Multisource Remote Sensing and Machine Learning for Mapping Sisaket Lava Durian Plantations

Abstract

Accurate mapping of commercial durian plantations is essential for agricultural inventory, precision agriculture, and sustainable land management but remains challenging because of spectral similarity with other evergreen vegetation. This study developed a multisource remote sensing approach integrating multi-temporal Sentinel-2 imagery, Sentinel-1 Synthetic Aperture Radar (SAR), DEM-derived terrain variables, recursive feature selection, and machine learning for binary durian plantation classification in Sisaket Province, Thailand. A total of 2430 field reference samples were used to evaluate Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART). RF achieved the strongest overall performance using the integrated Sentinel-2, Sentinel-1, and DEM dataset, with an Overall Accuracy of 90.38%, an F1-score of 90.59%, a Kappa coefficient of 0.81, and an AUC of 0.991. Recursive feature selection reduced the predictor set from 53 to 12 variables (77.4%) while retaining high classification performance. The retained 12-predictor subset comprised nine Sentinel-2-derived variables (five seasonal EVI and four NDBI variables), two Sentinel-1 VV/VH ratio variables, and one DEM-derived elevation variable, corresponding to 75.0%, 16.7%, and 8.3% of the retained predictors, respectively. The proposed approach provides an accurate and computationally efficient method for durian plantation mapping within the study area, with broader applicability requiring further validation.