Recent expansion of oil palm plantation in the most eastern part of Indonesia: feature extraction with polarimetric SAR.

Tropical forest is important in tackling global climate change. However, large-scale transformation of tropical forest in Indonesia is being done for oil palm plantation. Till date, there is a lack of scientific documentation that shows the most recent situation of oil palm plantation in the most eastern part of the country. This study documents the real condition of oil palm plantation expansion based on Sentinel data of European Space Agency (ESA). Sentinel-1 data sets of Papua Provinces of the year 2017 acquisition are used to avoid cloud cover problems in tropical regions. Object-based geospatial data feature extraction with Polarimetric SAR compared to the multiple classifier methods (that is, Gaussian Mixture Method, Random Forest, Support Vector Machine, and K-Nearest Neighbors) were introduced and employed using GRASS & QGIS of free open source software (FOSS). Google Earth Engine (GEE) was then used to verify the classification result of oil palm plantation. The preprocessing step is very crucial in achieving high accuracy. However, it is time-consuming. This study concluded that the proposed method and SVM classifiers achieved the highest accuracy, 99.7% and 99.12%, respectively. While the KNN, RF, and GMM classifier produced 94.63%, 89.06%, and 72.87%, respectively. We found that vast scale transformation of tropical rainforest in lowland areas is happening in eastern Indonesia. This situation should be strictly monitored and controlled to avoid more significant issues related to socio-economic-environmental implications. [ABSTRACT FROM AUTHOR]