A comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr^–1.     

三套NDVI长时间序列植被指数的对比——以玛曲为例

NDVI／MODIS、NDVI／GIMMS和NDVI／NSMC是时间长度不同、空间分辨率相差甚远的3套ND—VI数据集,如何集成应用这些不同时间长度、不同分辨率的数据进行相关研究,数据集间的比较是最基础的工作。本文以甘肃省甘南州玛曲县为例,用直方图、相关分析、趋势分析等方法研究了这3套NDVI产品数据集的相互关系。结果表明：1）NDVI／NSMC与NDVI/MODIS的直方图具有类似的图像分布特征,但是NDVI／MODIS数据分布范围更大;2）3套NDVI在数值上表现为NDVI／MODIS〉NDVI／GIMMS〉NDVI／NSMC;3）3套数据集空间图像特征一致,两两间均具有十分显著的空间相关性,其中1月份相对最弱,5、10月份最强,三者相比NDVIfNSMC与NDVI／MODIS的空间相关性更强;4）1—3月、5—8月及年均的NDVI／GIMMS与NDVI／NSMC值存在显著的时间相关性,但两者逐年变化趋势存在较大差别,两者气候倾向率相差最大的高达5倍之多。NDVIfNSMC数据集在处理过程中可能未进行大气订正及交叉定标,这是造成共同源的NDVIfGIMMS与NDVI／NSMC差异较大的重要原因。     

Autumn NDVI contributes more and more to vegetation improvement in the growing season across the Tibetan Plateau

ABSTRACT

Detecting changes in vegetation, distinguishing the persistence of changes, and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics. Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (NDVI) version NDVI_3g dataset in the Tibetan Plateau, the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982. Dynamic trends in vegetation experienced an obvious increase at regional scale, but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended. At pixel scale, areas with significant browning significantly expanded over 14 periods for all seasons, but for significant greening significantly increased only in autumn. The changes of vegetation activity in spring were the most drastic among three seasons. Increased increments of NDVI in summer, spring, and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods. Vegetation activity was mainly regulated by a thermal factor, and the dominant climatic drivers of vegetation growth varied across different seasons and regions. We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.     

蒙古高原NDVI的空间格局及空间分异

基于GIMMS NDVI多年最大值合成数据,采用空间统计学方法,利用Moran’ s I系数分析、半变异函数分析以及分维分析等3种方法,对蒙古高原NDVI空间格局及空间分异特征进行研究。结果表明:(1)蒙古高原NDVI的空间分布在全局范围内呈现正的空间自相关,相似的NDVI值倾向于聚集在一起,这表明蒙古高原植被具有较好的整体性,地表植被无显著破碎化;(2)蒙古高原NDVI的空间分布虽然同时受到结构性因子和随机性因子的影响,但结构性因子占据绝对控制地位,结构性因子引起的空间变异占系统总变异的88.7%;(3)蒙古高原NDVI存在各向异性的分布特征,具有相似NDVI值的像元主要沿着西北-东南方向展布;全局NDVI空间自相关距离约为1178km,西北-东南方向与东北-西南方向的空间自相关距离比可达2.4 ∶ 1。     

不同数据源归一化植被指数在中国北方草原区的应用比较

中国北方草原区生产力在区域碳水循环、农牧业发展中举足轻重。归一化植被指数（Normalized Difference Vegetation Index,NDVI）广泛应用于生产力的计算,然而目前来源众多的NDVI数据反映中国北方草原植被时空动态的一致性仍未可知。本研究利用2000—2015年3个来源NDVI数据集（MODIS NDVI、GIMMS NDVI和SPOT NDVI）并以国际上公认的数据准确性较高的MODIS NDVI为基准对比分析了中国北方草原区NDVI时空动态的一致性,并选取适宜的NDVI产品揭示研究区NDVI长期的时空格局。结果表明：整个中国北方草原区以及部分草原类型（高寒草甸、高寒草原、高寒荒漠、温带荒漠草原）GIMMS NDVI和MODIS NDVI 2套数据集无论是数值范围,还是年际波动和变化趋势具有较高一致性（二者在高寒草甸、高寒草原、高寒荒漠、温带荒漠草原的相关系数分别为0.60、0.47、0.51、0.74）,而SPOT NDVI数值远高于其他2个数据集,尤其是在青藏高原草原区,SPOT NDVI数值每年较另外两套数据集约偏高0.15,表明该区域使用SPOT数据应慎重。部分温带草原类型（典型草原和草甸草原）GIMMS NDVI和SPOT NDVI数据集在年际波动以及变化趋势上具有较高的一致性（相关系数分别为0.85和0.60）,但温带草原区3种数据集NDVI数值范围整体相差不大,小于0.06。基于上述结果,本研究进一步采用时间序列最长且与MODIS NDVI一致性最好的GIMMS NDVI分析了研究区NDVI的时空动态,发现1982—2015年中国北方草原区NDVI整体呈增加趋势,25%的区域达显著水平（p<0.05）,主要集中在温带草原区;高寒草原区NDVI大部分区域变化不显著且有一定比例的区域NDVI呈显著下降趋势。本研究可以为模型数据集选择和预测中国北方草原区植被对未来气候变化的响应提供科学依据。     

近21年青藏高原植被覆盖变化规律

利用GIMMS NDVI遥感数据和GIS技术，结合多种统计、计算方法，定量分析了1982—2002年青藏高原植被覆盖随时间和空间的变化规律，评定了植被变化的自然和人类的影响。结果表明，21年来，青藏高原植被覆盖呈总体增加的变化趋势，平均增长率为3 961.9 km2/年，仅局部出现退化现象，人类对高原植被覆盖未造成破坏性影响。1982—1991年，高原植被呈现良好增加趋势，增加幅度从东部南部向西部北部逐渐减弱，表明由东南向西北逐步减弱的有利气候条件具有经向和纬向的变化规律。1992—2002年，高原中部和西北地区植被呈现退化趋势，强烈退化的地区集中在长江、黄河、澜沧江和怒江的源头地区，显示了高原中部和西北地区的气候条件向不利于植被生长方向转变，高原中部和西北地区植被是响应气候变化的最敏感区。高原植被变化具有7年、3.5年两个显著周期，均为温度所致，表现对温度的变化敏感性。21年期间，高原的8种主要植被类型中有7种类型表现为波动上升的趋势，且寒区旱区植被表现出脆弱性和难恢复性。     

Seasonal dynamic pattern analysis on global FPAR derived from AVHRR GIMMS NDVI

Abstract

The purpose of this paper is to develop Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalised Difference Vegetation Index (NDVI; AVHRR GIMMS NDVI for short) based fraction of absorbed photosynthetically active radiation (FPAR) from 1982 to 2006 and focus on their seasonal and spatial patterns analysis. The available relationship between FPAR and NDVI was used to calculate FPAR values from 1982 to 2006 and validated by Moderate-resolution Imaging Spectroradiometer (MODIS) FPAR product. Then, the seasonal dynamic patterns were analysed, as well as the driving force of climatic factors. Results showed that there was an agreement between FPAR values from this study and those of the MODIS product in seasonal dynamic, and the spatial patterns of FPAR vary with vegetation type distribution and seasonal cycles. The time series of average FPAR revealed a strong seasonal variation, regular periodic variations from January 1982 to December 2006, and opposite patterns between the Northern and Southern Hemispheres. Evergreen vegetation FPAR values were close to 0.7. A clear single-peak curve was observed between 30°N and 80°N – an area covered by deciduous vegetation. In the Southern Hemisphere, the time series fluctuations of FPAR averaged by 0.7° latitude zones were not clear compared to those in the Northern Hemisphere. A significant positive correlation (P<0.01) was observed between the seasonal variation of temperature and precipitation and FPAR over most other global meteorological sites.     

Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981–2006): Impacts of climate changes and human activities

Spatiotemporal variations of Chinese Loess Plateau vegetation cover during 1981–2006 have been investigated using GIMMS and SPOT VGT NDVI data and the cause of vegetation cover changes has been analyzed, considering the climate changes and human activities. Vegetation cover changes on the Loess Plateau have experienced four stages as follows: (1) vegetation cover showed a continued increasing phase during 1981–1989; (2) vegetation cover changes came into a relative steady phase with small fluctuations during 1990–1998; (3) vegetation cover declined rapidly during 1999–2001; and (4) vegetation cover increased rapidly during 2002–2006. The vegetation cover changes of the Loess Plateau show a notable spatial difference. The vegetation cover has obviously increased in the Inner Mongolia and Ningxia plain along the Yellow River and the ecological rehabilitated region of Ordos Plateau, however the vegetation cover evidently decreased in the hilly and gully areas of Loess Plateau, Liupan Mountains region and the northern hillside of Qinling Mountains. The response of NDVI to climate changes varied with different vegetation types. NDVI of sandy land vegetation, grassland and cultivated land show a significant increasing trend, but forest shows a decreasing trend. The results obtained in this study show that the spatiotemporal variations of vegetation cover are the outcome of climate changes and human activities. Temperature is a control factor of the seasonal change of vegetation growth. The increased temperature makes soil drier and unfavors vegetation growth in summer, but it favors vegetation growth in spring and autumn because of a longer growing period. There is a significant correlation between vegetation cover and precipitation and thus, the change in precipitation is an important factor for vegetation variation. The improved agricultural production has resulted in an increase of NDVI in the farmland, and the implementation of large-scale vegetation construction has led to some beneficial effect in ecology. Supported by the National Natural Science Foundation of China (Grant No. 40671019) and the Knowledge Innovation Project of the Institute of Geographical Sciences and Natural Resources Research of Chinese Academy of Sciences     

1982-2013年青藏高原植被动态变化时序分析

针对不同的数据源及时间和空间尺度会使植被覆盖度及其与气象因子影响的结果有所差别这一情况,该文基于青藏高原1982-2012年GIMMS NDVI和2001-2013年MODIS NDVI遥感数据集,结合研究区内12个典型的气象站点数据,进行了青藏高原地区植被覆盖时空动态变化规律及其与气象因子响应的时序分析,并利用重合时间段的数据对比分析了两种传感器在青藏高原地区对植被动态变化监测方面的差异.结果表明:近30年来,青藏高原地区植被呈整体改善趋势,尤其是高海拔地区;不同阶段植被的变化趋势有所不同;两种传感器在反映植被动态变化趋势上差异显著,但两者与气候因子的响应规律相同.     

基于GIMMS 3g NDVI的近30年中国北部植被生长季始期变化研究

基于全球库存建模与绘图研究第三代归一化差值植被指数（GIMMS 3g NDVI）、土地利用和气温降水数据,利用NDVI时间序列谐波分析法(HANTS)重构了中国北部地区原始植被NDVI,用一元六次多项式拟合了植被生长曲线并结合逐像元动态阈值法提取了中国北部地区1983~2012年植被生长季始期并分析了其时空变化及对气温和降水的响应情况。结果表明：①GIMMS 3g NDVI具有较长的时序特征和较好的数据质量,经HANTS时间序列谐波分析后能很好的表现植被生长季曲线特征,可用于后续植被生长季的研究。② 北部地区生长季始期均值主要集中分布在80~150βd之间,全区30βa平均为111.6βd,东北平原、华北平原、河套平原、新疆天山和阿尔泰地区生长季始期早于其它区域。③ 研究时段内北部地区生长季始期总体上呈提前趋势（R²=0.19）,空间上由西北向东北逐渐推移,明显提前的区域主要分布在内蒙古中东部、东北平原、陕西南部和新疆天山的部分地区,明显推迟的区域主要分布在青藏高原高寒地区。④ 因植被类型的不同和区域的差异,生长季始期对气温和降水的响应程度不同,春季气温是影响生长季始期变化的主要自然因素。