Published date: Jun 07 2022

Journal Title: KnE Life Sciences

Issue title: The First Asian PGPR Indonesian Chapter International e-Conference 2021

Pages: 368–381

DOI: 10.18502/kls.v7i3.11145

Serlina Hestiani Oktianserlinaoktian@gmail.comFaculty of Forestry, Nusa Bangsa University, Indonesia

Wahyu Catur AdinugrohoForest Research, Development and Innovation Agency, Ministry of Environment and Forestry, Indonesia

Nengsih AnenFaculty of Forestry, Nusa Bangsa University, Indonesia

Luluk SetyaningsihFaculty of Forestry, Nusa Bangsa University, Indonesia

To determine emission levels, information on carbon stocks and changes in each carbon pool is required. Aboveground biomass, particularly on dry land, is one carbon pool that contributes significantly to carbon storage. The goal of this study was to develop a model for estimating aboveground carbon stocks in the Mbeliling landscape, in Nusa Tenggara Timur, using a vegetation index that was correlated with field carbon stocks. The best model was then used to create a map of the distribution of carbon stocks as the final result. Simple linear regression analysis and multiple linear regression analysis were used in the study. Google Earth Engine was used to process the images on a cloud system. When comparing the RGI index for measuring field carbon stocks to other indexes, the correlation test revealed a perfect correlation. The linear regression model for aboveground biomass = 14.046 + 272.496 RGI (R-sq = 0.86) was found to be the best model for aboveground biomass. In the multiple linear regression model, there were signs of multicollinearity. With an overall accuracy of 68% and a cappa accuracy of 54.23%, the best model was able to be used to create a carbon stock map in Mbeliling landscape.

Keywords: Carbon stock estimation model, Above Ground Biomass, Sentinel 2A

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