Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India
Understanding and quantifying above-ground carbon stock is critical for assessing the impact of land use choices on carbon emissions which can inform conservation and management strategies to protect and increase carbon stocks. This study introduces a novel methodology for evaluating above-ground ca...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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IOP Publishing
2024
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| Acceso en línea: | https://hdl.handle.net/10568/169133 |
| _version_ | 1855516518697140224 |
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| author | Guo, Zhe Sharma, Himani Jadav, Mahesh Hettiarachchi, Upeksha Guha, Chiranjit Zhang, Wei Priyadarshini, Pratiti Meinzen-Dick, Ruth S. |
| author_browse | Guha, Chiranjit Guo, Zhe Hettiarachchi, Upeksha Jadav, Mahesh Meinzen-Dick, Ruth S. Priyadarshini, Pratiti Sharma, Himani Zhang, Wei |
| author_facet | Guo, Zhe Sharma, Himani Jadav, Mahesh Hettiarachchi, Upeksha Guha, Chiranjit Zhang, Wei Priyadarshini, Pratiti Meinzen-Dick, Ruth S. |
| author_sort | Guo, Zhe |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Understanding and quantifying above-ground carbon stock is critical for assessing the impact of land use choices on carbon emissions which can inform conservation and management strategies to protect and increase carbon stocks. This study introduces a novel methodology for evaluating above-ground carbon storage and sequestration in the Thoria watershed, India, using time-series open-access remotely sensed datasets and the InVEST (Integrated Valuation of Environmental Services and Tradeoffs) carbon model. Our spatially explicit analysis examines land cover and land use changes over the past 20 years. Using high-resolution NDVI (Normalized Difference Vegetation Index) data from Sentinel satellites, we disaggregate land cover types into high and low NDVI classes, which allows enhanced assessment of carbon stocks by capturing the spatial variation within the same land cover types. To assess the potential impacts of land cover changes on carbon stock, we generated two future scenarios suggested by local experts: a 20% expansion of cropland and a 20% expansion of wooded land. Using a proximity-based approach, we create these future land use maps and estimate the corresponding carbon stock with the InVEST carbon model. We demonstrate the utility of the methodology in informing land use decisions through spatially explicitly assessing how carbon stock changes in response to cropland expansion and wooded land growth. Our findings indicate that while urban development contributes to carbon losses, increasing wooded land and tree cover helps mitigate these losses, highlighting the importance of afforestation in maintaining ecological balance and reducing environmental impact. |
| format | Journal Article |
| id | CGSpace169133 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | IOP Publishing |
| publisherStr | IOP Publishing |
| record_format | dspace |
| spelling | CGSpace1691332025-10-26T12:52:25Z Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India Guo, Zhe Sharma, Himani Jadav, Mahesh Hettiarachchi, Upeksha Guha, Chiranjit Zhang, Wei Priyadarshini, Pratiti Meinzen-Dick, Ruth S. modelling carbon stock assessments watersheds spatial analysis Understanding and quantifying above-ground carbon stock is critical for assessing the impact of land use choices on carbon emissions which can inform conservation and management strategies to protect and increase carbon stocks. This study introduces a novel methodology for evaluating above-ground carbon storage and sequestration in the Thoria watershed, India, using time-series open-access remotely sensed datasets and the InVEST (Integrated Valuation of Environmental Services and Tradeoffs) carbon model. Our spatially explicit analysis examines land cover and land use changes over the past 20 years. Using high-resolution NDVI (Normalized Difference Vegetation Index) data from Sentinel satellites, we disaggregate land cover types into high and low NDVI classes, which allows enhanced assessment of carbon stocks by capturing the spatial variation within the same land cover types. To assess the potential impacts of land cover changes on carbon stock, we generated two future scenarios suggested by local experts: a 20% expansion of cropland and a 20% expansion of wooded land. Using a proximity-based approach, we create these future land use maps and estimate the corresponding carbon stock with the InVEST carbon model. We demonstrate the utility of the methodology in informing land use decisions through spatially explicitly assessing how carbon stock changes in response to cropland expansion and wooded land growth. Our findings indicate that while urban development contributes to carbon losses, increasing wooded land and tree cover helps mitigate these losses, highlighting the importance of afforestation in maintaining ecological balance and reducing environmental impact. 2024-11-01 2025-01-15T17:24:17Z 2025-01-15T17:24:17Z Journal Article https://hdl.handle.net/10568/169133 en https://hdl.handle.net/10568/138818 https://doi.org/10.1109/Agro-Geoinformatics55649.2022.9858976 Open Access IOP Publishing Guo, Zhe; Sharma, Himani; Jadav, Mahesh; Hettiarachchi, Upeksha; Guha, Chiranjit; et al. 2024. Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India. Environmental Research Communications 6(11): 115036. https://doi.org/10.1088/2515-7620/ad95e7 |
| spellingShingle | modelling carbon stock assessments watersheds spatial analysis Guo, Zhe Sharma, Himani Jadav, Mahesh Hettiarachchi, Upeksha Guha, Chiranjit Zhang, Wei Priyadarshini, Pratiti Meinzen-Dick, Ruth S. Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India |
| title | Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India |
| title_full | Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India |
| title_fullStr | Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India |
| title_full_unstemmed | Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India |
| title_short | Measuring above-ground carbon stock using spatial analysis and the InVEST model: Application in the Thoria Watershed, India |
| title_sort | measuring above ground carbon stock using spatial analysis and the invest model application in the thoria watershed india |
| topic | modelling carbon stock assessments watersheds spatial analysis |
| url | https://hdl.handle.net/10568/169133 |
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