Highlights What are the main findings? A geographical process object-based spatiotemporal graph (GPO-STG) framework was developed to model continuous land-cover change processes from remote sensing time series. Multilevel analysis of the NX-LCC-GPO-STG identified the key landscape processes, dominant interaction mechanisms, and sequential evolutionary patterns. What are the implications of the main findings? The GPO-STG framework provides an effective and feasible alternative to conventional pixel- and object-based methods for characterizing complex spatiotemporal land-cover dynamics. The proposed multi-scale graph analysis confirms a "utilization-recovery" dynamic, revealing a dominant sequential transition from agricultural expansion to ecological restoration.Highlights What are the main findings? A geographical process object-based spatiotemporal graph (GPO-STG) framework was developed to model continuous land-cover change processes from remote sensing time series. Multilevel analysis of the NX-LCC-GPO-STG identified the key landscape processes, dominant interaction mechanisms, and sequential evolutionary patterns. What are the implications of the main findings? The GPO-STG framework provides an effective and feasible alternative to conventional pixel- and object-based methods for characterizing complex spatiotemporal land-cover dynamics. The proposed multi-scale graph analysis confirms a "utilization-recovery" dynamic, revealing a dominant sequential transition from agricultural expansion to ecological restoration.Abstract Remote sensing time series (RSTS) are essential for monitoring land surface dynamics, yet existing pixel- or object-based methods often treat changes as isolated snapshots, failing to capture continuous spatiotemporal interactions. To address this, this study proposes the geographical process object-based spatiotemporal graph (GPO-STG) framework, which models land-cover changes as continuous geographical process objects (GPOs) connected by spatiotemporal topological relationships (STTRs). We applied this framework to the China Land Cover Dataset (CLCD) for the central arid and semi-arid region of the Ningxia Hui Autonomous Region (1991-2020) and conducted a systematic multilevel analysis. At the node level, degree centrality analysis identified key processes, revealing that grassland growing acts as the high centrality backbone of the regional landscape structure. At the edge level, interaction pattern analysis quantified the relationships between land-cover types and evolutionary states, uncovering a dominant coupling between grassland growing and cropland fluctuating. At the subgraph level, chain pattern extraction traced sequential evolutionary trajectories, confirming a "utilization-recovery" dynamic characterized by a transition from agricultural expansion to ecological restoration. The results demonstrate that the GPO-STG framework effectively characterizes complex land-cover changes that are often missed by pixel- or object-based methods.