近30年中国陆地生态系统NDVI时空变化特征

气候变化已明显影响到陆地植被的活动,但在不同生态系统间存在差异,研究不同陆地生态系统归一化植被指数(NDVI)的时空变化特征,不仅可揭示各生态系统植被活动对气候变化的响应规律,而且可为我国不同生态区制定应对气候变化的策略和生态文明建设提供科学依据。基于1982—2012年GIMMS NDVI3g和中国陆地生态系统类型数据,利用一元线性回归、集合经验模态分解和相关分析等方法,研究了近30年中国各陆地生态系统NDVI的时空变化特征,分析了其与气候事件的关系。结果表明,近30年中国植被活动显著上升,年平均归一化植被指数(ANDVI)的上升幅度为0.0029/10a(P0.05),年最大归一化植被指数(MNDVI)的上升幅度为0.0076/10a(P0.01);植被活动显著增强的区域主要是分布在东部季风区的农田和森林生态系统,显著下降的区域主要是分布于西北的荒漠生态系统和东北的森林生态系统;尽管ANDVI和MNDVI线性趋势的显著性有所差异,但农田、森林、草地和水体与湿地生态系统的NDVI总体呈非稳定的上升趋势,上升过程中伴随着较大波动,荒漠生态系统的NDVI呈下降趋势,植被退化显著;与线性趋势不同,各生态系统植被活动的残差趋势包含"上升—下降"两个阶段,并相继于20世纪90年代到21世纪初发生转折;上述5类生态系统的植被活动存在不同尺度的周期特征,年际周期波动特征(1.9—7.6a)比较显著,而年代际周期(10.7a和22.2a)的显著性相对较差;各生态系统的空间异质性在趋强过程中存在2.1—7.1a的年际周期节律;海洋与大气环流的短周期脉动与各生态系统植被活动的周期性节律有着明显关联,ENSO事件和太阳活动是推动植被活动周期性振荡的重要因素。     

锡林郭勒草原1983～1999年NDVI逐旬变化量与气象因子的相关分析

 陆地生态系统对气候变化的响应关系一直是全球变化研究的热点。大量研究表明表征植被生长状况的遥感植被指数——NDVI与温度、降水的相关性非常高。但这些研究都忽略了NDVI 数据本身的累积性,而这一点对研究较短时间尺度上植被生长与气候因子间的关系尤为重要。因此,本文提出应以NDVI的变化量序列取代一般研究中使用的NDVI时间序列数据。基于该论点,该文采用1983～1999年NOAA/AVHRR的NDVI逐旬变化量数据序列对锡林郭勒盟草原的草原植被生长与气象因子的相互关系进行了研究。研究结果表明：1）NDVI变化量与气象因子之间的相关性最高的时间段为植被生长过程中NDVI增长阶段部分,这一时期草原植被的生长对气候反映最为敏感,在衰败阶段,其相关性比较弱;2）在典型草原,温度和降水与NDVI变化量的相关性随其主要植被类型的不同而不同,在以羊草（Leymus chinensis ）为主的典型草原,温度比降水的影响作用高;而在以克氏针茅（Stipa krylovii）为主的典型草原,降水的影响高于温度;在大针茅（Stipa grandis）为主的草原,两者与NDVI变化量的相关性相差不大。而在荒漠草原,降水是最主要的影响因子,同期的温度作用并不显著; 3）无论是典型草原还是荒漠草原,该地区草原植被的生长对同期的降水反应最为敏感,而非前期。而在荒漠草原以及以旱生性较强的克氏针茅为主的典型草原,温度对NDVI变化量会有较明显的时滞效应;4）在温度升高、降水基本不变的情况下,典型草原和荒漠草原 N DVI变化量对温度的响应能力都有所提高,降水的响应能力则变化不大。     

Fire monitoring in savanna ecosystems using MODIS data: a case study of Kruger National Park, South Africa

The heterogeneity of savanna ecosystems is guaranteed by disturbance events like fires, droughts, floods and browsing and grazing by herbivores. For conservation areas with limited space to preserve biodiversity, fire monitoring is crucial. Long periods of satellite remotely sensed data provide an alternative solution to estimate the distribution of different vegetation types and fire-affected patches over time. This study focusses on the application of MODIS data to detect, identify and delineate fire-affected areas in Kruger National Park (KNP), South Africa, for the period 2001–2003. Fire scars on KNP’s savanna were identified using threshold and supervised classification methods on moderate-resolution imaging spectroradiometer (MODIS) with 500-m resolution and 32-day global composites using a combination of band 1 (red), 2 (NIR, near infrared), 4 (green) and 6 (SWIR, short wave infrared). On identified fire scars, the spectral indexes of albedo, normalised difference infrared index (NDII) and normalised difference vegetation index (NDVI) were extracted. The following four broad habitat types were used for this analysis: riparian woodland, dense woodland, mixed woodland and open-tree savanna. The values of albedo, NDII and NDVI during the dry season (June to October) for different years are lower on fire-affected patches. Mixed woodland is the largest habitat burned with 21%, 43% and 2% of the KNP area affected by fire in 2001, 2002 and 2003, respectively. Riparian woodland is the least affected by fire. The supervised classification method has a greater accuracy for fire scars detection in KNP savannas during the dry season. We conclude that MODIS data can be used successfully for fire monitoring in savanna ecosystems.     

Correlation analysis of Normalized Different Vegetation Index (NDVI)difference series and climate variables in the Xilingole steppe,China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system.It has been demonstrated by many researches that Normalized Different Vegetation Index (NDVI)time series from remotely sensed data,which provide effective information of vegetation conditions on a large scale with highly temporal resolution,have a good relation with meteorological factors.However,few of these studies have taken the cumulative property of NDVI time series into account.In this study,NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors.As a proxy of the vegetation growing process,NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors.This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series,and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale.By using the correlation analysis method,we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia.The results show that:(1)meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase;(2)the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities.In a typical steppe dominated by Leymus chinensis,temperature has higher correlation with NDVI difference than precipitation does,and in a typical steppe dominated by Stipa krylovii,the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference.In a typical steppe dominated by Stipa grandis,there is no significant difference between the two correlations.Precipitation is the key factor influencing vegetation growth in a desert steppe,and temperature has poor correlation with NDVI difference;(3)the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe,however,mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii;(4)the relationship between NDVI difference and temperature is becoming stronger with global warming.     

Spatial analysis of growing season length control over net ecosystem exchange

Using data from 28 flux measurement sites, we performed an analysis of the relationship between annual net ecosystem exchange (NEE) and the length of the carbon uptake period (CUP) (the number of days when the ecosystem is a net carbon sink). The observations suggest a linear correlation between the two quantities. The change in annual carbon exchange per day of the CUP differs significantly between deciduous and evergreen vegetation types. The sites containing vegetation with short‐lived foliage (less than 1 year) have higher carbon uptake and respiration rates than evergreen vegetation. The ratio between mean daily carbon exchange rates during carbon uptake and release periods is relatively invariant (2.73±1.08) across different vegetation types. This implies that a balance between carbon release and uptake periods exists despite different photosynthetic pathways, life forms, and leaf habits. The mean daily carbon sequestration rate for these ecosystems never exceeds the carbon emission rate by more than a factor of 3. Growing season lengths for the study sites were derived from the normalized difference vegetation index (NDVI) of advanced very‐high‐resolution radiometer and from the enhanced vegetation index (EVI) of VEGETATION SPOT‐4. NDVI and EVI were found to be closely related to the CUP, and consequently they also can be used to approximate annual carbon exchange of the ecosystems. This approach has potential for allowing extrapolation of NEE over large areas from remotely sensed data, given a certain amount of ancillary information. This method could complement the currently existing techniques for extrapolation, which rely upon modeling of the individual gross fluxes.     

Estimating woody and herbaceous vegetation cover from time series satellite observations

In this paper we test a method to estimate the tree and grass vegetation cover over Australia from satellite-derived normalized difference vegetation index (NDVI) time series (monthly 1981–91, ≈5 km pixels) observations. The evergreen cover is assumed to track along the base of the NDVI time series, which is assumed to be equivalent to the woody vegetation cover. The base of the NDVI time series is estimated using modifications to a classical econometric model (i.e. time series is the sum of trend, seasonal and random components). Estimates of the average evergreen component during 1982–85 and 1986–89 were generally consistent with known vegetation distributions. Changes in evergreen cover were largely restricted to the south-west and south-east of Australia. Those changes were largely the result of differences in rainfall between the two periods. The proposed method for estimating woody vegetation cover is found to be generally robust. However, there are some regions where the grass (or pasture) is mostly evergreen. Some possible refinements are proposed to handle such cases.     

Correlation analysis of Normalized Different Vegetation Index (NDVI) difference series and climate variables in the Xilingole steppe, China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system. It has been demonstrated by many researches that Normalized Different Vegetation Index (NDVI) time series from remotely sensed data, which provide effective information of vegetation conditions on a large scale with highly temporal resolution, have a good relation with meteorological factors. However, few of these studies have taken the cumulative property of NDVI time series into account. In this study, NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors. As a proxy of the vegetation growing process, NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors. This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series, and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale. By using the correlation analysis method, we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia. The results show that: (1) meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase; (2) the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities. In a typical steppe dominated by Leymus chinensis, temperature has higher correlation with NDVI difference than precipitation does, and in a typical steppe dominated by Stipa krylovii, the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference. In a typical steppe dominated by Stipa grandis, there is no significant difference between the two correlations. Precipitation is the key factor influencing vegetation growth in a desert steppe, and temperature has poor correlation with NDVI difference; (3) the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe, however, mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii; (4) the relationship between NDVI difference and temperature is becoming stronger with global warming. __________ Translated from Acta Phytoecologica Sinica, 2005, 29(5): 753–765 [译自: 植物生态学报]     

中国东部南北样带主要植被类型归一化植被指数对气候变化的响应及不同时间尺度的差异性

植被的动态变化及其与环境的关系已成为全球变化研究的热点问题。陆地样带是进行全球变化驱动因素梯度分析的有效途径。该研究依托中国东部南北样带(NSTEC), 对南北样带不同时间尺度的气候因子和植被活动变化特征进行了分析, 并重点阐述了具有代表性的12种植被类型对气候因子的响应方式。研究结果表明: 南北样带植被的归一化植被指数(NDVI)的变化同时受控于气温和降水, 但是在不同的空间和时间尺度上植被NDVI的响应方式各异。在年时间尺度上, 只有温带落叶灌丛(TDS)的NDVI受气温控制; 而温带禾草草原(TGS)和亚热带和热带针叶林(STCF)的NDVI同时受气温和降水调控。其他植被类型的年NDVI与年平均气温和年总降水量没有直接显著的联系, 而受年内气温变化和降水分配状况的影响更大。在月时间尺度上, NDVI与气温的关系在不同类型植被之间存在很大差异。一般而言, 植被NDVI与前4个月内的气温关系最为密切, 并且从1月份到4月份气温的滞后时长在缩短。其中, 温带针叶林(TCF)、温带落叶阔叶林(TDBF)、TDS、STCF和亚热带热带草丛(STG)等植被类型, 5-8月的NDVI与气温普遍呈负相关关系。草原和灌丛植被类型当月NDVI与当月降水量主要以正相关为主, 而森林类型当月NDVI与当月降水量主要以负相关为主。     

基于MODIS数据的中国三种主要土地类型变化的空间特征分析

人类活动使得土地利用和植被覆盖发生了巨大变化,直接影响着全球气候。本研究通过从2000—2013年对中国三种主要土地利用类型的NDVI变化特征进行了分析,结果表明:(1)14年来,中国三种主要土地利用类型NDVI平均值均有增强的趋势。(2)三种主要土地利用类型中除耕地中的水田,林地中的有林地和草地中的高覆盖草地增长速率不显著外,其他土地类型增长速率均显著。(3)三种土地利用类型均以改善面积大于退化面积,耕地中改善面积占总耕地的64.21%,退化的区域占18.50%;林地改善的区域占总林地的54.21%,退化的区域占20.13%;草地改善的区域占55.53%,退化的区域占18.23%。三种土地类型均有所改善且改善明显的区域主要集中在甘肃以南,陕西以北和东北部分地区。     

Estimation of forest-ecosystem site index using remote-sensed data

The estimation of site index and site quality forms the fundamental theory and basic tools in forest-ecosystem management and silviculture practice. The study on the spatial pattern and temporal dynamics of site index and site quality of forest ecosystem still lacks technological advancement. It is a novel approach for estimating forest productivity in large areas using satellite remote-sensed data. The site-index spatial distribution pattern of spruce (Picea asperata) forest in Songpan-Zhengjiangguan watershed, northwestern Sichuan Province, China, was described using the remote-sensing vegetation index application and the established inverse models. The application potential of the methodology in broad regions and forests using the accuracy assessment was evaluated. The results show that the site index of the spruce forest is in linear correlation with the remote-sensed vegetation indices (normalized difference vegetation index (NDVI) and soil adjust NDVI (TNDVI)), as well as with these inverse models with high accuracy. This study demonstrated that this approach can be used in similar estimation of different forest ecosystems.     

Estimation of forest-ecosystem site index using remote-sensed data

The estimation of site index and site quality forms the fundamental theory and basic tools in forest-ecosystem management and silviculture practice. The study on the spatial pattern and temporal dynamics of site index and site quality of forest ecosystem still lacks technological advancement. It is a novel approach for estimating forest productivity in large areas using satellite remote-sensed data. The site-index spatial distribution pattern of spruce (Picea asperata) forest in Songpan-Zhengjiangguan watershed, northwestern Sichuan Province, China, was described using the remote-sensing vegetation index application and the established inverse models. The application potential of the methodology in broad regions and forests using the accuracy assessment was evaluated. The results show that the site index of the spruce forest is in linear correlation with the remote-sensed vegetation indices (normalized difference vegetation index (NDVI) and soil adjust NDVI (TNDVI)), as well as with these inverse models with high accuracy. This study demonstrated that this approach can be used in similar estimation of different forest ecosystems.     

MODIS植被指数与水稻叶面积指数及叶片叶绿素含量相关性研究

利用光谱分辨率为3nm的ASD FieldSpec UV／VNIR光谱仪获得了2002和2003年水稻整个生长期的高光谱数据,同时对水稻叶面积指数(LAI)和叶绿素含量(CHL．Ｃ)进行了测定,对中分辨率成像光谱仪(MODIS)的增强植被指数(EVI)、归一化植被指数(NＤVI)以及红边位置(REP)与LAI及CHL．C之间的关系进行了研究．结果表明。LAI与冠层光谱在可见光、近红外波段相关性较好,叶绿素含量与冠层光谱在红光波段相关性较好．EVI、REP和LAI之间的相关关系不受水稻覆盖率的影响;NDVI与LAI的相关关系在水稻低覆盖率情况下较好;在水稻高覆盖率情况下。EVI和REP比NDVI与LAI之间的相关关系要好．MODIS-NDVI、EVI及REP与叶片叶绿素含量相关性较好．由此可见,EVI和REP可以有效地监测水稻LAI和CHL．C．     

Normalized difference vegetation index (NDVI) as a predictor of forage availability for ungulates in forest and field habitats

Quantifying available plant biomass is a crucial step towards improving our understanding of herbivore ecology and trophic interactions. Thanks to the development of satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI), ecologists have been provided with indirect estimates of primary production at various temporal and spatial scales. When it comes to forested ecosystems, most mammalian herbivores predominantly rely on the ground vegetation, yet little is known regarding the suitability of NDVI to predict this component of forest vegetation cover. This study compares the relationship between NDVI and ground vegetation biomass in two contrasting habitats (field and forest) in Eastern Poland over the spring and summer seasons (2007–2008). Results indicate that seasonality shapes the relationship between NDVI and ground vegetation biomass for each habitat type. In the field habitat, NDVI and ground vegetation biomass were positively related, with a stronger correlation between the two variables occurring in summer. In the forest habitat, a switch in the direction of the correlation between biomass and NDVI (from positive in spring to negative in summer) was detected. The timing of the switch was related to the timing of full development of tree and shrub leaves (late May–early June). This suggests that the usefulness of NDVI as a predictor of ground vegetation biomass is dependent upon the habitat considered and the targeted season.     

基于ICEEMDAN方法的黄土高原植被覆盖变化及其对气候变化的响应

地理数据和遥感数据的长期序列中包含噪声和周期性波动信息。本研究基于ICEEMDAN方法对黄土高原1982—2015年归一化植被指数(NDVI)、降雨和温度进行逐像元分解,分解后得到的残差项减少了原始数据中的噪声和周期性波动,并利用残差项研究NDVI的变化趋势以及NDVI与气候因子之间的关系。结果表明: 1982—2015年,黄土高原NDVI以上升为主,残差项NDVI变化趋势的显著性(95.9%)大于原始NDVI变化趋势的显著性(72.3%),并且存在一定的空间差异性。温度和降雨的变化可以在很大程度上解释植被覆盖的变化。其中,温度与黄土高原NDVI之间呈极显著正相关的区域占83.7%,极显著负相关区域占13.9%;降雨与黄土高原NDVI之间呈极显著正相关的区域占54.4%,极显著负相关区域占37.2%。黄土高原植被对气候变化的响应存在明显的空间差异性,不同气候因子对不同植被覆盖类型的影响程度不同。总体上,黄土高原生长季不同植被与温度之间的相关性强于降水,温度是影响黄土高原植被覆盖变化的主要因素。     

Time lag and negative responses of forest greenness and tree growth to warming over circumboreal forests

The terrestrial forest ecosystems in the northern high latitude region have been experiencing significant warming rates over several decades. These forests are considered crucial to the climate system and global carbon cycle and are particularly vulnerable to climate change. To obtain an improved estimate of the response of vegetation activity, e.g., forest greenness and tree growth, to climate change, we investigated spatiotemporal variations in two independent data sets containing the dendroecological information for this region over the past 30 years. These indices are the normalized difference vegetation index (NDVI3g) and the tree‐ring width index (RWI), both of which showed significant spatial variability in past trends and responses to climate changes. These trends and responses to climate change differed significantly in the ecosystems of the circumarctic (latitude higher than 67°N) and the circumboreal forests (latitude higher and lower than 50°N and 67°N, respectively), but the way in which they differed was relatively similar in the NDVI3g and the RWI. In the circumarctic ecosystem, the climate variables of the current summer were the main climatic drivers for the positive response to the increase in temperatures showed by both the NDVI3g and the RWI indices. On the other hand, in the circumboreal forest ecosystem, the climate variables of the previous year (from summer to winter) were also important climatic drivers for both the NDVI3g and the RWI. Importantly, both indices showed that the temperatures in the previous year negatively affected the ecosystem. Although such negative responses to warming did not necessarily lead to a past negative linear trend in the NDVI3g and the RWI over the past 30 years, future climate warming could potentially cause severe reduction in forest greenness and tree growth in the circumboreal forest ecosystem.     

The remote sensing approach in broad‐scale phenological studies

Abstract. Satellite imagery provides a unique tool for monitoring seasonal dynamics of the Earth's vegetation on a global scale. The combination of the normalized difference vegetation index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR) with a daily repeat cycle and 1 km spatial resolution makes weather satellites operated by the National Oceanic and Atmospheric Administration very well suited for deriving broad‐scale phenological metrics from satellite images. In this paper, similarities and differences between remotely sensed phenological studies and traditional symphenological studies conducted by ground‐based observations are summarized. Finally, major shortcomings in deriving phenological metrics from NDVI time series are discussed.     

基于雪岭云杉树轮宽度重建过去339年天山中部巴音布鲁克地区夏季NDVI

归一化差异植被指数(NDVI)被广泛应用于植被研究的各个领域,但由于观测时长较短,难以满足长时间尺度的研究需要。基于巴音布鲁克地区雪岭云杉建立了树轮宽度年表(STD),计算年表和NDVI同气象观测数据的相关系数。结果表明:树轮宽度指数和NDVI均与同时段的气象数据具有显著相关。结合宽度年表与6—8月NDVI间的显著正相关(r=0.7,P＜0.01,n=38),使用回归模型重建了研究区过去339年的夏季(6—8月)NDVI变化序列,在1680—2018年,重建序列有4个高植被覆盖时段(1738—1765、1786—1798、1964—1973和2000—2018年)和5个低植被覆盖时段(1690—1714、1825—1834、1850—1880、1895—1920和1945—1955年)。重建结果也反映了天山中部水文气候。与周边重建的对比显示,当开都河径流量增加,且研究区处于较为潮湿的环境时,植被覆盖相对较高,反之植被覆盖偏低。重建序列的极值也捕捉了历史文献中一系列自然灾害。混合单粒子拉格朗日综合轨迹模型(HYSPLT)后向轨迹模型和风场分析表明,NDVI异常受到西风带来的降水影响。     

Impacts of changes in climate variability on regional vegetation in China: NDVI-based analysis from 1982 to 2000

Three methods were used to distinguish the characteristics of changes in climate variability and normalized difference vegetation index (NDVI) during the period from 1982 to 2000 in China. Great changes in climate variability and an increased trend in NDVI were observed. The changes in precipitation variability were greater than the changes in temperature variability in each month, which is attributed to changes in the monsoon system in East Asia. The abrupt changes in climate and NDVI were more significant in 1983 than in the other years due to the impacts of El Niño/Southern Oscillation (ENSO). Using these results, the influences of changes in climate variability on vegetation were studied in the whole nation, and eight regions were defined according to the vegetation division map of China. The results show that abrupt climate changes at a small scale cannot cause abrupt NDVI changes directly. At a nationwide level, over a longer time scale the persistence of above/below average temperature determines the changes in NDVI; at a shorter time scale, changes in the magnitude of precipitation influence NDVI significantly. Such regional climate variability affects vegetation in different ways owing to the diversity of vegetation types, climatic conditions and topography of the land.     

植被指数的地形效应研究进展

植被指数是定性、定量评估绿色植被的关键指标,已经广泛应用于地表植被的监测.森林多分布在地形复杂山区,利用植被指数进行森林植被信息反演时地形对其影响较大.本文从几何光学模型原理分析了冠层反射率的地形效应,分析比较完全比值型植被指数(简单比值植被指数SR、归一化植被指数NDVI和湿度调整植被指数MAVI)、非完全比值型植被指数(增强型植被指数EVI和土壤调整植被指数SAVI)、非比值型植被指数(减化比值植被指数RSR、修正归一化植被指数MNDVI和绿度植被指数GVI),以及地形调节植被指数TAVI对地形的响应,试图为复杂地形山区选取植被指数提供参考.最后分析了植被指数地形效应研究的不足并对未来发展进行展望.     

Influence of land-use types and climatic variables on seasonal patterns of NDVI in Mediterranean Iberian ecosystems

Question: What is the influence of management on the functioning of vegetation over time in Mediterranean ecosystems under different climate conditions? Location: Mediterranean shrublands and forests in SE Iberia (Andalusia). Methods: We evaluated the Normalized Difference Vegetation Index (NDVI) for the 1997-2002 time series to determine phenological vegetation patterns under different historical management regimes. Three altitudinal ranges were considered within each area to explore climate × management interactions. Each phenological pattern was analysed using time series statistics, together with precipitation (monthly and cumulative) and temperature. Results: NDVI time series were significantly different under different management regimes, particularly in highly transformed areas, which showed the lowest NDVI, weakest annual seasonality and a more immediate phenological response to precipitation. The NDVI relationship with precipitation was strongest in the summer-autumn period, when precipitation is the main plant growth-limiting factor. Conclusions: NDVI time series analyses elucidated complex influences of land use and climate on ecosystem functioning in these Mediterranean ecosystems. We demonstrated that NDVI time series analyses are a useful tool for monitoring programmes because of their sensitivity to changes, ease of use and applicability to large-scale studies.