A new framework for mapping rubber plantations through the combination of semiautomatic sample migration, dynamic phenology, and change detection variables based on time series Landsat images

Reliable mapping of rubber plantations is essential for yield estimation and sustainable land management, yet existing methods struggle with the lack of historical samples and the underuse of dynamic phenological information. We propose a novel time-series mapping framework that overcomes these limitations through three methodological innovations: (1) propose a semiautomatic sample migration algorithm to generate reliable historical samples, (2) develop dynamic phenology indices to capture seasonal growth patterns, and (3) change detection variables were utilized to capture the temporal characteristics of the rubber plantation time series. The variable importance analysis revealed that elevation was consistently highly ranked in rubber plantations classification. Dynamic phenology indices were also crucial, especially those based on the month of major leaf fall. The change detection variables, particularly the NDFI, ranked highly across all years. Overall, the highest classification accuracy for rubber plantations was achieved by using all six variable types: spectral bands, composite indices, terrain, textures, change detection, and dynamic phenology indices. When applied at an experimental site in China, the framework achieved over 97% accuracy for historically migrated samples and the rubber plantations mapping accuracy (RPMA) of 93.68% between 2003 and 2022. Transfer tests in Myanmar and Thailand confirmed its robustness, with RPMA of 94.34% and 93.73%, respectively. The maps showed high spatial consistency with Google Earth imagery, clearly distinguishing rubber plantations from other vegetation. By integrating sample migration with dynamic spatiotemporal features, this framework provides a transferable and scalable solution for long-term rubber plantation monitoring. This highlights the critical role of phenology and change detection in improving classification accuracy, offering new opportunities for regional-scale plantation mapping in Southeast Asia.