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<p>Forest landscapes play a significant role in both global and local carbon cycles, mitigating climate change by sequestering atmospheric carbon. To maintain carbon stock and enhance sequestration from the atmosphere, it is important to quantify the effects of driving factors on carbon stock. Therefore, this study was designed to evaluate the effects of storing factors, maintaining factors, and disturbing factors on carbon stock, and to analyze the individual and combined effects of multiple factors. All-subsets regression and hierarchical partitioning methods were used to analyze the driving factors of carbon stock. A total of nine subset regression models were developed with different combinations of driving factors. The combination of five driving factors, such as tree density, tree diameter at breast height (DBH), canopy cover, Terrain Ruggedness Index, and distance from agricultural land gave the optimum model and explained almost 79% (R<sup>2</sup> = 0.791) of the variability in carbon stock. Storing factors contributed a total of 68.637% to the optimum model, followed by maintaining factors (8.314%) and disturbing factors (23.049%). However, canopy cover was the most dominant factor, while tree density and diversity were essential factors that exerted an influence on tree DBH, tree height, tree basal diameter (BD), and canopy cover. Furthermore, regular monitoring and management of forests near built-up and agricultural areas are required to improve carbon stock. These findings may aid in developing scientific management strategies, and methodological setup can be used in other geographical regions.</p>

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Evaluating the Impact of Diverse Driving Factors on Carbon Stock Variability in Tropical Dry Deciduous Forests of West Bengal, India

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Dipankar Bera,
  • Nilanjana Das Chatterjee,
  • Santanu Dinda,
  • Subrata Ghosh,
  • Sudip Bera,
  • Mrinmay Mandal,
  • Vivek Dhiman,
  • Akash Kashyap,
  • Mohamed Zhran

摘要

Forest landscapes play a significant role in both global and local carbon cycles, mitigating climate change by sequestering atmospheric carbon. To maintain carbon stock and enhance sequestration from the atmosphere, it is important to quantify the effects of driving factors on carbon stock. Therefore, this study was designed to evaluate the effects of storing factors, maintaining factors, and disturbing factors on carbon stock, and to analyze the individual and combined effects of multiple factors. All-subsets regression and hierarchical partitioning methods were used to analyze the driving factors of carbon stock. A total of nine subset regression models were developed with different combinations of driving factors. The combination of five driving factors, such as tree density, tree diameter at breast height (DBH), canopy cover, Terrain Ruggedness Index, and distance from agricultural land gave the optimum model and explained almost 79% (R2 = 0.791) of the variability in carbon stock. Storing factors contributed a total of 68.637% to the optimum model, followed by maintaining factors (8.314%) and disturbing factors (23.049%). However, canopy cover was the most dominant factor, while tree density and diversity were essential factors that exerted an influence on tree DBH, tree height, tree basal diameter (BD), and canopy cover. Furthermore, regular monitoring and management of forests near built-up and agricultural areas are required to improve carbon stock. These findings may aid in developing scientific management strategies, and methodological setup can be used in other geographical regions.