近几十年来，中国环境变化呈现出高度的时空异质性，陆地植被状况对外界环境变化的响应也表现出复杂性。目前，中国植被呈现持续绿化的趋势，然而，当前缺乏2000年后中国长时间序列的植被动态变化检测及其影响因素的研究。本研究基于GLASS FVC数据、气候数据、CO₂、土地覆盖数据以及夜间灯光数据，量化了植被生长趋势及影响因素贡献。首先，采用线性回归分析了植被生长趋势，分别以国家和城市为尺度，分析统计了2001–2018年植被变化。其次，利用广义线性模型(generalized linear models, GLM)，结合GLASS FVC数据、气候数据、CO₂和土地覆盖数据，量化了中国不同区域内影响植被生长的影响因素贡献。最后，为了探究城市化的影响，聚焦于中国32个主要城市，利用GLM，结合GLASS FVC数据、气候数据、CO₂和夜间灯光数据，量化了中国32个主要城市内影响植被生长的影响因素贡献。结果表明：

（1）     中国整体上FVC呈现东高西低的分布特点，FVC总体上呈现增加的趋势，平均FVC从2001年的28.2%增加到2018年的32.2%，平均每年增加了0.22%。植被覆盖度呈现显著增加趋势的区域占30.91%，呈现显著下降趋势的区域占3.38%。少数地区植被呈现减少的趋势，例如中国西南地区以及长江三角洲。全国32个主要城市中，有21个城市平均FVC呈现减少的趋势，其中核心区有23个城市呈下降趋势，扩展区有21个城市呈下降趋势。

（2）     气候要素（包括CO₂、降水、温度和辐射）和人类活动（包括土地覆盖类型变化和城市化）均对FVC的变化产生重要影响，其中气候要素是影响FVC变化的主要因素。具体表现为，中国整体上CO₂、降水、温度、辐射和土地覆盖变化分别占FVC变化的31%、24%、17%、14%和2%，而在发生土地覆盖变化的像元统计中，CO₂、降水、温度、辐射和土地覆盖变化分别占FVC变化的33%、16%、16%、13%和12%。此外，在不同的地理分区中，影响植被的主导因子也发生了变化。降水是青藏和西北地区植被覆盖度变化的主要影响因素，分别解释了24%和36%的植被覆盖度变化。CO₂是南方地区和北方地区植被覆盖度变化的主要影响因素，分别解释了38%和40%的FVC变化。

（3）     气候因子和CO₂是影响城市植被的主要因子。虽然中国经历了快速的城市化，但是中国32个主要城市的植被主要受气候因素和二氧化碳的影响，而非城市化。气候、CO₂和城市化对中国32个主要城市植被覆盖度变化的相对贡献分别为40.6%、39.2%和10.6%。气候因子中降水、温度和辐射对植被生长的贡献分别为13.2%、15.7%和11.7%。虽然降水、温度和辐射对植被的影响相似，但植被对降水、温度和辐射的响应表现出较强的空间异质性。不同的城市由于地理位置的差异，受到的主导因子也会发生变化。总体上，CO₂为主导，降水的影响由湿润区向干旱区递增，温度的影响沿着海岸线随着纬度增加而增加，辐射在一些海拔较高的城市中发挥了一些作用，城市化整体影响较小。

综合来看，本文基于一元线性回归分析了植被生长变化趋势，对于了解植被生长情况提供了科学依据。此外，研究利用GLM，结合CO₂、气候数据、土地覆盖数据以及夜间灯光数据，量化了影响因素的贡献，从不同的尺度上分析了影响因素对植被变化的影响。研究能够丰富植被变化领域的内容，并且在一定程度上帮助理解植被与环境之间的影响机制。

In recent decades, China's environmental change has shown a high degree of temporal and spatial heterogeneity, and the state of land vegetation has also shown complexity to the external environmental change. At present, China's vegetation presented a trend of continuous greening. However, studies on vegetation dynamics and its influencing factors in China after 2000 were still scarce. Therefore, this study quantified the contribution of vegetation growth trends and their drivers based on GLASS FVC data, climate data, CO₂ data, land cover data and nighttime light data. Firstly, linear regression method was used to analyze the trend of vegetation change at national and urban scales from 2001 to 2018. Secondly, the contribution of influence factor affecting vegetation growth in different regions of China was quantified using a generalized linear models (GLM) combined with GLASS FVC data, climate data, CO₂ and land cover data. Then, in order to explore the impact of urbanization, we focused on 32 major cities in China, and quantified the contribution of influence factor affecting vegetation growth in 32 major cities in China by using a GLM combined with GLASS FVC data, climate data, CO₂ data, and night light data.  The results show that:

(1) As a whole, FVC in China has a high distribution in the east and low distribution in the west. FVC has shown an increasing trend on the whole, with the average FVC increasing from 28.2% in 2001 to 32.2% in 2018, with an average annual increase of 0.22%. The area with significant increasing trend of FVC accounted for 30.91%, and the area with significant decreasing trend accounted for 3.38%. A few areas, such as southwest China and the Yangtze River Delta, showed a trend of vegetation reduction. Among the 32 major cities in China, 21 cities showed a decreasing trend in average FVC. Specifically, the FVC of 32 cities showed an overall decreasing trend, including 23 cities in the core area and 21 cities in the expansion area.

(2) Climate factors (including CO₂, precipitation, temperature and radiation) and human activities (including land cover type change and urbanization) have important impacts on FVC change, and climate factors are the main factor affecting FVC change. Specifically, on the whole, CO₂, precipitation, temperature, radiation and land cover change account for 31%, 24%, 17%, 14% and 2% of FVC change in China, while in the pixel statistics of land cover change, CO₂, precipitation, temperature, radiation and land cover change account for 33%, 16%, 16%, 13% and 12% of FVC change respectively. In addition, the dominant factors affecting vegetation also changed in different geographical regions. Precipitation was the main driving factor of FVC change in Qinghai–Tibet and Northwest China, which accounted for 24% and 36% of FVC change, respectively. CO₂ was the main driving factor of FVC change in southern China and northern China, explaining 38% and 40% of FVC change, respectively.

(3) Climate factors and CO₂ were the main factors affecting urban vegetation. China has experienced rapid urbanization, however, vegetation in its 32 major cities was still mainly influenced by climate factors and CO₂, rather than urbanization. The relative contributions of climate, CO₂ and urbanization to FVC change in 32 major cities in China were 40.6%, 39.2% and 10.6%, respectively. The contribution of precipitation, temperature and radiation to vegetation growth were 13.2%, 15.7% and 11.7%, respectively. Although the effects of precipitation, temperature and radiation on vegetation were similar, the responses of vegetation to precipitation, temperature and radiation showed strong spatial heterogeneity. Due to the difference of geographical location, the dominant factors of different cities will also change. In general, CO₂ plays the leading role, and the impact of precipitation increases from humid to arid areas. The impact of temperature increases along the coastline with the increase of latitude. Radiation plays a role in some cities with higher elevations, but the overall impact of urbanization is small.

In general, this study analyzed the vegetation growth trend based on linear regression, which provides a scientific basis for understanding the vegetation growth. In addition, GLM was used to quantify the contribution of influence factor and analyze the impact of influence factor on vegetation growth at different scales in combination with CO₂, climate data, land cover data and nighttime light data. Research can enrich the content of the field of vegetation change and help understand the impact mechanism between vegetation and environment to a certain extent.