are central to biogenic carbon (C) sequestration in cities, as well as for the many other ecosystem services they provide. However, many modelling applications used to study them have certain limitations, for instance related to inaccurate representation of tree stand development over time or considering the effects of changing climate. In this study, we examined the applicability of a non-urban forest growth and C balance model PREBASSO to simulating tree-covered UGS at a set of study sites in Southern Finland. We also investigated both the potential of acquiring tree stand composition from airborne laser scanning data and the role of uncertainty in soil conditions in the model results. In doing so, we aimed to address some data limitations typical for urban areas and provide new tools for studying the development of C sequestration of tree-covered UGS in the future. Our results showed that the modelling approach effectively captured the phenology of C sequestration when evaluated against satellite-observed leaf area index time series. However, the model tended to underestimate peak leaf area index and frequently overestimate annual tree height growth. C sequestration was particularly sensitive to the assigned forest site type and to assumptions related to soil C stock and water holding properties. The simulations further indicated that younger forest stands may exhibit higher increase in C sequestration in future conditions. Together, these findings both demonstrated the promise of the assessed model in representing tree-covered UGS and highlighted several avenues for methodological improvement.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1088/2515-7620/ae7253first seen 2026-06-11 04:57:47
🔔 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。