Hyperspectral Remote Sensing Data Analysis for Oil Palm and Nipa Palm Plantation Using EnMAP-Box open-source plugin on QGIS
Spaceborne hyperspectral data can assist in estimating crop yields, predicting crop outcomes, and monitoring crops, which ultimately contributes to loss prevention and food security. Recently, EnMAP hyperspectral imagery has become available, starting from 2022. This study aims to analyze and classify oil palm and nipa palm plantations using hyperspectral images combined with machine learning algorithms. The Random Forest classifier, CatBoost classifier, and LightGBM classifier were utilized to automatically map the oil palm and nipa palm areas. The fully workflows of the hyperspectral imaging process were performed in EnMAP-Box plugin on QGIS software. The overall accuracy of three ML classfiers provides greater than 90% especially in oil palm and nipa palm plantations. Machine learning can find out the hidden information about spectral characteristics.