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Vegetation is fundamental to regulating the climate system and ensuring carbon balance. Recognizing the effects of climate change (CC) and human activities (HA) is vital for understanding shifts in vegetation. However, climate time-lag effects are rarely measured, resulting in an inadequate assessment of CC's effects on vegetation dynamics. In this study, firstly, based on the Landsat image dataset, the spatiotemporal variations of the kernel Normalized Difference Vegetation Index (kNDVI) in the northern foothills of the Qinling Mountains (NQLM) from 1986 to 2022 were analyzed. Then, the multiple regression residuals method, accounting for time-lag effects, was employed to determine the effects of CC and HA on kNDVI change. Finally, six patterns of kNDVI change were obtained based on the kNDVI trend and the changes of CC and HA to kNDVI. Our research found: (1) Over the past 37 years, the vegetation has fluctuated upward at a rate of 0.0061/a, and most areas have experienced significant greening (84.82%) in the NQLM. Only 0.86% of the area has experienced vegetation degradation, and the stability of vegetation has been maintained. (2) The kNDVI exhibited a positive correlation with both precipitation and temperature, kNDVI response to precipitation with 1-month time lag and 0-month for temperature. (3) The contribution of CC to kNDVI change was 84%, temperature and precipitation drive kNDVI change rates with 0.0012/a and 0.0039/a, respectively. The contribution of HA to kNDVI change was only 16%. While the role of HA cannot be overlooked, these findings underscore the critical influence of CC on vegetation changes. (4) Among the six patterns of kNDVI change, CC and HA collectively contributed to kNDVI change, and the effect of CC alone was more significant than that of HA. These findings can help policymakers design more targeted interventions to enhance ecological resilience and support long-term environmental stability, which is critical for the development of informed, sustainable revegetation strategies in the NQLM.
Chen et al. (Tue,) studied this question.