Monitoring Spatiotemporal Expansion Dynamics of Short-Rotation Eucalyptus Plantations Over Large Scales Using Landsat Time-Series Data

Eucalyptus, valued for its rapid growth and economic potential, has been widely introduced in China to address timber demands while conserving natural forests. Precisely estimating the spatiotemporal expansion of short-rotation eucalyptus plantations is crucial for evaluating their ecological and social value and formulating effective sustainable forestry policies. Medium-resolution satellite images, such as Landsat data, offer a cost-effective tool for large-scale forest mapping compared with the traditional forest inventories. This study used pixel-level time-series analysis to identify annual eucalyptus plantation distributions across Guangxi, China, from 2004 to 2019, based on the standard temporal vegetation index curves derived from the characteristics of short-rotation and fast-growing eucalyptus. Furthermore, an image segmentation method, coupled with an empirical relationship linking patch-level landscape indices to optimal thresholds, was employed to eliminate isolated pixels and reduce omission errors arising from the above time-series analysis. The established thresholds increased the accurate identification of eucalyptus patches within segments. Our proposed eucalyptus detection algorithm achieved an overall accuracy exceeding 80%, demonstrating its effectiveness. The analysis revealed eucalyptus plantations increased from 0.42 × 10
⁶
ha in 2004 to 2.47 × 10
⁶
ha in 2019, exhibiting a pronounced northward expansion. Initially concentrated in upland areas, plantations subsequently expanded into flatter terrains, raising concerns about potential agricultural conflicts. Annual eucalyptus plantation maps offer critical information for sustainable forest management and policymaking. This study highlights the potential of medium-resolution satellite data and time-series analysis for robust and cost-effective monitoring of annual short-rotation timber forest expansion dynamics over large scales.