Mangrove forests have an essential role in reducing the impact of global warming in the urban and surrounding areas; one of them is through carbon sequestration. Carbon sequestration is needed to reduce greenhouse gases in the atmosphere. The capacity of carbon sequestration or carbon stocks of mangrove forests can be seen from its biomass value. This study aims to produce a regression model between the vegetation index and the carbon stock of mangrove forests so that carbon stocks can be seen easily from satellite imagery and analyze their distribution in the Coastal City of Benoa, Bali. The distribution of carbon stocks was analyzed using a combination of vegetation index approach and statistical regression analysis. The vegetation index used is ARVI and EVI obtained from processing Sentinel 2-A satellite imagery in 2020. Mangrove forest biomass values are derived from allometric equations. After getting the amount of biomass, a regression model was built with a vegetation index. The model with the highest level of accuracy is used to process the image of the whole mangrove forest. This study’s results indicate that the regression model formed by the ARVI has the highest level of accuracy compared to the EVI, with the best regression model for predicting carbon stock values is the exponential regression model with an ARVI vegetation index variable. High carbon stock values are distributed in almost all regions of Benoa Bay.
|Number of pages||8|
|Journal||International Journal on Advanced Science, Engineering and Information Technology|
|Publication status||Published - 2020|
- Benoa Bay
- carbon stock