Pixel-Based Mapping of Rubber Plantation Age at Annual Resolution Using Supervised Learning for Forest Inventory and Monitoring

Accurate mapping of rubber plantation stand age is essential for forest inventory, land use monitoring, and carbon stock estimation. This study proposes a pixel-based approach that integrates the Bare Soil Index (BSI) with Normalized Difference Vegetation Index (NDVI) time series to detect land clearance events and predict stand age. The methodology involves feature engineering, selection, and evaluation of three tree-based and one non-parametric supervised machine learning models. Predictive features were extracted from interannual spectral index profiles, with an optimal subset selected using Recursive Feature Elimination (RFE). The best-performing model, optimized using a grid search matrix, was trained and applied to stacked images for pixel-level land clearance prediction over 37 years of NDVI and BSI time series. By aggregating predictions and performing post-classification analysis, a spatially explicit stand-age map was generated. The result was validated using secondary rubber farmer registration data, achieving an overall prediction accuracy of 84.5% and a root mean squared error (RMSE) of 1.86 years. The findings highlight the effectiveness of machine learning with NDVI and BSI time series for stand-age estimation, contributing to advancing remote sensing methodologies for forest inventory and support furfure high-precision carbon stock assessments.