Estimating Oil Palm Age using Regression and Random Forest with Sentinel-2 Data on Google Earth Engine: A Case Study in Betung Krawo, South Sumatra

Anggita Pratiwi Sutrisno, Agung Budi Harto, Budhy Soeksmantono

Abstract


Palm oil (Elaeis guineensis) was extensively farmed in Southeast Asia, mainly in Indonesia and Malaysia, and it had a significant impact on the economy of the region. This study aimed to measure and classify oil palm plantations by age using Google Earth Engine, comparing   Regression and Random Forest methods. The research focused on Betung Krawo in   South Sumatra Province, using Sentinel-2 MSI imagery from 2019 to 2022, with results showing an age range of 1 to 30 years. The Random Forest method achieved an accuracy of 0.844 and a Kappa value of 0.825, and Regression accuracy of 0.922 and a Kappa value of 0.913. Data was divided into 70% for training and 30% for testing in Random Forest Method. For the Regression method, the model derived   y= 〖-0.0011x〗^2+0.0266x +0.6148, R² = 0.8554. which means that the model indicated created fell into the strong category. This research helps to understand the condition and productivity of oil palm plantations, aiding farmers and managers in better decision-making,   including early disease detection.

Keywords


Oil palm; Google Earth Engine; Random Forest; Regression; Age classification

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DOI: https://doi.org/10.17509/gea.v24i2.69334

DOI (PDF): https://doi.org/10.17509/gea.v24i2.69334.g29147

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