Chen Lili, Li Zhenhong, Zhang Chenglong, Fu Xinxin, Ma Jiahao, Zhou Meiling, Peng Jianbing
College of Geological Engineering and Geomatics, Chang'an University, Xi'an, 710054, China; Big Data Center for Geosciences and Satellites, Xi'an, 710054, China; Generic Technical Development Platform of Shaanxi Province for Imaging Geodesy, Xi'an, 710054, China.
College of Geological Engineering and Geomatics, Chang'an University, Xi'an, 710054, China; Key Laboratory of Loess, Xi'an, 710054, China; Big Data Center for Geosciences and Satellites, Xi'an, 710054, China; Key Laboratory of Ecological Geology and Disaster Prevention, Ministry of Natural Resources, Xi'an, 710054, China; Generic Technical Development Platform of Shaanxi Province for Imaging Geodesy, Xi'an, 710054, China.
Environ Res. 2025 Apr 1;270:120959. doi: 10.1016/j.envres.2025.120959. Epub 2025 Jan 28.
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.
植被对于调节气候系统和确保碳平衡至关重要。认识到气候变化（CC）和人类活动（HA）的影响对于理解植被变化至关重要。然而，气候时间滞后效应很少被测量，导致对CC对植被动态影响的评估不足。在本研究中，首先，基于Landsat影像数据集，分析了1986年至2022年秦岭北麓（NQLM）内核归一化差异植被指数（kNDVI）的时空变化。然后，采用考虑时间滞后效应的多元回归残差法来确定CC和HA对kNDVI变化的影响。最后，基于kNDVI趋势以及CC和HA对kNDVI的变化，获得了六种kNDVI变化模式。我们的研究发现：（1）在过去37年中，NQLM地区植被以每年0.0061的速率波动上升，大部分地区经历了显著的绿化（84.82%）。只有0.86%的区域经历了植被退化，植被稳定性得以维持。（2）kNDVI与降水和温度均呈正相关，kNDVI对降水的响应有1个月的时间滞后，对温度的响应为0个月。（3）CC对kNDVI变化的贡献率为84%，温度和降水分别以每年0.0012和0.0039的速率驱动kNDVI变化。HA对kNDVI变化的贡献率仅为16%。虽然HA的作用不可忽视，但这些发现强调了CC对植被变化的关键影响。（4）在六种kNDVI变化模式中，CC和HA共同促成了kNDVI变化，单独CC的影响比HA更显著。这些发现有助于政策制定者设计更具针对性的干预措施，以增强生态恢复力并支持长期环境稳定性，这对于在NQLM制定明智、可持续的植被恢复策略至关重要。
