Analysis of monotonic greening and browning trends from global NDVI time-series

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.     

Depicting the Asymmetries of Vegetation Phenology over Northeast China Using Remote Sensing NDVI Dataset

Vegetation phenology is an important ecological indicator for global climate change.Plant greenup phenology in the spring time has been well studied,whereas autumn phenology and its asymmetry with spring phenology still remain unclear.Here,the GIMMS NDVI3g dataset for Northeast China was applied to extract the key phenological parameters during plant growth process,then three phenological asymmetry indices were defined according to the difference between greenup rate and senescence rate(AsyR),growth length in spring and autumn(AsyL),mean vegetation greenness index in spring and autumn(AsyV).First,plant growing curve was fitted with double logistic function and the phenological parameters was calculated.Second,the spatiotemporal pattern of asymmetry indices was explored.The results indicate that the three phenological asymmetry indices show a significant interannual variability and a time cycle of around ten years.The direction of amplitude for AsyV and AsyL was opposite with that of AsyR.Three indices could depict the phenological asymmetries from various perspectives and have a degree of uncertainty.The landscape pattern for AsyV and Asy R is similar.AsyV and AsyR show a capability of distinguishing cropland and natural vegetation cover.AsyL reflects a complex spatial distribution.Phenological asymmetries reveal that coniferous forest and broad-leaved forest present a dominant control of senescence vegetation activities.These natural vegetation commonly show a growth feature of rapid growth in spring and slow decrease in autumn.Cropland exhibits a slowly growing rate in spring and a rapid decrease in autumn.Phenological asymmetry is not significant in grassland area.Phenological asymmetry could enhance our knowledge on ecosystem carbon sink.In a practical way,phenological asymmetry could serve as a useful tools in vegetation type classification,agricultural investigation and plant ecosystem management.     

Comparison of cloud-reconstruction methods for time series of composite NDVI data

Land cover change can be assessed from ground measurements or remotely sensed data. As regards remotely sensed data, such as NDVI (Normalized Difference Vegetation Index) parameter, the presence of atmospherically contaminated data in the time series introduces some noise that may blur the change analysis. Several methods have already been developed to reconstruct NDVI time series, although most methods have been dedicated to reconstruction of acquired time series, while publicly available databases are usually composited over time. This paper presents the IDR (iterative Interpolation for Data Reconstruction) method, a new method designed to approximate the upper envelope of the NDVI time series while conserving as much as possible of the original data. This method is compared quantitatively to two previously applied methods to NDVI time series over different land cover classes. The IDR method provides the best profile reconstruction in most cases. Nevertheless, the IDR method tends to overestimate low NDVI values when high rates of change are present, although this effect can be lowered with shorter compositing periods. This method could also be applied to data before compositing, as well as to reconstruct time series for other biophysical parameters such as land surface temperature, as long as atmospheric contamination affects these parameters negatively.     

Analysis of trends in the Sahelian ‘rain-use efficiency’ using GIMMS NDVI, RFE and GPCP rainfall data

Rain-use efficiency (RUE; the ratio of vegetation productivity to annual precipitation) has been suggested as a measure for assessing land degradation in arid/semi-arid areas. In the absence of anthropogenic influence, RUE has been reported to be constant over time, and any observed change may therefore be attributed to non-rainfall impacts. This study presents an analysis of the decadal time-scale changes in the relationship between a proxy for vegetation productivity (ΣNDVI) and annual rainfall in the Sahel-Sudanian zone of Africa. The aim is to test the quality of data input and the usefulness of both the RUE approach and an alternative method for separating the effects on vegetation productivity of rainfall change and human impact. The analyses are based on earth observation of both rainfall (GPCP (Global Precipitation Climatology Project), 1982-2007 and RFE (Rainfall Estimate) (1996-2007)) and ΣNDVI (AVHRR GIMMS NDVI dataset, 1982-2007). It is shown that the increase in ΣNDVI has been substantial in the Sahel-Sudanian zone over the 1982-2007 period, whereas for the period 1996-2007 the pattern of ΣNDVI trends is more complex. Also, trend analysis of annual rainfall from GPCP data (2.5° resolution) and RFE data (0.1° resolution) suggests that rainfall has increased over both periods. Further it is shown that RUE values are highly correlated to rainfall, undermining the use of earth observation (EO)-based RUE (using ΣNDVI) as a means of separating rainfall impacts from other factors. An alternative method identify temporal trends in residuals of ΣNDVI, after regressing it against annual rainfall, is tested, yet is shown to be useful only where a high correlation between ΣNDVI and annual rainfall exists. For the areas in the Sahel-Sudanian zone for which this condition is fulfilled, trend analyses suggest very limited anthropogenic land degradation in the Sahel-Sudanian zone.     

Effects of atmospheric variation on AVHRR NDVI data

The AVHRR (Advanced Very High Resolution Radiometer) series of instruments has frequently been used for vegetation studies. The 25+ year record has enabled important time-series studies. Many applications use NDVI (Normalized Difference Vegetation Index), or derivatives of it, as their operational variable. However, most AVHRR datasets have incomplete atmospheric correction, because of which there is considerable, but largely unknown, uncertainty in the significance of differences in NDVI and other short wave observations from AVHRR instruments.The purpose of this study was to gain better understanding of the impact of incomplete or lack of atmospheric correction in widely-used, publicly available processed AVHRR-NDVI long-term datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AERONET (AErosol RObotic NETwork) sunphotometer sites in 1999. The datasets included in this study are: TOA (Top Of Atmosphere) that is with no atmospheric correction; PAL (Pathfinder AVHRR Land); and an early version of the new LTDR (Long Term Data Record) NDVI. The other publicly available datasets like GIMMS (Global Inventory Modeling and Mapping studies) and GVI (Global Vegetation Index) have atmospheric error budget similar to that of TOA, because no atmospheric correction is used in either processing stream. Of the three datasets, LTDR was found to have least errors (accuracy = 0.0064 to − 0.024, precision = 0.02 to 0.037 for clear and average atmospheric conditions) followed by PAL (accuracy = − 0.145 to − 0.035, precision = 0.0606 to 0.0418), and TOA (accuracy = − 0.0791 to − 0.112, precision = 0.0613 to 0.0684). It was also observed that temporal maximum value compositing technique does not cause significant improvement of precision in regions experiencing persistently high AOT (Aerosol Optical Thickness).     

西南河流源区归一化植被指数时空动态变化及其与气候因子的关系

为揭示气候变化对西南河流源区植被生态系统的影响,基于1982~2012年GIMMS NDVI(归一化植被指数)第三代数据及降雨、温度、潜在蒸散发等气象数据,运用趋势分析法和相关分析法,探讨归一化植被指数时空动态变化及其与气候因子的关系。结果表明,1982~2012年间,西南河流源区归一化植被指数总体呈不显著增加趋势,但存在显著的空间差异性,归一化植被指数增加的区域占研究区总面积的66.72%,归一化植被指数减少的区域占33.28%;研究区植被覆盖动态变化在流域尺度上呈显著的空间差异性;温度是影响西南河流源区归一化植被指数变化的最主要因素,降雨次之,潜在蒸散发和日照时数的影响较小。     

基于MODIS和AVHRR数据源的东北地区植被NDVI变化及其与气温和降水间的相关分析 

基于逐像元一元线性回归模型,应用MODIS NDVI数据对AVHRR-GIMMS NDVI进行时间序列拓展,拓展序列通过一致性检验,基于所建立的1982~2009年植被年最大NDVI数据集,在GIS平台上进行了植被NDVI变化和NDVI与年平均气温、年降水量之间的相关分析。研究结果表明:过去28 a间,植被年最大NDVI呈3个变化阶段:1982~1992年呈小幅上升趋势,1992~2006年呈缓慢下降趋势,2006~2009年呈缓慢回升态势。由空间变异分析得出NDVI变化相对大的区域主要分布在内蒙干旱和半干旱区。21世纪初和20世纪90年代相对于80年代NDVI值升高,3个阶段平均NDVI变化幅度为±0.3。 20世纪初,赤峰地区以及松嫩平原西部地区植被NDVI呈轻度增加的面积占全区6.45%。植被年最大NDVI与年平均气温、年降水量相关性空间差异明显。偏相关系数绝对值,气温大于降水的像元数占54%;综合分析,较降水而言,气温是东北全区植被年最大NDVI的主控影响因子。对于不同植被类型年最大NDVI,受气温影响强度由大到小依次为:森林>草地>沼泽湿地>灌丛>耕地;受降水影响按草地>耕地>灌丛>沼泽湿地>森林依次减弱。     

黑龙江省植被指数时空变化及其对气候因子的响应

为探究全球气候变暖条件[HJ1.8mm]下黑龙江省覆被变化特征，基于1982—2015年GIMMS（global inventory modeling and mapping studies） NDVI3g（normalized difference vegetation index 3rd generation）数据，利用趋势分析、Hurst指数法和相关性分析法，分析黑龙江省近34年的植被指数时空变化规律和未来趋势，[JP2]同时结合省内34个气象站点数据，分析归一化植被指数（normalized difference vegetation index，NDVI）与气温、降水及蒸散量的响应特征。结果表明：黑龙江省3种植被覆盖类型的NDVI均呈缓慢上升趋势，其中，林地和草地NDVI增速为0.004/10 a，农田NDVI增速为0.002/10 a；植被覆盖的整体变化先升高后降低，其中最大值出现在2014年，最小值出现在1984年；全省植被覆盖空间格局呈东高西低的分布特征，以“大兴安岭—伊春—鹤岗—佳木斯—双鸭山”为界限，西南地区植被覆盖较为丰富，而东北部地区植被覆盖较少；全省植被变化的同向特征强于反向特征，持续性区域和反持续性区域分别占总面积的97.65%和2.35%；植被覆盖变化与气温相关性高于降水，蒸散与植被指数的相关性在年均和生长季差异性较显著，气温是影响生长季植被覆盖变化的主要气候因子。研究结果可为气候变化背景下寒区土地利用/覆被变化研究提供一定的理论支撑。     

陕西彬长矿区NDVI3g(1982-2013)变化趋势及气候响应

过去30 a,全球归一化差值植被指数(NDVI)呈增加趋势,植被返青期(SOS)提前,枯黄期(EOS)滞后,生长期(LOS)延长。作为人类经济活动最密集的工矿区,其生态环境问题是环境与发展的焦点问题之一,在全球变化的背景下,研究矿区及周边地区NDVI趋势,能很好地反映区域尺度上非自然生态区植被变化及对全球变化的响应。基于GIMMS AVHRR NDVI3g长时间序列遥感影像,利用IDL编程、线性回归、趋势拟合提取研究区1982—2013年植被覆盖的物候信息,分析矿区、缓冲区(10 km,20 km)及生态校验区月度、季度、年际变化情况,推算出植被SOS,EOS与LOS长度变化趋势,并结合气象观测记录分析气候驱动因子(降水、气温)与NDVI变化相关性。时序相关性分析表明:32 a彬长矿区年均NDVI总量随时间序列缓慢升高,32 a增长百分比达13. 31%;生态校验区NDVI总量随时间序列升高趋势明显,32 a增长百分比达19. 45%;受人类活动影响,矿区NDVI阶段增长百分比明显低于生态校验区。空间相关性分析表明:彬长矿区NDVI原始基底值高于校验区,植被SOS提前6±3 d,EOS滞后5±3 d,LOS延长11±3 d;生态校验区植被SOS提前3±3d,EOS滞后3±3 d,LOS延长6±3 d;矿区植被LOS延长天数高于生态校验区5 d。气候相关性分析表明:彬长矿区处于半湿润气候区,植被对气温变化的响应高于降水,植被生长对降水有2～3 a的滞后性,NDVI的增加与该区气温升高、降水减少的共同作用有关。研究认为,采矿活动使得矿区及其周边地区植被活动放缓,年均NDVI增长率明显低于生态校验区;在全球变化作用下,植被LOS延长的基础上,人类活动使得矿区植被LOS被再度延长。     

基于NDVI数据的江苏省植被物候变化及其影响因子分析

以气候变暖为主要特征的全球气候变化与生态系统的相互作用成为影响可持续发展的重要因素。植被作为陆地生态系统的主要组成部分,在生态环境评价及碳水循环等方面具有重要作用。以江苏省为研究区,利用长时间序列的GIMMS NDVI3g数据集和气象数据,采用Logistic函数法提取该区域过去34 a(1982~2015年)植被生长期物候(Start Of Season SOS,End Of Season EOS)变化的时空分布特征,并用相关性分析法定量确定主要气象因子(温度、降水)对物候变化的贡献。结果表明：①空间上,从江苏省南部到北部,SOS呈递增趋势,EOS呈递减趋势;②时间上,大部分(83.1%)像元的SOS提前,主要分布在江苏省中部及北部地区,大多提前1~2 d/a,69.2%像元的EOS延后,大多延后0~1 d/a;③植被生长期开始SOS/EOS对温度、降水有明显响应,70.5%像元的SOS与温度呈负相关,主要位于江苏省北部及少部分南部地区,55.5%像元的SOS与降水呈负相关,55.2%像元的EOS与温度呈正相关,71.2%像元的EOS与降水呈负相关。整体上,温度的升高导致生长期提前,降水对SOS具有双向作用,秋季物候的影响因子更为复杂,温度和降水的变化并不能导致EOS的提前或者推迟。本研究加深对气候变化与植被生态系统相互作用过程的认识,为未来植被及气候变化分析提供参考。