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<p>Aboveground carbon (AGC) storage is a critical indicator for understanding how forest ecosystems contribute to climate change mitigation and sustainable management. This study applied two modelling approaches—the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) carbon model and the Normalized Difference Vegetation Index (NDVI)-based model—to estimate aboveground carbon storage in the forest ecosystems of Unye District, Ordu Province, Turkey. Spatial datasets derived from remote sensing and field-based inventories were integrated into both models to evaluate and compare their performances. The results indicated that the InVEST model provided a detailed spatial representation of carbon storage by incorporating land-use and land-cover dynamics, whereas the NDVI-based approach offered a simpler yet effective estimation based on vegetation greenness. Comparative analysis revealed significant variations in carbon storage among forest types and highlighted the methodological advantages and limitations of both techniques. This study provides a methodological framework for aboveground carbon assessment and offers practical insights for forest managers, policymakers, and researchers involved in carbon accounting and climate mitigation strategies.</p>

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Modelling aboveground carbon storage through INVEST and NDVI approaches: a case study in the forest ecosystems of Unye District, Ordu Province Northern Turkey

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Seyma Sengur,
  • Aslıhan Argan Sahın

摘要

Aboveground carbon (AGC) storage is a critical indicator for understanding how forest ecosystems contribute to climate change mitigation and sustainable management. This study applied two modelling approaches—the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) carbon model and the Normalized Difference Vegetation Index (NDVI)-based model—to estimate aboveground carbon storage in the forest ecosystems of Unye District, Ordu Province, Turkey. Spatial datasets derived from remote sensing and field-based inventories were integrated into both models to evaluate and compare their performances. The results indicated that the InVEST model provided a detailed spatial representation of carbon storage by incorporating land-use and land-cover dynamics, whereas the NDVI-based approach offered a simpler yet effective estimation based on vegetation greenness. Comparative analysis revealed significant variations in carbon storage among forest types and highlighted the methodological advantages and limitations of both techniques. This study provides a methodological framework for aboveground carbon assessment and offers practical insights for forest managers, policymakers, and researchers involved in carbon accounting and climate mitigation strategies.