Insights into spatiotemporal dynamics of vegetation in the Beijing-Tianjin-Hebei urban agglomeration: exploring urbanization stage-specific responses and nonlinear driving mechanisms

• Introduced an analytical framework to explore vegetation dynamics. • Revealed different vegetation responses to different urbanization stages. • Identified dominant drivers of vegetation changes during urbanization. • Quantified time lag effects of dominant drivers on vegetation. • Provided empirical evidence for sustainable urban development. Vegetation change serves as a comprehensive indicator for monitoring regional and global environmental conditions, exhibiting increased complexity under accelerated urbanization. As a typical example of rapid urbanization, China has witnessed the emergence of several large urban agglomerations, making vegetation change studies particularly critical. However, inadequate consideration of spatiotemporal heterogeneity and complex interactions among multiple factors poses challenges in analyzing vegetation change in China’s urban agglomerations. Additionally, few studies simultaneously identify factor interactions and further quantitatively assess the underlying processes, such as potential time lags. Focusing on the Beijing-Tianjin-Hebei (BTH) region from 2000 to 2020, this study characterizes long-term vegetation dynamics using fractional vegetation cover (FVC) derived from Landsat imagery and validated with manual samples. We introduced an analytical framework that combines geostatistical tools to address spatiotemporal heterogeneity with the optimal parameter-based geographical detector (OPGD) model to disentangle complex interactions among multiple factors. Our findings revealed distinct north–south differentiation in FVC across the BTH region, delineated by the Taihang-Yanshan Mountain Range as the primary biogeographic boundary. Vegetation cover exhibited a fluctuating yet increasing trend with 65.87% of the area showing improvement, accompanied by intensified spatial heterogeneity. Temporally, this improvement appears to be strongly associated with ecological policies. These spatial heterogeneous patterns underscore the region’s sensitivity to altitude-dependent climatic gradients and uneven policy implementation. Notably, we also identified stage‑dependent vegetation trends within urban agglomerations, indicating that later-stage urbanization does not inevitably lead to a net vegetation decline. The OPGD model identified land use type, which directly reflects the impact of human activities and policy, as the dominant overall factor. Other detection analysis demonstrated synergistic amplification between paired factors, with optimal vegetation growth thresholds identified. Long-term analysis specifically revealed the evolving influencing factors (e.g., the contribution of urbanization rate factors more than tripled between 2015 and 2020). Considering that both spatiotemporal analysis and driving model identified policy as a key role, we further quantify the time-lag effects of policy by employing the autoregressive distributed lag (ARDL) model. This analysis indicated that ecological policy in the BTH region significantly enhanced FVC, exhibiting a time lag of approximately three years. By qualitatively identifying and quantitatively analyzing vegetation, we offer empirical evidence and a methodological framework for balancing ecological preservation with sustainable development in urban agglomerations worldwide.