Abstract:

The Qinling Mountains, as an important geographical boundary and key ecological conservation area in China, play a critical role in environmental preservation, species protection, and water conservation, while also significantly contributing to ecosystem carbon sources/sinks and carbon cycling processes. Therefore, conducting long-term, systematic monitoring and scientific evaluation of the spatiotemporal dynamics of vegetation in the Qinling Mountains can not only reveal the patterns of ecological and environmental changes but also provide crucial decision-making support for regional sustainable development strategies. This holds significant practical and scientific value for enhancing ecosystem stability and safeguarding the regional ecological security. Net primary productivity (NPP) refers to the remainder of the organic matter accumulated by plants through photosynthesis after subtracting the organic matter consumed through respiration. NPP not only reflects vegetation productivity but also serves as a crucial factor in determining ecosystem carbon sources and sinks and assessing ecological sustainability. Therefore, under the “dual carbon” strategic goals, investigating the spatiotemporal variations of vegetation NPP and its driving factors is essential for evaluating regional ecosystem quality, carbon sink functions, and guiding ecological conservation and restoration efforts. Currently, research on vegetation in the Qinling Mountains predominantly focuses on the dynamic monitoring of vegetation health using indices such as NDVI and EVI, whereas studies on net primary productivity (NPP) remain relatively limited. When exploring the relationships between vegetation NPP and environmental factors, the selection of driving factors is often restricted, failing to comprehensively reveal the complex mechanisms underlying these interactions. Therefore, systematic studies on vegetation NPP and its driving factors in the Qinling region are crucial to enhance our understanding of the ecological functions of the Qinling Mountains. This study used medium-resolution satellite data to explore the distribution and evolution of vegetation NPP along the northern slopes of the Qinling Mountains between 2001 and 2020. It further analyzed the responses of vegetation NPP to driving factors such as climate, topography, and land use types, providing a reference for accurately evaluating vegetation productivity, achieving regional ecological sustainability, and advancing carbon neutrality. First, vegetation NPP on a monthly scale from 2001 to 2020 was estimated using the improved CASA model based on NDVI, meteorological, and land use data. At the same time, the reliability of the estimated NPP was verified through MODIS NPP product data. Second, spatiotemporal variations in vegetation NPP were analyzed by adopting the Theil-Sen median trend analysis and Mann-Kendall tests, dividing the study period into four phases (five years per phase). This method revealed changes in vegetation NPP during different periods. Third, the correlations between NPP and precipitation, temperature, and solar radiation were analyzed using the Pearson’s correlation analysis. Finally, the driving forces of vegetation NPP were assessed using GeoDetector, which considers six factors: precipitation, temperature, solar radiation, elevation, slope, and land use types. The model quantified the contributions of different factors to NPP variations, analyzed the influence of these factors across different restoration units, and identified regional differences in vegetation NPP responses to the driving factors. The results showed that 1) from 2001 to 2020, the vegetation NPP on the northern slope of the Qinling Mountains exhibited an initial increase, followed by a decline and subsequent fluctuating growth. The lowest NPP value (639.27 g∙m⁻²) was recorded in 2001, whereas the highest (769.61 g∙m⁻²) occurred in 2008. A decreasing trend was observed between 2008 and 2010, after which the NPP showed fluctuating growth. The average NPP during 2001-2020 was 719.50 g∙m⁻², with an general increasing trend and an annual growth rate of 0.67 g∙m⁻². The seasonal variation in NPP was significant, with summer contributing the highest share (51.13% of the annual total) and winter contributing the lowest (4.89%). Spatially, NPP demonstrated a pattern of being low in the north and high in the south, attributed to the dominance of cropland and built-up land in the northern areas, which are heavily influenced by human activities, and lush vegetation at higher elevations and fewer human disturbances in the southern areas. 2) During 2001-2005, 89.96% of the northern slope exhibited an increasing NPP trend. However, between 2006 and 2010, only 11.33% of the area exhibited stable or increasing trends, primarily in relatively flat foothill regions, whereas the rest showed declining trends. Between 2011 and 2015, 62.79% of the study area experienced increasing NPP, whereas 33.89% exhibited declines, mainly in the southwestern part of the study area. After 2016, an increasing trend in NPP was observed. In general, 76.08% of the area showed increasing NPP trends, with 22.92% exhibiting significant increases, whereas 21.44% showed declines, with a significant decrease of 3.66%. These results indicated a pronounced overall upward trend in vegetation NPP on the northern slope. 3) Precipitation, temperature, and solar radiation were positively correlated with NPP across most regions, with positive correlation areas accounting for 73.26, 65.81, and 87.15%, respectively. Among which, the correlation coefficient for precipitation ranged from −0.67 to 0.98, with negative correlations covering 25.74% of the area, primarily in central regions. Temperature correlations ranged from −0.73 to 0.98, with negative correlations in northern areas dominated by crops, where excessive heat led to drought and reduced NPP. Solar radiation correlations ranged from −0.61 to 0.83, with only small northeastern areas showing negative correlations, while other regions, especially southern areas, exhibited significant positive correlations, indicating that solar radiation promoted NPP growth. 4) Vegetation NPP is influenced by multiple factors. Among the single factors, temperature, elevation, and precipitation were the most critical, with q values of 0.68, 0.61, and 0.56, respectively, reflecting significant differences in NPP responses to these factors. The interaction between any two factors contributed more to NPP changes than did single factors, highlighting the non-independent nature of these drivers. The interaction between temperature and land-use type was the most significant for NPP change, with a q value of 0.73. The analysis of vegetation NPP drivers in different ecological restoration units revealed that the relative influence of factors varied across units, underscoring the significant spatial heterogeneity and complexity of NPP responses. Therefore, for the ecological restoration and management of the northern slope of the Qinling Mountains, it is necessary to take appropriate ecological construction and management measures according to the characteristics of each restoration unit. This study provides a scientific basis for vegetation monitoring and ecological protection in the region.

Key words: net primary productivity of vegetation, spatiotemporal changes, correlation analysis, driving factors, geographical detector, the northern slope of the Qinling Mountains

摘要：

为探究秦岭北麓植被NPP的分布状况、演变规律及驱动因素，准确评估秦岭北麓植被生产能力，实现区域生态可持续发展，达成碳中和提供参考依据。利用改进CASA模型对秦岭北麓2001－2020年植被NPP进行估算，并利用趋势分析法、相关性分析法以及地理探测器对研究区植被NPP的时空演变特征和驱动因素进行研究。结果表明，秦岭北麓2001－2020年植被NPP呈波动上升的趋势，年均NPP为719.50 g∙m⁻²（以C计，下同），空间分布呈现南高北低的分布格局，季节差异明显，夏季NPP最高，约占全年NPP总量的51.13%；降雨、气温和太阳辐射3种气候因子与植被NPP整体呈现正相关关系，正相关区域占比分别为73.26%、65.81%、87.15%；植被NPP受多种驱动因子共同影响，从单一影响因子来看，气温、海拔以及降水是驱动秦岭北麓NPP变化的主要因子，其q值分别为0.68、0.61、0.56，从双因子交互来看，气温和土地利用类型交互作用下对NPP变化的解释力更强，其q值为0.73，从不同修复单元来看，不同影响因素对不同修复单元内植被NPP驱动力大小并不相同，反应了植被NPP对不同影响因子的响应具有显著的区域差异性和复杂性。研究结果可为秦岭北麓植被监测、生态环境保护提供科学参考。

关键词: 植被净初级生产力, 时空变化, 相关分析, 驱动因素, 地理探测器, 秦岭北麓