Discriminating Industrial and Smallholder Oil Palm Plantations in Indonesia Using Sentinel-1 Textural Features

In recent decades, oil palm plantations have expanded significantly in Indonesia. Palm oil production currently relies on two plantation types with different economic, social and environmental impacts: (1) industrial plantations and (2) smallholder plantations. While efforts have been made to characterize these plantation types, this objective remains challenging for the remote sensing community. Consequently, this study assesses the potential of Sentinel-1 textural metrics in distinguishing between industrial and smallholder plantations, offering potential solutions to this persistent challenge. We used machine learning algorithms (Random Forest vs. eXtreme Gradient Boosting Tree) with textural features calculated by the Grey Level Co-occurrence Matrix. The results confirmed the potential of Sentinel-1 textural metrics to discriminate OP plantation types. The XGBTree model achieved a higher Kappa (0.71) than the Random Forest model (0.63). Moreover, the contrast, dissimilarity, GLCM-Mean, and GLCMVariance metrics were the most explanatory for discriminating industrial and smallholder plantations. This study possesses limitations, especially concerning its applicability on a broader scale. However, leveraging cloud computing tools like Google Earth Engine could aid in scaling up the methodology.