Aboveground Carbon Stock Estimation Model Using Sentinel-2A Imagery in Mbeliling Lanscape in Nusa Tenggara Timur, 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
 Maslin MA. Climate change: Essential knowledge for developing holistic solutions to our climate crisis. Emerging Topics in Life Sciences. 2019;3(2):245–256.
 Alemu B. The role of forest and soil carbon sequestrations on climate change mitigation. Research Journal Environmental Management. 2014;3(10):492-505.
 Ravindranath N, Ostwald M. Carbon inventory methods. Bangalore: Indian Institute of Science; 2008.
 Saatchi SS, Harris NL, Brown Sandra et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(24):9899–9904.
 Krisnawati H, Adinugroho WC, Imanuddin R, Hutabarat S. Forest biomass estimator for CO2 emission calculation in central Kalimantan. Bogor: Research and Development Center for Conservation and Rehabilitation, Forest Research and Development Agency; 2014.
 Marini Y, Hawariyah S, Hartuti M. Comparison of supervised maximum likelihood classification methods with object-based classification for pond land inventory in Maros Regency. Proceedings of the National Remote Sensing Seminar 2014.2014:505–516.
 Galidaki G, Zianis D, Gitas I et al. Vegetation biomass estimation with remote sensing: Focus on forest and other wooded land over the Mediterranean ecosystem. International Journal of Remote Sensing. 2016;38(7):1940-1966.
 Herold M, Román-Cuesta RM, Mollicone D et al. Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD+. Carbon Balance Manage. 2011;6(3):1–7.
 Mastu L, Nababan B, Panjaitan J. Object-based benthic habitat mapping using sentinel-2 imagery in fragrant waters of Wakatobi regency. Journal of Tropical Marine Science and Technology. 2018;10(2):381-396.
 Krisnawati H, Adinugroho WC, Imanuddin R. Monograph allometric models for estimating tree biomass in various types of forest ecosystems in Indonesia. Bogor: Ministry of Forestry; 2012.
 Anitha K, Verchot LV, Joseph S, Herold M, Manuri S, Avitabile V. A review of forest and tree plantation biomass equation in Indonesia. Annals of Forest Science. 2015;(72):981-997.
 Adinugroho WC, Imanuddin R, Krisnawati H, Ayat A, Wirastami LA. Carbon stock (c-stock) bentang alam mbeliling. Bogor: Indonesian Bird/ Indonesian Wild Bird Conservation Association; 2020.
 Hidayatullah MI. Estimation of biomass potential on teak stands (Tectona grandis Linn. f) using landsat 8 imagery at BKPH wilangan selatan KPH saradan [Thesis]. Bogor: Faculty of Forestry, Bogor Agricultural University; 2016.
 Widhi SJK, Murti SH. Forest carbon stock estimation using landsat 8 imagery in Tesso Nilo national park, Riau. Indonesian Earth Journal. 2014;3(2):1-11.
 Fadiakhumairah M. Estimating model of mangrove ecosystem stand stock using high resolution image of Spot 6 in Kubu Raya Regency, West Kalimantan [Thesis]. Bogor: Faculty of Forestry, Bogor Agricultural University; 2018.
 Xue J, Su B. Significant remote sensing vegetation indices: A review of developments and applications. Jurnal Hidawi. 2017;2017(3):1-17.
 Filho MG, Kuplich TM, Quadros FLFD. Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data, International Journal of Remote Sensing. 2020;41(8):2861-2876.
 Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 2017;202:18-27.
 Sumadi A. Onion wood (Disoxylum molliscimum burm f.) estimator model in Bengkulu province. Journal of Plantation Forest Research. 2010;7(5):227-231. https://doi.org/10.20886/jpht.2010.7.5.227-231
 Struges HA. The choice of a class interval. Journal of the American Statistical Association. 1926;21(153):65-66.
 Jaya INS. Digital image analysis: Remote sensing perspectives for natural resources management. Bogor: Faculty of Forestry Bogor Agriculture University; 2010.
 Ayuningdyah NE. Mangrove biomass estimation model using SPOT 6 high resolution imagery in Kubu Raya Regency, West Kalimantan [Thesis]. Bogor: Faculty of Forestry, Bogor Agricultural University; 2018.