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Computational Ecology and Software, 2023, 13(4): 123-135
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Article

Estimating above and below ground carbon stock of forest using field inventory and vegetation indices: A case study of Godebie National Park, Ethiopia

Adamsew Marelign1, Muhabaw Taju2, Ebrahim Esa3, Asrat Akele1, Temesgen Mekonen1, Birara Fentahun4, Kindie Gebeye4, Habtamu Tekeba1
1Department of Natural Resource Management, College of Agriculture and Environmental Sciences, University of Gondar, P.O Box 179 Gondar, Ethiopia
2Department of Forestry, College of Agriculture and Environmental Sciences, University of Gondar, Gondar, Ethiopia
3Department of Geography and Environmental Studies, College of Social Science and Humanities, University of Gondar, P.O Box 179 Gondar, Ethiopia
4Department of Plant Sciences, College of Agriculture and Environmental Sciences, University of Gondar, Gondar, Ethiopia

Received 11 April 2023;Accepted 20 May 2023;Published online 20 June 2023;Published 1 December 2023
IAEES

Abstract
Forests are the potential source for managing carbon sequestration and balancing universal carbon equilibrium between sources and sinks. In view of the importance of biomass, this study makes an attempt to estimate temporal and spatial carbon stock of Godebie National Park, Ethiopia, using Moderate Resolution Imaging Spectro radiometer (MODIS), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and the field inventory data through geospatial techniques. A model was developed for establishing the relationship between forest carbon, EVI, and NDVI in the selected study site. The correlation value between estimated carbon stock with EVI were found as 0.69, while with NDVI, the values were obtained as 0.87 respectively. The regression model of measured biomass with NDVI and EVI was developed for the data obtained during the period 2020-2021. The R2 values obtained were 0.81 for the regression model between estimated carbon stock and EVI, and 0.77 for the regression model between NDVI and estimated carbon stock. The results indicate that the methodology adopted in this study can help in selecting best fit model for analyzing relationship between carbon stock and NDVI/EVI and for estimating biomass and carbon stock using allometric equation at various spatial scales. The produced output map and allometric equation revealed carbon stock distribution of 5.88 t/ha up to 900 t/ha, with an average value of 406.67. Generally, the approaches used on this study can be used by the forest planners, policy makers, and government officials for conservation and protection of the forest ecosystem.

Keywords carbon stock;remote sensing;vegetation index;Godebie;regression.


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