Spatiotemporal analysis of biomass carbon and forest dynamics through remote sensing data in Alemsaga Forest, Ethiopia
エチオピア・アレムサガ森林におけるリモートセンシングデータを用いたバイオマス炭素と森林動態の時空間分析 (AI 翻訳)
Agenagnew A. Gessesse, Anbaw Tigabu
🤖 gxceed AI 要約
日本語
本研究は、エチオピアのアレムサガ森林を対象に、1992年から2022年までの30年間の森林被覆変化と炭素蓄積量を定量化した。Landsat衛星画像と地上調査データを用いて、密林面積が35.34%増加し、総炭素蓄積量は374.54 t/ha（CO2換算で1374.76 t/ha）と推定された。森林が炭素吸収源として機能していることを示し、緑の遺産イニシアチブ（GLI）の有効性を裏付けるとともに、政策強化への示唆を提供する。
English
This study quantifies forest cover changes and carbon stocks in Alemsaga Forest, Ethiopia, over 30 years (1992-2022) using Landsat imagery and field data. Dense forest increased by 35.34%, and total carbon stock was estimated at 374.54 t/ha (equivalent to 1374.76 t/ha CO2). The forest acts as a dynamic carbon sink, validating reforestation efforts and informing national climate policy.
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、SSBJ開示やカーボンニュートラル目標に向けて、森林炭素吸収源の評価が重要視されている。本研究成果は、リモートセンシングと地上調査を組み合わせた炭素蓄積量の推定手法を提供し、日本の森林管理やカーボンクレジット制度の改善に示唆を与える。
In the global GX context
Globally, this study contributes to the growing body of evidence on forest carbon dynamics, supporting climate mitigation strategies under the Paris Agreement. It demonstrates a cost-effective approach for monitoring forest carbon stocks, which is relevant for REDD+ and national carbon accounting frameworks.
👥 読者別の含意
🔬研究者:Provides a methodological framework for combining remote sensing and field data to estimate forest carbon stocks, useful for similar studies in other regions.
🏢実務担当者:Offers insights for forest management and carbon credit projects, highlighting the importance of species-specific allometric models and community monitoring.
🏛政策担当者:Supports evidence-based policy for reforestation initiatives and national carbon registries, emphasizing the need for integrating remote sensing data.
📄 Abstract（原文）
Climate change poses a profound global threat, with forest ecosystems serving as essential carbon sinks that mitigate atmospheric CO₂ accumulation. Despite Ethiopia’s commitment to forest restoration under initiatives like the Green Legacy Initiative (GLI), quantitative data on long-term forest cover dynamics and carbon stocks in key areas such as Alemsaga Forest remain limited, hindering evidence-based conservation and climate policy. This study aimed to quantify forest cover changes in Alemsaga Forest over 30 years (1992–2022) and estimate its carbon stocks to inform national mitigation strategies. Forest cover was mapped using Landsat satellite imagery (1992, 2003, 2013, and 2022) via supervised classification in ERDAS Imagine. Ground-truth data were collected from 36 randomly selected 16 m × 16 m plots, measuring tree diameter at breast height (DBH) and height for 64 species. Aboveground biomass (AGB) and belowground biomass (BGB) were calculated using algometric equations; soil organic carbon (SOC) was analysed from 1 m × 1 m samples in 16 plots. Carbon stocks were summed across pools (AGB, BGB, deadwood, SOC). The results indicate a 35.34% increase in dense forest cover, which currently constitutes 48.25% of the total forest area (888.30 ha), indicating significant forest regeneration. Average carbon stocks were 191.72 ± 57.92 t/ha (AGB), 92.96 ± 75.74 t/ha (BGB), 2.61 ± 2.26 t/ha (deadwood), and 87.25 ± 51.72 t/ha (SOC), totaling 374.54 ± 187.64 t/ha and equivalent to 1374.76 t/ha CO₂. Alemsaga Forest acts as a dynamic carbon sink, validating the efficacy of ongoing reforestation efforts. Policymakers should strengthen GLI through species-specific allometric models, integrate remote sensing into national carbon registries, and establish community-led monitoring to sustain gains and support Ethiopia’s GLI.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s43621-026-03475-4first seen 2026-08-11 04:35:12
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