Long-Term Vegetation Dynamics and Seasonal Drivers in the Fen River Basin Revealed by kNDVI

Large-scale ecological restoration has substantially altered vegetation patterns in many environmentally sensitive regions, yet how the relative importance of climatic and non-climatic influences varies among seasons remains insufficiently understood. This study investigated long-term vegetation dynamics and their seasonal drivers in the Fen River Basin, China, using kernel Normalized Difference Vegetation Index (kNDVI) derived from the GIMMS-3G+ dataset for 1982–2022. Trend analysis, partial and multiple correlation analyses, residual trend analysis, and land-use change information were integrated to characterize vegetation changes and assess the relative contributions of climatic and residual-based anthropogenic influences. The results showed widespread vegetation greening across the basin. Growing-season kNDVI increased significantly at a rate of 0.0029 yr−1, with significant increases occurring over 90.74% of the basin, while the mean growing-season kNDVI after 2000 was 23.1% higher than that before 2000. Precipitation showed the strongest overall relationship with vegetation, with positive correlations across 92.86% of the basin, whereas solar radiation exhibited relatively weak associations. Residual-based attribution further revealed pronounced seasonal differences in vegetation drivers. The climate-predicted component accounted for 55.9% of the attributed vegetation greening in spring, whereas the residual component accounted for larger relative shares in summer (60.3%) and autumn (61.7%). Over the entire growing season, the climate-predicted and residual components accounted for 41.6% and 58.4% of the attributed greening, respectively. Land-use transitions after 2000, particularly farmland-to-forest and farmland-to-grassland conversions, provided additional evidence consistent with a possible contribution of ecological restoration to vegetation recovery. Overall, the results reveal a clear seasonal shift from stronger climatic control in spring toward increasing residual-based anthropogenic influences in summer and autumn. This study demonstrates the value of integrating long-term kNDVI remote sensing, seasonal attribution, and land-use information for monitoring vegetation recovery and supporting adaptive ecosystem management in ecologically sensitive regions.