Quantifying Carbon Sequestration Potential in Rwanda’s Green Village Agroforestry Systems, Nyagatare District, Rwanda
Peruth Munezero, Isaac Olajide Areo, Theodore Rusesabahizi
🤖 gxceed AI 要約
日本語
本論文は、ルワンダのアグロフォレストリーシステムにおける炭素隔離ポテンシャルを評価。NDVIリモートセンシングとIPCCガイドラインを用い、2004年から2024年にかけて植生が大幅に減少し、約109万トンの炭素損失を推定。樹木密度や土地管理が炭素蓄積に影響することを示す。
English
This study evaluates carbon sequestration potential of agroforestry systems in Rwanda using NDVI remote sensing and IPCC Tier 1 guidelines. It estimates a net loss of 1.09 million t C from 2004-2024 due to vegetation decline, highlighting that tree density and management practices significantly influence carbon stocks.
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
日本とは直接関係ないが、アグロフォレストリーによる炭素隔離の定量化手法は、日本の森林炭素吸収源評価にも参考になる。
In the global GX context
This study provides empirical evidence on carbon sequestration in agroforestry systems using remote sensing and IPCC guidelines, relevant for global land-use carbon accounting, though not directly tied to corporate disclosure frameworks.
👥 読者別の含意
🔬研究者:Researchers can learn from the mixed-methods approach combining NDVI and field data for carbon stock estimation.
🏢実務担当者:Practitioners in land management can use the findings to improve agroforestry system management for carbon sequestration.
🏛政策担当者:Policymakers can consider integrating NDVI-based monitoring to track vegetation and carbon stocks in national climate plans.
📄 Abstract（原文）
This study evaluates the carbon sequestration potential of agroforestry systems in Rwanda’s Green Village Model within Nyagatare District, addressing gaps in empirical evidence on local-level climate mitigation. Using a mixed-methods approach, Landsat (2004, 2014) and Sentinel (2024) imagery were analysed through NDVI to assess vegetation dynamics, while sector-level data on tree density, species composition, land area, plot numbers, and household adoption were used to estimate above-ground biomass and carbon stocks following IPCC Tier 1 guidelines. Results show a sharp decline in healthy vegetation from 17% in 2004 to 1% in 2024, with bare land and grassland expanding to 84% of the landscape, leading to a net loss of approximately 1.09 million t C (4.0 million t CO₂ eq). Regression analysis indicates that tree density, species selection, agroforestry land area, plot numbers, and adoption rates significantly influence carbon stocks, with Karangazi Sector storing more carbon due to higher tree density and adoption. The findings demonstrate that while agroforestry systems possess substantial mitigation potential, their effectiveness is constrained by weak management, drought, and land-use pressures. Strengthening system management, scaling climate-resilient species, and institutionalising NDVI-based monitoring are recommended to restore vegetation cover and maximise carbon sequestration.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.37284/eajfa.9.1.4964first seen 2026-06-02 04:40:01 · last seen 2026-06-12 04:39:20
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