CFDC: A Spatiotemporal Change Detection Framework for Mapping Tree Planting Program Scenarios Using Landsat Time Series Images

Artificial afforestation plays a critical role in ecological restoration, but its implementation involves multiple strategies—such as new afforestation, densification, and replacement afforestation. Long-term spatial and temporal identification of these tree planting program scenarios (TPPSs) is key to evaluating ecological restoration policies, yet existing pixel-based time series change detection methods still face challenges in discriminating these patterns on a large scale. To address these challenges, we propose CFDC, the first framework that synergistically integrates Continuous Change Detection (CCD) for temporal spectral trajectories and Focal Context (FC) analysis for spatial neighborhood context. A Spatiotemporal Coupling Index (STCI) is proposed to abstractly summarize the two modules, and a rule-based model classifies TPPSs by their unique temporal–spatial signatures. Implemented on Google Earth Engine (GEE) for Bayi District, Tibet, CFDC delivered overall accuracies of 76.0–82.5% from 2007 to 2022, with user’s accuracies for all TPPS types exceeding 75% in most years. Detected TPPS timelines coincide with documented ecological restoration projects within a ±1-year tolerance. Overall, CFDC offers a novel mechanism that fuses spatiotemporal features to effectively distinguish new afforestation, densification, and replacement afforestation scenarios, addressing the limitations of previous methods and enabling more accurate and scalable TPPS monitoring, thereby supporting scalable artificial forest management and ecological restoration planning.