Multiscale exploration of spatiotemporal dynamics in China's largest urban agglomeration: An interactive coupling...

Human economic construction increasingly impacts highly sensitive ecological zones, weakening ecosystem health in cross-regional urban agglomerations. Exploring the spatiotemporal dynamics of urban agglomerations from the interactive coupling perspective between human activity intensity (HAI) and ecosystem health index (EHI) is crucial for resolving human-land conflicts. This study developed a human-land coupling assessment framework integrating human footprint and ecosystem Maintain-Bearing-Service-Resilience models. Across multiple scales, from urban agglomerations and cities to grid cells, we initially employed exploratory spatiotemporal data analysis techniques to reveal HAI and EHI evolution patterns. Subsequently, we used the four-quadrant model, coupling coordination degree (CCD), and relative development model to explore their spatiotemporal interactions. Applied to China's largest urban agglomeration, the middle reaches of the Yangtze River urban agglomerations (MRYRUA), results revealed a significant spatiotemporal mismatch pattern between HAI and EHI. High HAI and low EHI areas were widely distributed in highly urbanized waterfront plains. At the urban agglomeration scale, HAI and EHI exhibited spatiotemporal differentiation patterns extending toward polarization along the Yangtze River Economic Belt, while their correlation intensity among cities indicated conflicting development patterns. At the grid scale, the spatiotemporal clustering pattern highlighted waterfront built-up areas as HAI hotspots and peripheral forest zones as EHI hotspots. The interactive relationship between HAI and EHI shifted increasingly towards Quadrant IV as HAI rose. The coupling levels between HAI and EHI will tend toward misalignment as urbanization advances, although current CCD shows positive trends. This study offers scientific guidance for achieving sustainable development in urban agglomerations across multiple scales.