Understanding the spatiotemporal dynamics of vegetation under the coupled influence of climatic and anthropogenic drivers is crucial for ecological governance at the river basin scale. This study focuses on the Huaihe River Basin (HRB) in China, a climate-sensitive region spanning both northern and southern climatic zones. Previous studies have often overlooked the inherent temporal and spatial autocorrelation in NDVI time series within this region, which may lead to overestimation or underestimation of vegetation trends, as well as misidentification of spurious trends as statistically significant. To address spatial and temporal autocorrelation challenges, we developed a novel hybrid framework combining Univariate Stationary First-order Gaussian Autoregressive (AR1) modeling with spatial autocorrelation analysis (Moran’s I index), enabling robust detection of vegetation trends, stability evaluation, and clustering characteristics. Combined with Geodetector, we quantified the impacts of climatic extremes, land use change, and urbanization on NDVI patterns(2000–2022). Key findings reveal: (1) A significant greening trend with mean annual NDVI increase of 0.00152 yr⁻¹ (p < 0.05), demonstrating basin-wide ecological improvement; (2) Spatially divergent patterns where 42.89% of the basin show significant NDVI growth, contrasted by 9.54% degradation areas. Notably, 47.57% exhibits non-significant increases, emphasizing the necessity of temporal autocorrelation correction in trend detection; (3) Geodetector analysis identifies land use type as the dominant spatial heterogeneity driver (q = 0.35–0.42), while extreme climatic events (particularly the 2000–2001 mega-drought) govern temporal anomalies. Urban expansion reduced vegetation cover, except near water bodies where NDVI remained stable. Interaction analysis revealed nonlinear synergies between anthropogenic activities (e.g., GDP, population) and climatic factors, emphasizing the need for adaptive policies in transitional ecosystems. These findings advocate for tiered management strategies incorporating climate-adaptive zoning, emphasizing riparian corridor conservation and dynamic land-use optimization to balance ecological restoration with urban development. This work offers a replicable framework for regional-scale ecological assessments.

This research seeks to advance the understanding of ecological management in the Huaihe River Basin, China. The spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) and its underlying driving forces are examined utilizing SPOT-NDVI data covering the period 2000-2019. Linear regression analysis is employed to quantify trends and assess the statistical significance of NDVI variations. A differencing approach is subsequently applied to discern the variability in NDVI. The distribution of its spatial pattern stability over 20 years is explored through the coefficient of variation. Lastly, the Geodetector method is utilized to elucidate the driving factors influencing NDVI dynamics. The results indicate that the overall NDVI is generally high, with an overall growth rate of 0.00148/year, and 2010 is identified as a significant inflection point in the trend. NDVI values are notably lower in urban areas, corresponding to urban expansion, except for regions near rivers and lakes. Land use type emerges as the strongest explanatory factor for NDVI among single factors, with climate factors exhibiting a relatively weaker degree of explanatory force. These findings suggest that the overall NDVI in the Huaihe River Basin is high, indicative of a vegetation condition that continues to improve.