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Assessment of Carbon Dynamics Using Remote Sensing, Machine Learning, and Cellular Automata in a Semi-Arid Region

半乾燥地域におけるリモートセンシング、機械学習、セルオートマトンを用いた炭素動態の評価 (AI 翻訳)

Vincenzo Barrile, Emanuela Genovese, Clemente Maesano, Davide Borrello, Fatma Ben Brahim

Applied Sciences📚 査読済 / ジャーナル2026-05-12#AI×ESGOrigin: Global対象セクター: agriculture
DOI: 10.3390/app16104801
原典: https://doi.org/10.3390/app16104801

🤖 gxceed AI 要約

日本語

本研究は、チュニジアのスファックス県を対象に、機械学習（ランダムフォレスト）とリモートセンシングを用いて土地利用・被覆変化を解析し、InVESTモデルで炭素貯留量と排出量を推定した。さらに、セルオートマトンで2030年の将来シナリオを予測し、炭素動態の時空間評価を提供する。

English

This study integrates machine learning, remote sensing, and ecosystem modeling to assess carbon dynamics in a semi-arid region of Tunisia. It maps land use/land cover, estimates carbon stocks and emissions using InVEST, and simulates future scenarios with cellular automata, providing insights for land management and climate mitigation.

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政策が進む中、土地利用変化に伴う炭素ストック評価は重要。本手法は、地域レベルでの炭素会計や気候変動適応策に応用可能であり、自治体や企業のScope 1排出削減計画に示唆を与える。

In the global GX context

Globally, this study contributes to spatially explicit carbon accounting and climate mitigation strategies, aligning with TCFD/ISSB disclosure requirements for land-related emissions. It demonstrates a replicable framework for semi-arid regions, supporting nature-based solutions and sustainable land management.

👥 読者別の含意

🔬研究者:Provides a methodological framework for integrating ML, remote sensing, and ecosystem models to assess carbon dynamics, useful for land-use and climate research.

🏢実務担当者:Offers a practical approach for companies and municipalities to monitor and project carbon stocks, aiding in sustainability reporting and land-use planning.

🏛政策担当者:Highlights the importance of land-use policies in carbon sequestration, informing climate mitigation strategies and land management regulations.

📄 Abstract（原文）

Soil Organic Matter (SOM) and Soil Organic Carbon (SOC) are essential for regulating ecosystem functions, soil fertility, and influencing climate change processes, especially in semi-arid regions. The recent improvements in remote sensing instruments and the development of artificial intelligence methodologies, such as machine learning, enable an improved understanding of carbon dynamics, facilitate the estimation of SOC content, and support predictive modeling. This study presents an integrated framework to analyze past and future carbon dynamics in the Sfax Governorate (Tunisia). Land-use and land-cover (LULC) maps for the years 2019, 2020, 2022, and 2024 were generated using a Random Forest algorithm applied to multispectral satellite data in the Google Earth Engine platform, achieving high classification accuracy (overall accuracy up to 0.90). Carbon stocks and their temporal variations were estimated using the InVEST Carbon Storage and Sequestration model, while carbon emissions and the Net Ecosystem Carbon Balance (NECB) were derived by integrating land-use-specific emission factors. Future LULC scenarios for 2030 were simulated through a Cellular Automata model under three alternative development pathways: conservation-oriented (CONS), business-as-usual (BAU), and urban expansion (URB+). The study demonstrates how the integration of machine learning, remote sensing, and ecosystem modeling supports spatially explicit assessment of SOC-related carbon dynamics and provides useful insights for land management and climate mitigation strategies.

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