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Carbon stock serves as a crucial metric for assessing the quantity of carbon stored within terrestrial and aquatic ecosystems, exerting significant influence on global carbon dynamics and climate change mitigation efforts. Effective management of carbon stocks is vital for regulating atmospheric carbon dioxide (CO \(_2\) ) levels and mitigating the adverse impacts of climate change. The study delves into the estimation of carbon stocks, particularly focusing on above-ground biomass (AGB) as a key component of carbon storage in forests. In addition, explores methods for estimating above-ground biomass (AGB) of carbon storage in forests. Traditional field-based approaches, statistical methods like regression, and machine learning techniques such as deep learning offer varied strategies for AGB estimation. These methods leverage a variety of data to enhance accuracy and scalability. Through empirical examples, the study presents their effectiveness in informing conservation strategies and fostering sustainable development amidst environmental challenges.

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Estimating Forest Carbon Stocks: A Review of Above-Ground Biomass Methods

  • 会议论文

  • 发表日期：

    2025年

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  • John Khoo,
  • Rayner Alfred,
  • Khalifa Chekima,
  • Rayner Pailus,
  • Chin Kim On,
  • Ervin Gubin Moung,
  • Raymond Alfred,
  • Oliver Valentine Eboy,
  • Normah Awang Besar Raffie,
  • Ashraf Osman Ibrahim,
  • Nosius Luaran

摘要

Carbon stock serves as a crucial metric for assessing the quantity of carbon stored within terrestrial and aquatic ecosystems, exerting significant influence on global carbon dynamics and climate change mitigation efforts. Effective management of carbon stocks is vital for regulating atmospheric carbon dioxide (CO \(_2\) ) levels and mitigating the adverse impacts of climate change. The study delves into the estimation of carbon stocks, particularly focusing on above-ground biomass (AGB) as a key component of carbon storage in forests. In addition, explores methods for estimating above-ground biomass (AGB) of carbon storage in forests. Traditional field-based approaches, statistical methods like regression, and machine learning techniques such as deep learning offer varied strategies for AGB estimation. These methods leverage a variety of data to enhance accuracy and scalability. Through empirical examples, the study presents their effectiveness in informing conservation strategies and fostering sustainable development amidst environmental challenges.