青藏高原1982~2015年FPAR时空变化分析

植被吸收利用太阳光合有效辐射比率反映了植被固碳释氧能力,根据青藏高原GIMMS NDVI3g（1982~2015年）和MODIS NDVI（2001~2015年）数据,采用非线性半理论半经验模型进行FPAR反演及时空变化分析。结果表明：①2001~2015年GIMMS NDVI3g和MODIS NDVI反演FPAR在空间分布上具有较高的一致性,相关系数为0.82（P<0.01）,年际变化趋势一致至少6年的区域占80%;②青藏高原FPAR受坡度和海拔影响较大,其中15~35坡度FPAR变化最快,700~2 100 m海拔区间FPAR值最大;不同坡向对应的FPAR除南坡方向偏低外其他方向差异不大。③1982~2015年青藏高原四季FPAR时空变化研究中,冬季FPAR年际变化最明显,约78.5%的区域表现为增长趋势;秋季FPAR下降区域最多,但超过71.5%区域变化不显著;④基于MODIS NDVI和GIMMS NDVI两数据反演的所有植被类型的FPAR都在2012年间出现小幅度下降趋势,且不同植被类型FPAR的年际变化趋势各不相同。     

不同等级石漠化区MODIS-NDVI与MODIS-EVI对比分析

利用2001～2010年10 a的MODIS资料,比较分析广西喀斯特不同等级石漠化区MODIS\|NDVI和MODIS\|EVI的时间变化特征差异,利用全时间序列及16 d10 a均值序列分析NDVI和EVI之间的相关关系,比较线性及对数相关模型对两种植被指数相关关系的拟合效果,结果表明:石漠化等级由重度到潜在,两者之间的差值也随着植被覆盖度的增加而增大,植被覆盖度越低,NDVI和EVI所表征的植被变化特征越相似。NDVI的峰值出现时间多晚于EVI且其反映的植被变化趋势与实况更吻合,但其NDVI偏高;各等级石漠化的两种时间序列NDVI与EVI的对数相关关系优于线性相关,两种植被指数的相关性随着植被覆盖度的降低而增大,但全时间序列中轻度、中度石漠化相关性变化规律与16 d 10 a均值序列相反。     

北美永久湿地NDVI时序变化特征分析——以哈德逊湾西南湿地区域为例

基于2001~2009年MCD12Q1数据,提取哈德逊湾西南区连续9年都是湿地的区域作为研究区。利用1982~2006年GIMMS NDVI数据,分析此区域25 a来NDVI季节、年际变化,并与温度、降水数据进行相关分析。结果显示:研究区域NDVI季节变化呈现单峰曲线形状,夏季达到最大值。1982~2006年NDVI年际变化在春秋两季与全年平均NDVI年际变化都呈增加趋势,夏季与冬季趋势则相反。此区域NDVI季节变化与温度、降水呈极显著相关,冬季NDVI年际变化与温度以及夏季NDVI年际变化与降水的相关性不显著,而其他各时期NDVI年际变化与温度、降水也都呈极显著相关。然而,由于人类活动及其他因素的影响,此区域的NDVI年际变化驱动还存在一定的不确定性。     

基于植被指数季节变化曲线的年总初级生产力估算

针对年总初级生产力估算的研究,提出了一种参数简单、误差较小的估算方法。以"三北"防护林工程区域各类型植被为研究对象,获取2010年研究区全年时序的MODIS植被指数并构建植被指数季节变化曲线,建立该曲线积分ΣVIs与MODIS GPP产品的拟合关系,并研究各植被类型GPP估算适用的植被指数时间序列曲线积分ΣVIs。结果表明:①ΣVIs适用于估算研究区年总GPP并与MODIS GPP在p0.01置信水平下,显著相关;②ΣNDVI估算郁闭灌丛、稀疏灌丛、草地、耕地以及荒地或稀疏植被GPP的效果要优于ΣEVI和ΣEVI2,但在森林及其他植被类型方面要比ΣEVI或ΣEVI2的精度低;③由于NDVI在高LAI地区趋于饱和,使ΣNDVI估算高LAI植被类型GPP的误差较大,而利用ΣEVI和ΣEVI2估算高LAI植被类型的GPP具有较好的精度,并且EVI2相对于EVI减少了来自于蓝光波段的限制,能够更好地应用于长时间序列GPP研究。     

MODIS NDVI与MODIS EVI的比较分析

MODIS NDVI与MODIS EVI是目前应用比较广泛的植被指数,MODIS EVI是对NDVI的发展和延续,从植被指数计算公式和合成方法两方面做了改进。具体表现在:避免了MODIS NDVI在植被高覆盖区易饱和的问题,考虑了土壤背景对植被指数的影响,对气溶胶等残留做了进一步校正,采用BRDF/CV-MVC合成方法保证了合成采用最佳像元。EVI时间序列相较于NDVI时间序列季节性更明显,能够更好地反映高植被覆盖区的季节性变化特征,并且很少有突降现象,时间序列曲线较平滑。EVI的这些优势为高覆盖植被物候特征的季节性变化监测提供了新的思路。     

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。     

基于NDVI序列影像的植被覆盖变化研究

归一化植被指数NDVI是地表植被覆盖特征的重要指标之一。以新疆石河子地区2003~2006年MODIS遥感数据反演的NDVI时间序列影像为例,分析研究了植被长势的年内和年际变化,将植被长势的年内变化和年际变化分为比前一年(月)好、比前一年(月)稍好、与前一年(月)持平、比前一年(月)稍差和比前一年(月)差5个等级,得到年内和年际间植被长势的动态分布图,从植被长势分布图中NDVI的变化可以看出年际和年内植被长势的变化。并应用变化矢量分析法对2003~2006年石河子地区NDVI的变化强度进行了分析,获得了植被覆盖变化强度分布情况,研究结果表明4 a内石河子地区植被覆盖未发生大的变化,植被系统基本稳定。     

基于多源数据的禹城农田生态系统冬小麦生育期识别方法比较研究

多源数据与其技术方法逐渐被应用于植被物候的研究当中,但基于多源数据物候识别方法间的差异性比较及定量化评估工作还有待加强。以山东禹城农田生态系统为例,探讨了基于多源数据,NDVI、EVI、数字相机图片、碳通量数据(NEE)以及人工实测数据获取的冬小麦主要生育日期的结果进行差异比较及定量化评估。结果表明:①通过碳通量数据获取的主要生育日期的计算结果与人工实测结果最接近,各阶段差异均<3 d;通过数字相机图片获取的结果仅次于通过碳通量数据获取的结果,而通过遥感数据NDVI、EVI获取的结果与人工实测结果差距最大;②通过NDVI、EVI两种数据获取的冬小麦主要生育期结果具有极显著的相关性,最高达到R²=0.857(P<0.001);③基于多源数据获取的冬小麦主要生育期的计算结果,均显示出禹城站冬小麦返青期提前,蜡熟期推迟,生长季长度变长的年际变化特征。     

2001~2010年松木希错流域植被动态变化遥感研究

遥感在区域植被变化研究中具有十分重要的作用,能为大面积监测植被状况的演化过程提供技术支持。NDVI在高植被覆盖地区存在过饱和现象,对稀疏地区的植被变化尤其敏感。以古里雅冰帽南部的松木希错流域植被相对稀疏区域为研究区,基于MODIS NDVI数据和逐月气象观测数据,以及RS和GIS平台,对该区域2001~2010年主要植被变化趋势进行了初步研究,并对植被变化与气候驱动因子的关系进行了分析和探讨。结果表明:① 2001~2010年间该区域的植被活动有加强趋势;② NDVI表明研究区植被生长季较短(5~9月),NDVI浮动区间为0.11～0.13,低于全国水平(0.3～0.35),也低于全球稀疏灌丛的平均水平(0.2～0.4);③NDVI与年均气温整体上呈正相关,而与年降水量相关性不强。表明近年来持续升温是影响该区域植被活动加强的最主要原因。     

基于不同植被指数的重庆地区物候参数提取对比研究

基于不同植被指数提取物候参数是分析长时间物候变化的重要基础。以多云雾的重庆地区为例，使用2010～2019年MODIS NDVI/EVI/EVI2共3种长时序的植被指数数据，通过D-L滤波方法分析了不同植被指数特征；并使用动态阈值法和趋势分析法，对比研究了基于3种植被指数提取的物候参数结果及其随不同地形因子的分异规律，结果如下：(1)EVI和EVI2的时间序列拟合曲线比NDVI的拟合曲线更加平滑，3种植被指数原始值与拟合值的差值主要分布为NDVI(0.05～0.18)、EVI(0.03～0.11)、EVI2(0.03～0.1)。(2)基于3种植被指数提取的物候参数在空间分布和变化趋势上呈现一致性，其中EVI和EVI2提取的植被指数参数皆相近，相差5d之内占比79%以上，SOSEVI2变化显著性区域所占比面积最高（16.36%）,SOSNDVI最低为12.37%。(3)SOS随海拔升高而推迟，EOS随海拔升高先延后再提前，LOS随海拔升高先延长后缩短，且EOSNDVI、LOSNDVI随着海拔增加分别与EOSEVI/EOSEVI2、LOSEVI/LOSEVI2差异增大，不同植被类型上，EV...     

Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data

AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference vegetation Index) data is available from 1981 to present time. The global coverage 8 km resolution 15-day composite data set has been used for numerous local to global scale vegetation time series studies during recent years. Several aspects however potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. More recent NDVI data sets from both Terra MODIS and SPOT VGT data are considered an improvement over AVHRR and these products in theory provide a possibility to evaluate the accuracy of GIMMS NDVI time series trend analysis for the overlapping period of available data. In this study the accuracy of the GIMMS NDVI time series trend analysis is evaluated by comparison with the 1 km resolution Terra MODIS (MOD13A2) 16-day composite NDVI data, the SPOT Vegetation (VGT) 10-day composite (S10) NDVI data and in situ measurements of a test site in Dahra, Senegal. Linear least squares regression trend analysis on eight years of GIMMS annual average NDVI (2000-2007) has been compared to Terra MODIS (1 km and 8 km resampled) and SPOT VGT NDVI data 1 km (2000-2007). The three data products do not exhibit identical patterns of NDVI trends. SPOT VGT NDVI data are characterised by higher positive regression slopes over the 8-year period as compared to Terra MODIS and AVHRR GIMMS NDVI data, possibly caused by a change in channels 1 and 2 spectral response functions from SPOT VGT1 to SPOT VGT2 in 2003. Trend analysis of AVHRR GIMMS NDVI exhibits a regression slope range in better agreement with Terra MODIS NDVI for semi-arid areas. However, GIMMS NDVI shows a tendency towards higher positive regression slope values than Terra MODIS in more humid areas. Validation of the different NDVI data products against continuous in situ NDVI measurements for the period 2002-2007 in the semi-arid Senegal revealed a good agreement between in situ measurements and all satellite based NDVI products. Using Terra MODIS NDVI as a reference, it is concluded that AVHRR GIMMS coarse resolution NDVI data set is well-suited for long term vegetation studies of the Sahel-Sudanian areas receiving < 1000 mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian-Guinean zones should be done with certain reservations.     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem-atmosphere CO₂ exchange in the Amazon region.     

遥感干旱指数在洛川苹果干旱监测中的适用性分析

目前对苹果干旱研究较少且主要运用站点数据,对空间信息表征有限,遥感干旱指数可用于大范围干旱时空动态监测,但在苹果干旱监测中的适用性还有待研究.基于2014～2018年MODIS反射率、地表温度以及地表覆被数据,结合土壤湿度数据和野外调查资料,分析洛川苹果区温度植被干旱指数(TVDI)、归一化植被水分指数(NDWI)、植...     

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R² varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R²=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.     

Analysis of the Spatio-temporal Variation in FPAR of the Tibetan Plateau from 1982 to 2015

The absorbed and utilized Fraction of Photosynthetically Active Radiation(FPAR) reflects the capacity of carbon fixation and oxygen release by vegetation, which may vary over space and time in large scale. Analysis of spatial-temporal variation in FPAR is an important topic of plant ecology. Based on GIMMS NDVI3g (1982~2015) and MODIS NDVI (2001~2015) data in the Tibetan plateau, here we used the nonlinear, semi-theoretical and semi-empirical models to inverse and analyze the spatial and temporal variation in FPAR. The results showed that (1) The spatial distributions of FPAR derived from GIMMS NDVI3g and MODIS NDVI were highly consistent, with the correlation coefficient being 0.82 (P<0.01). The area in which the trends of inter-annual change in the two inversion data were consistent for at least 6 years made up 80% of the studying area. (2) FPAR in Tibetan Plateau was greatly affected by slope and altitude. Changes in FPAR were fastest at slopes of 15~35 degrees and highest at altitude of 700~2 100 m. The effect of slope direction on FPAR was limited. There was little difference in FPAR among different slope directions except for the south where the FPAR was relatively lower. (3)The FPAR data from 1982 to 2015 demonstrated seasonal variation. The inter-annual variation in FPAR was most significant in winter, in which FPAR in about 78.5% of the area increased. FPAR declined most significantly in the fall. (4) FPAR derived from both the MODIS NDVI and GIMMS NDVI data demonstrated a small, temporary decline in 2012. The trend of inter-annual variation in FPAR was largely different among different vegetation types. In conclusion, the FPAR data from 1982 to 2015 in the Tibetan plateau demonstrated both spatial and seasonal variation, which may have important implications for further studies concerning climate and environmental changes in the region.     

Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems

Northern Arizona ecosystems are particularly sensitive to plant-available moisture and have experienced a severe drought with considerable impacts on ecosystems from desert shrub and grasslands to pinyon-juniper and conifer forests. Long-term time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used to monitor recent regional vegetation activity and temporal patterns across various ecosystems. Surface air temperature, solar radiation and precipitation are used to represent meteorological anomalies and to investigate associated impacts on vegetation greenness. Vegetation index anomalies in the northern Arizona ecosystem have a decreasing trend with increasing surface air temperature and decreasing precipitation. MODIS NDVI and EVI anomalies are likely sensitive to the amount of rainfall for northern Arizona ecosystem conditions, whereas inter-annual variability of surface air temperature accounts for MODIS NDVI anomaly variation. The higher elevation area shows the slow vegetation recovery through trend analysis from MODIS vegetation indices for 2000–2011 within the study domain and along elevation.