Estimation of Palm Oil Biomass Carbon from Sentinel-2 Image using the Random Forest Classification Method

Abstract

Oil palm is a carbon absorbing plant that stores it in biomass. To monitor biomass, especially in large areas of oil palm plantations, remote sensing data can be used combined with machine learning algorithms. The aims of this study were to estimate oil palm biomass carbon according to age class using non-destructive methods, as well as analyze the relationship between the reflectance of Sentinel 2 image oil palm and oil palm biomass carbon, and estimate the distribution of oil palm biomass carbon using a learning algorithm random forest (RF) engine. Measurement of biomass at the study site was carried out non-destructively using stratified purposive sampling. The closeness of the relationship between Sentinel 2 image and measured oil palm biomass is assessed from the coefficient of determination of the regression equation. Estimation of the distribution of biomass carbon in all research locations was carried out using the RF method with the Dzetsaka classification tool. The results showed that the highest biomass carbon stock was obtained in oil palm aged 20 years with an average of 59.6 tons C/ha, while the lowest biomass carbon stock was obtained in oil palm aged 17 years with an average of 32.9 tons C/ha. The reflectance value of Sentinel-2 image on the blue, green, red, and near infrared channels has a positive correlation to biomass carbon from oil palm with an R² greater than 0.8. The classification of biomass carbon with the RF approach applied to Sentinel-2 image gives an adequate accuracy value of 76.40% in the combination of the proportion of training and testing data 60% : 40%.