分析长江源区生态保护工程和气候变化对植被变化的影响程度,对于长江源区生态工程的生态效益评估,以及区域植被适应性生态管理政策的制定具有重要意义。因此,本文基于1982-2015年的归一化植被指数数据（Normalized Difference Vegetation Index, NDVI）和气象数据,分析长江源区植被NDVI的时空变化规律,构建预测植被NDVI对气候因子响应的人工神经网络模型,在此基础上,在年和季节尺度上量化气候变化和生态保护工程对长江源区植被变化的影响程度。结果表明：① 在长江源区气候条件变化和生态保护工程影响下,长江源区植被退化得到遏制,植被生长呈好转趋势;② 海拔相对较低的通天河附近植被NDVI增加幅度较大,高海拔的沱沱河和当曲流域的植被NDVI增加幅度相对较小;③ 长江源区植被NDVI对气候因子响应存在1~2月的滞后性。构建的人工神经网络模型的拟合优度参数人工神经网模型具有较高的预测精度,可以用来模拟植被NDVI对气候因子的响应;④ 年尺度的植被NDVI增加受到生态保护工程的影响程度（58.5%）大于气候变化的影响程度（41.5%）。生长季生态保护工程对NDVI的影响程度（63.3%）大于气候变化对NDVI的影响程度（36.7%）,而非生长季气候变化是影响长江源区植被生长的关键要素（52.8%）。研究结果有助于为长江源区植被生态系统恢复、管理和利用战略的科学制定提供决策依据。

Quantitative research on the effects of ecological conservation projects and climate variations on vegetation changes is vital to the ecological benefit evaluation of ecological conservation projects, and has important implications for sustainable ecological rehabilitation management strategies in the source region of the Yangtze River. Based on the normalized difference vegetation index (NDVI) data and meteorological data during 1982-2015, this paper examines the temporal and spatial variations of NDVI; constructs back propagation artificial neural network (BPANN) model to simulate the responses of NDVI to climate factors; and quantifies the effects of ecological conservation projects and climate variations on vegetation changes at the annual and seasonal scales in the source region of the Yangtze River. The results indicate that: (1) Because of the effects of ecological conservation projects and climate variations, vegetation degradation curbed in the source region of the Yangtze River. (2) NDVI increased greatly near the Tongtian River located at relatively low altitudes. Minor increases in NDVI were observed near the Tuotuo and Dangqu rivers located at relatively high altitudes. (3) A time lag (about 1-2 months) existed between NDVI and major climate factors in the source region of the Yangtze River. The goodness of fit of the BPANN model shows that the simulation accuracy is relatively high. The model can be used to simulate the responses of NDVI to climate variations. (4) Ecological conservation projects exerted a slightly greater impact on NDVI changes than they did on climate variations at the yearly time scale (58.5% and 41.5%, respectively). During growing season, ecological conservation projects also exerted a slightly greater impact on NDVI changes than they did on climate variations (63.3% and 36.7%, respectively). During non-growth season, climate variations are the key factor affecting vegetation growth in the source region of the Yangtze River (52.8%). The research results provide a basis for scientific decision-making about the vegetation ecosystem rehabilitation, management and utilization strategies in the source region of the Yangtze River.

<p>Based on MODIS NDVI and meteorological data from 2000 to 2010, this study analyzed the correlation between NDVI and meteorological factors pixel by pixel in the Lancang River Basin, studied the spatial pattern of the relationship between vegetation and climate, and explored the possible influence factors. The study indicated that: 1) Both temperature and precipitation had significant impacts on vegetation growth in the Lancang River Basin, and the temperature&rsquo;s effect was particularly striking. 2) The response of vegetation growth to climate change showed significant time lag effect, and the lag time shortened as the latitude went up. 3) For different vegetation types, both the orders of influence degree of meteorological factors on NDVI and the sensitive degree of NDVI to climate change were, in turn, grassland, cropland, shrub forest and woodland. For the same vegetation, the impact of temperature was greater than that of precipitation, but NDVI was more sensitive to precipitation. 4) Climate characteristics (perennial mean temperature and multi-year average precipitation) had significant impacts on the response time of NDVI to climate change. There is no significant influence of perennial mean temperature on the relationship between NDVI and temperature, but multi-year average precipitation has impacts on the degree of correlation between NDVI and precipitation.</p>

. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC, and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010. They also showed that GLC_FCS30-2015 achieved the best overall accuracy of 82.5 % against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).

中国西北地区土地荒漠化问题严重，生态环境脆弱。厘清该地区植被覆盖时空变化特征及影响因子，对生态环境保护具有重要意义。基于MOD13A3数据，通过最大值合成法处理获得2000—2019年归一化差值植被指数（Normalized Difference Vegetation Index，NDVI）时序数据，采用趋势分析、Hurst指数法及地理探测器对研究区植被覆盖的时空变化特征及影响因子进行分析。结果表明：（1）2000—2019年，研究区植被覆盖整体呈增长趋势，NDVI年增长速率为0.0027（P&lt;0.05），均值为0.252。空间分区年增长速率有差异，黄河流域片区（0.0062）&gt;半干旱草原片区（0.0026）&gt;内陆干旱片区（0.0018）。（2）研究区植被覆盖呈增长趋势的面积占55.77%，退化区域占3.76%，增长的土地利用类型以耕、林、草地为主。植被覆盖变化趋势具有持续性的区域面积占总面积的31.87%，其中持续性改善面积（17.04%）大于持续性退化面积（1.27%），黄河流域片区增长情况及持续性增长情况最优。（3）影响植被覆盖空间分布的主要因子按影响力依次为降水、气温、日照、相对湿度，但对各分区的影响程度略有差异。黄河流域片区、内陆干旱片区空间分布受降水影响最大，半干旱草原区受日照影响最大。（4）研究区植被覆盖变化以自然因子与人类活动共同驱动为主，自然因子对植被生长的促进作用大于人类活动，且自然因子对植被覆盖变化的贡献率更高。本研究结果可为评估气候变化背景下西北地区生态环境变化提供参考。

The problem of land desertification in northwest region of China is serious and the ecological environment is severe. It is of great significance to clarify the spatio-temporal variation characteristics and driving factors of vegetation cover in this area for ecological environment protection. In this study， MOD13A3 products in this area were used as the data source to obtain the NDVI sequence set from 2000 to 2019 through the maximum value synthesis method. Trend analysis， anomaly analysis， Hurst index， geographic detector， correlation analysis and residue analysis were used to analyze the spatiotemporal variation characteristics and impact factors of vegetation cover in the study area. The results showed that ：（1） In recent 20 years， the vegetation coverage in the study area showed an overall growth trend， with an increase rate of 0.0027·a^-1 and an average NDVI of 0.252. However， the growth rate of the Yellow River basin area （0.0062·a^-1） is higher than that of the semi-arid grassland area （0.0026·a^-1） and inland arid area （0.0018·a^-1）. （2） The vegetation coverage in the study area is on the rise， accounting for 55.77% of the total area， while the degraded area accounts for 3.76% of the total area. The increased land use types were mainly tillage， forest and grassland. The area with sustainable change trend of vegetation cover accounted for 31.87% of the total area， the sustainable improvement （17.04%） was greater than the sustainable degradation （1.27%）， and the growth and sustainable growth of the Yellow River basin area were the best. （3） The main contributing factors that affect the spatial distribution of vegetation cover are precipitation， temperature， sunshine and relative humidity in order of influence， but the influence degree of each sub-region is slightly different. The spatial distribution of arid areas in the Yellow River basin and inland is most affected by precipitation， and the semi-arid grassland is most affected by sunshine. （4） Vegetation cover changes is mainly driven by natural factors and human activities， and natural factors on the growth of vegetation role in promoting are greater than human activity， and natural factors on vegetation cover change in the rate of contribution are higher. The results of this study can provide reference for assessing the ecological environment change under the background of climate change in northwest China.

三江源国家公园位于青藏高原腹地，拥有独特气候和丰富物种基因，在生态系统中具有特殊地位。然而，气候变化和人类活动的影响使其面临生态挑战。准确监测三江源国家公园植被净初级生产力（NPP）的时空变化对于促进生态环境的保护和改善至关重要。模型模拟是陆地生态系统研究的重要方法，但模拟结果存在不确定性。多模型集成技术能够综合不同模型的优点，提高NPP模拟的准确性，为研究NPP长时序变化提供思路，同时为环境治理提供科学支持。本文应用多模型集成分析方法，结合4种生态系统过程模型CLM、DALEC、CEVSA和GLOPEM-CEVSA，分析三江源国家公园2000—2018年NPP的时空变化，并探讨气候因子对NPP变化的影响。结果表明：① 2000—2018年三江源国家公园年均NPP为251.17 gC m^-2 a^-1，澜沧江源园区最大（267.24 gC m^-2 a^-1），长江源园区最小（121.88 gC m^-2a^-1），黄河源园区居中（198.81 gC m^-2 a^-1），NPP呈现自东南向西北递减的空间分布。② 2000—2018年三江源平均NPP为222.00~298.02 gC m^-2 a^-1，年际变化呈显著增加趋势，增速为9.8 gC m^-2 10a^-1。③ 气候因子对三江源NPP的影响存在区域差异性，长江源园区和黄河源园区主要受到气温和辐射的影响，澜沧江源园区还受降水的显著影响。本文研究结果将为三江源国家公园生态保护成效评估和科学管理提供技术支撑与决策依据。

The Three-River-Source National Park, located in the hinterland of the Qinghai-Tibet Plateau, possesses unique climate and abundant genetic species. However, this region is facing serious ecological problems due to climate change and human activities. Accurate monitoring of spatiotemporal variations in net primary productivity (NPP) in the Three-River-Source region is crucial for promoting ecological conservation and environment improvement. Model simulation is an important approach in terrestrial ecosystem research, but it inherently remains uncertain. Multi-model integration techniques can enhance the accuracy of NPP simulation and provide a better estimation of NPP variations for environmental governance. In this study, we used four process-based ecosystem models (i.e., CLM, DALEC, CEVSA, and GLOPEM-CEVSA) and a multi-model integration analysis method to examine the spatiotemporal changes in NPP in the Three-River-Source National Park from 2000 to 2018 and to investigate the effect of climatic factors on NPP variations. The results show that NPP exhibited a decreasing trend from southeast to northwest, with an average annual NPP of 251.17 gC m^-2 a^-1 during 2000-2018. Ecosystems in the Lancang River Source Park had the highest NPP (267.24 gC m^-2 a^-1), followed by the Yellow River Source Park (198.81 gC m^-2 a^-1) and Yangtze River Source Park (121.88 gC m^-2 a^-1. The average NPP in the Three-River-Source region ranged from 222.00 to 298.02 gC m^-2 a^-1 and had a significant increasing trend with the rate of 9.8 gC m^-2 10a^-1. The attributions of NPP variation to climatic factors were far different among regions. It was primarily affected by temperature and radiation in the Yangtze River Source Park and Yellow River Source Park, but was also significantly influenced by precipitation in the Lancang River Source Park. The findings of this study could provide technical support and decision-making basis for assessing the effectiveness of ecological conservation and ecological management in the Three-River-Source National Park.

Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the time-lag effects, which are the most important mechanism of climate-vegetation interactive effects. Extensive studies focused on large-scale vegetation-climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the time-lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the time-lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the time-lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate-driving factors for different vegetation types were determined. The results showed that (i) both the time-lag effects of the vegetation responses and the major climate-driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the time-lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the time-lag effects; (iii) for the area with a significant change trend (for the period 1982-2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the time-lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change. © 2015 John Wiley & Sons Ltd.

蒸散发（ET）是水文能量循环和气候系统的关键环节,研究ET的时空变化特征及其响应土地利用和气候变化的驱动机制对于理清流域水资源和气候变化的关系具有重要的意义。本文基于MOD16/ET数据集定量分析了海河流域2000-2014年ET的时空变化特征,并结合时序气温降水数据和土地利用数据,采用相关分析方法定量探索了ET与气候因子的驱动力关系。结果表明：① 海河流域2000-2014年ET表现为较为显著的空间分布格局,呈现出北部和南部高、西北部和中东部低的分布特性。不同土地利用类型的多年ET呈林地>草地>耕地>其他类型的特征;② 2000-2014年海河流域年均ET波动范围为371.96~441.29 mm/a,多年ET的均值为398.69 mm/a,平均相对变化率为-0.41%,整体呈下降趋势;③ 多年月ET与气温和降水均呈单峰型周期性变化趋势,年内月ET呈单峰变化趋势;④ 春秋两季的ET与降水和气温的相关性明显高于其他季节,ET与气温和降水的平均相关系数是-0.17和0.37,表明降水对于ET的响应程度强于气温;⑤ 驱动分区结果表明海河流域ET受气候因子驱动的主要类型是降水驱动型和降水、气温共同驱动型;⑥ 海河流域耕地ET变化气候因子驱动模式主要是降水、气温共同驱动型;林地、草地的驱动模式主要气温驱动型和降水驱动型,其他土地利用类型的驱动模式主要是受其他因素驱动。该研究将对海河流域水资源开发管理和区域气候调节起到科学指导作用。