湖南省MODIS遥感植被指数的时空变化

采用最大值合成法,以MODIS 250 m分辨率图像为基础,提取湖南省2005年逐月植被指数值.通过月植被指数对比分析,将湖南省分为6个区描述其空间分布特征.利用5个分布均匀的气象站观测的月降水量和月平均气温数据,分析了湖南省植被指数的时相变化特征.结果表明：湖南省MODIS植被指数空间分布与植被覆盖率呈正相关,且具有一定的地域性;MODIS植被指数随季节变化,其月平均植被指数曲线形似开口向下的二次抛物线,最大值出现在7月份;MODIS月平均EVI值小于MODIS月平均NDVI值;植被指数的季节变化受温度影响较大,并且随着纬度的降低,温度对植被指数的影响力下降;MODIS EVI的变化规律比MODIS NDVI更加明显,其二次曲线更为光滑,月平均值由低逐渐上升到最大值,再逐渐降低,而后者的曲线在最大值两侧有细微波动现象.     

基于多时相MODIS数据的东北地区一季稻面积提取

利用2007-2008年MODIS/Terra陆表反射率数据提取的归一化植被指数(NDVI)、增强植被指数(EVI)及陆表水分指数(LSWI),分析了东北地区水稻、旱田、林地、湿地和水体5种不同下垫面在作物生长季的动态变化,同时结合水稻发育期观测数据,建立了东北地区一季稻面积提取模型,并制作了东北地区水稻种植面积分布图.以辽宁盘锦为试验区,利用ALOS数据提取结果对该模型进行了试验,提取精度达到89.5％,结果表明该方法可以较高精度进行大范围的一季稻种植面积提取.     

Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data

Monitoring soil respiration (R_s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R_s to the regional scales. In this study, we estimated the spatial distribution of R_s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R_s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R_s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R_s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R_s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R_s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R_s predictor model was validated by comparing it with the field measured R_s using an independent dataset. The TM-calibrated MODIS-estimated R_s was within an accuracy of field measured R_s with R² of 0.78 and root mean square error of 1.45 gC m⁻² d⁻¹. At the peak growing season of alpine grasslands, R_s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R_s in this study.     

基于遥感图像不同辐射校正水平的植被覆盖度估算模型

选用南京市SPOT 5 HRG图像的地物反射率(PAC)、表观反射率(TOA)和灰度值（DN）影像,提取了4种植被指数（VI）,即归一化植被指数（NDVI）、转换植被指数（TVI）、土壤调节植被指数（SAVI）和修正的土壤调节植被指数（MSAVI）,与地面实测的植被覆盖度进行了回归分析,并建立了36个VI-VFC关系模型.结果表明：在所有模型中,基于PAC级影像提取的NDVI和TVI的3次多项式模型最优;其次为基于DN级影像提取的SAVI和MSAVI的3次多项式模型,在VFC>0.8时其精度略高于前两种模型.这4个模型在植被中等密集区域（VFC＝0.4～0.8）的精度高于植被稀疏区域（VFC=0～0.4）.所建模型可通过中间模型的联结,进行推广使用.在基于VI-VFC关系建模过程中,基于遥感影像不同辐射校正水平提取植被指数,有利于充分挖掘遥感影像信息,进而提高VFC估算的精度.     

The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls

Aims Grassland is the most widely distributed vegetation type on the Xizang Plateau. Accurate remote sensing estimation of the grassland aboveground biomass (AGB) in this region is influenced by the types of vegetation indexes (VIs) used, the grain size (resolution) of the remote sensing data and the targeted ecosystem features. This study attempts to answer the following questions: (i) Which VI can most accurately reflect the grassland AGB distribution on the Xizang Plateau? (ii) How does the grain size of remote sensing imagery affect AGB reflection? (iii) What is the spatial distribution pattern of the grassland AGB on the plateau and its relationship with the climate?Methods We investigated 90 sample sites and measured site-specific AGBs using the harvest method for three grassland types (alpine meadow, alpine steppe and desert steppe). For each sample site, four VIs, namely, Normalized Difference VI (NDVI), Enhanced VI, Normalized Difference Water Index (NDWI) and Modified Soil-Adjusted VI (MSAVI) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) products with grain sizes of 250 m and 1 km. Linear regression models were employed to identify the best estimator of the AGB for the entire grassland and the three individual grassland types. Paired Wilcoxon tests were applied to assess the grain size effect on the AGB estimation. General linear models were used to quantify the relationships between the spatial distribution of the grassland AGB and climatic factors.Important findings The results showed that the best estimator for the entire grassland AGB on the Xizang Plateau was MSAVI at a 250 m grain size (MSAVI 250 m). For each individual grassland type, the best estimator was MSAVI at a grain size of 250 m for alpine meadow, NDWI at a grain size of 1 km for alpine steppe and NDVI at a grain size of 1 km for desert steppe. The explanation ability of each VI for the grassland AGB did not significantly differ for the two grain sizes. Based on the best fit model (AGB =^-10.80 + 139.13 MSAVI 250 m), the spatial pattern of the grassland AGB on the plateau was characterized. The AGB varied from 1 to 136g m ?2. Approximately 59% of total spatial variation in the AGB for the entire grassland was explained by the combination of the mean annual precipitation (MAP) and mean annual temperature. The explanatory power of MAP was weaker for each individual grassland type than that for the entire grassland. This study illustrated the high efficiency of the VIs derived from MODIS data in the grassland AGB estimation on the Xizang Plateau due to the vegetation homogeneity within a 1×1 km pixel in this region. Furthermore, MAP is a primary driver on the spatial variation of AGB at a regional scale.     

基于MOD09产品的水稻叶面积指数和叶绿素含量的遥感估算模型

基于中分辨率成像光谱仪MODIS (moderate resolution imaging spectroradiometer)反射率产品MOD09的同步野外实测水稻叶面积指数（LAI）和叶绿素含量（Chltot）相关数据,探寻用MOD09产品提取的植被指数(VIs)与水稻LAI和Chltot之间的相关性以及估算模型. 结果表明,MOD09计算的VI数值比MODIS前3个波段数值偏大,归一化植被指数NDVI (normalized difference vegetation index) 值普遍比增强性植被指数EVI(enhanced vegetation index) 值大. 通过4种不同植被指数与LAI相关性的比较,得出EVI与LAI的相关关系在水稻各个生育期优于其它植被指数,基于MOD09-EVI建立水稻LAI的遥感估算模型,经实际地面同步数据检验, 模型精度较高. 因而, MOD09-EVI较适用于水稻叶面积指数的实时遥感监测. MOD09红波段与Chltot之间的相关性在水稻前中期达到显著,并且优于其它植被指数,基于MOD09红波段建立了水稻前中期Chltot的估算模型并进行了精度检验. 除水稻孕穗期叶绿素含量估算模型的相关系数和F值通过了显著性检验外, 其余生育期估算模型都没有通过显著性检验.     

日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪

植被物候期(春季返青和秋季衰老)是表征生物响应和陆地碳循环的基础信息。由于常绿针叶林冠层绿度的季节变动较弱,遥感提取常绿针叶林的物候信息存在着较大的不确定性,是目前区域物候监测中的难点。利用MODIS植被指数(归一化植被指数NDVI和增强型植被指数EVI)、GOME-2日光诱导叶绿素荧光(SIF)和通量数据(总初级生产力GPP)估算2007—2011年亚热带常绿针叶林物候期,用来比较三类遥感指数估算常绿针叶林物候的差异。结果表明:基于表征光合作用物候的通量GPP数据估算得到5年内亚热带常绿针叶林生长季开始时间(SOS_(GPP))为第63天,生长季结束时间(EOS_(GPP))为第324天,生长季长度为272天;基于反映植被光合作用特征的SIF曲线获得物候信息要滞后GPP物候期,其中生长季开始时间滞后19天,生长季结束时间滞后2天;基于传统植被指数NDVI和EVI的物候期滞后GPP物候期的时间要大于SIF滞后期,其中植被指数SOS滞后SOS_(GPP)31天,植被指数EOS滞后EOS_(GPP)10—17天。虽然基于3种遥感指数估算的春季和秋季物候都滞后于通量GPP的物候期,但是卫星SIF的物候信息能够更好地捕捉常绿针叶林的生长阶段。同时,春季温度是影响森林生长季开始时间的最重要因素;秋季水分和辐射是影响生长季结束时间的关键因素。由此可见,SIF估算的亚热带常绿针叶林的春季和秋季物候的滞后时间要短于传统植被指数,能更好地追踪常绿林光合作用的季节性,为深入研究陆地生态系统碳循环及其对气候变化的响应提供重要的基础。     

Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest

Aims Understanding of the ecophysiological dynamics of forest canopy photosynthesis and its spatial and temporal scaling is crucial for revealing ecological response to climate change. Combined observations and analyses of plant ecophysiology and optical remote sensing would enable us to achieve these studies. In order to examine the utility of spectral vegetation indices (VIs) for assessing ecosystem-level photosynthesis, we investigated the relationships between canopy-scale photosynthetic productivity and canopy spectral reflectance over seasons for 5 years in a cool, temperate deciduous broadleaf forest at 'Takayama' super site in central Japan.Methods Daily photosynthetic capacity was assessed by in situ canopy leaf area index (LAI), (LAI × V cmax [single-leaf photosynthetic capacity]), and the daily maximum rate of gross primary production (GPP max) was estimated by an ecosystem carbon cycle model. We examined five VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red vegetation index (GRVI), chlorophyll index (CI) and canopy chlorophyll index (CCI), which were obtained by the in situ measurements of canopy spectral reflectance.Important findings Our in situ observation of leaf and canopy characteristics, which were analyzed by an ecosystem carbon cycling model, revealed that their phenological changes are responsible for seasonal and interannual variations in canopy photosynthesis. Significant correlations were found between the five VIs and canopy photosynthetic capacity over the seasons and years; four of the VIs showed hysteresis-type relationships and only CCI showed rather linear relationship. Among the VIs examined, we applied EVI–GPP max relationship to EVI data obtained by Moderate Resolution Imaging Spectroradiometer to estimate the temporal and spatial variation in GPP max over central Japan. Our findings would improve the accuracy of satellite-based estimate of forest photosynthetic productivity in fine spatial and temporal resolutions, which are necessary for detecting any response of terrestrial ecosystem to meteorological fluctuations.     

绿洲生态系统生物量与植被指数分析

利用阜康绿洲野外实测的53个样方的植物生物量数据与同期陆地卫星MODIS影像的第1,2通道250 m遥感数据,分析植被指数与绿洲植物生物量的相关关系,建立植被指数与绿洲植物生物量的一元线性和非线性回归模型。结果表明,植被指数NDVI和MSAVI与绿洲生态系统植物生物量之间存在较好的相关性;所建植被指数与植物生物量的回归模型中,三次方程为所得到的回归模型中最适合用于绿洲生态系统植物生物量和生长监测。     

不同大气校正方法对森林叶面积指数遥感估算影响的比较

利用TM原始图像以及经过6S模型和基于影像自身的Gilabert模型大气校正后的地面绝对反射率图像,分别计算了褒河流域阔叶林和针阔混交林2种林型的5类光谱植被指数(SR、NDVI、MNDVI、ARVI和RSR),并建立各林型森林叶面积指数与同时相的各个植被指数的相关关系。结果表明,2种大气校正模型均显著提高了各植被指数与森林叶面积指数的相关关系,除了对森林叶面积指数与植被指数SR和NDVI的相关关系影响不显著外,对森林叶面积指数与植被指数MNDVI、ARVI和RSR相关关系的影响均非常显著。说明不同大气校正模型对叶面积指数的遥感估算结果有较大影响。因此,在利用遥感数据进行定量分析、信息提取和生态遥感应用时,不仅要进行大气校正,而且还要慎重选择大气校正模型和植被指数。     

Estimating soil respiration using spectral vegetation indices and abiotic factors in irrigated and rainfed agroecosystems

Aims

Our aims were to identify the primary factors involved in soil respiration (R_s) variability and the role that spectral vegetation indices played in R_s estimation in irrigated and rainfed agroecosystems during the growing season.

Methods

We employed three vegetation indices [i.e., normalized difference vegetation index (NDVI), green edge chlorophyll index (CI_{green edge}) and enhanced vegetation index (EVI)] derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product as approximations of crop gross primary production (GPP) for R_s estimation. Different statistical models were used to analyze the dependencies of R_s on soil temperature, soil water content and plant photosynthesis, and accuracy of these models were compared in the irrigated and rainfed agroecosystems.

Results

The results demonstrated that a model based only on abiotic factors (e.g., soil temperature and soil water content) failed to describe part of the growing-season variability in R_s. Residual analysis indicated that R_s was influenced by a short-term gross primary production (GPP) and a longer-term (≥3 days) accumulated GPP in the irrigated and rainfed agroecosystems. Therefore, photosynthesis dependency of R_s should be included in the R_s model to describe the growing-season dynamics of R_s. Among the three VIs, CI_{green edge} showed generally better correlations with GPP at different cumulative times and canopy green leaf area index than EVI and NDVI. Adding the CI_{green edge} into the model considering only soil temperature and soil water content significantly improved the simulation accuracy of R_s.

Conclusions

Our results suggest that spectral vegetation index from remote sensing could be used to estimate R_s, which will be helpful for the development of a future R_s model over a large spatial scale.     

基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析

植被物候学作为研究植被与环境条件相互作用的科学,在全球气候变化的大背景下已成为国际热点研究领域,其中森林植被在调节全球碳平衡、维护全球气候稳定的过程中有着至关重要的作用。随着遥感技术的发展,多种遥感指数被应用到森林植被物候研究中,其中以MODIS NDVI和EVI应用最为广泛,而叶绿素荧光(SIF)作为植被光合作用的"探针"也被广泛应用于森林植被物候研究中。为了探究3种指数在森林植被物候研究中的差异与特性,本文以长白山温带红松阔叶林通量观测站为研究区域,采用模型拟合结合动态阈值法提取2007—2013森林物候特征参数,并使用通量数据(总初级生产力GPP)进行验证。结果表明:NDVI与EVI、SIF相比,表现为生长季开始时间与结束时间的明显提前和滞后,与GPP数据偏差较大,且夏季生长季峰期曲线形态过宽且平坦,无法较好反映生长季变化特征;EVI相较于NDVI有所改善,整体变化趋势与SIF、GPP基本吻合,但依然存在秋季衰减时间稍迟于SIF与GPP的问题;SIF虽然存在夏季骤降现象,但依然与GPP数据一致性最好,可以较好反映出森林植被季节变化特征。SIF数据与植被光合作用的紧密关联使其在植被物候研究中具有优于植被指数的准确性,并随着遥感平台的增加和反演方法的改善,将会在多尺度、多类型的植被物候监测中发挥更加重要的作用。     

Detecting soil salinity with MODIS time series VI data

Mapping of salinization using the satellite derived vegetation indices (VIs) remains difficult at broad regional scales due to the low classification accuracy. Satellite derived VIs from the Moderate Resolution Imaging Spectroradiometer (MODIS) have more potential because the MODIS balances the requirements of spatial detail, spectral and temporal density and tends to reflect vegetation responses through time. However, the relationship between MODIS data and salinity may be underestimated in previous studies because the MODIS time series data were not investigated thoroughly, especially regarding vegetation phenology. This study assessed the applicability of MODIS time series VI data for monitoring soil salinization with a series of MODIS pixels selected in the Yellow River Delta, China. The hidden information in vegetation phenology was investigated by improving the quality of VIs time series data with the Savitzky–Golay filter, extracting the phenological markers and differentiating VIs time series data based on vegetation types. The results showed that the quality of the enhanced vegetation index (EVI) time series data were improved by the Savitzky–Golay filter, which could provide more accurate thresholds of phenological stages than the empirical definition. The seasonal integral of EVI (EVI-SI) extracted from the smoothed EVI time series profile was verified as the best indicator of the degree of soil salinity. Additionally, the correlation of EVI-SI and soil salinity was highly dependent on land cover heterogeneity, and the ranges of correlation coefficients were as high as 0.59–0.92. EVI-SI was linearly correlated with EC_e in cropland with a high model fit (R² = 0.85). The relationship of EVI-SI and EC_e fit best with a binomial line and EVI-SI was able to explain 70% of the variance of EC_e. Despite the poor fit of the linear regression model in mixed sites limited by spatial resolution (R² = 0.32), MODIS time series VI data, as well as the extracted seasonal parameters, still show great potential to assess large-scale soil salinization.     

Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China

Aims Accurate remote estimation of the fraction of absorbed photosynthetically active radiation (fAPAR) is essential for the light use efficiency (LUE) models. Currently, one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index (NDVI). Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method. This study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods To investigate the method of remote estimation of fAPAR seasonal dynamics, the CASA (Carnegie–Ames–Stanford Approach) model and spectral vegetation indices (VIs) were used for in situ measurements of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important findings The results showed that the fAPAR increased rapidly with the day of year during the vegetative stage, it remained relatively stable at the stage of reproduction, and finally decreased slowly during the senescence stage. In addition, fAPAR green [fAPAR green = fAPAR × (green LAI/green LAI max)] showed clearer seasonal trends than fAPAR. The NDVI, red-edge NDVI, wide dynamic range vegetation index, red-edge position (REP) and REP with Sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season. In a comparison of the predictive performance of VIs for the whole growing season, REP was the most appropriate spectral index, and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.     

Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data

There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.     

岷江上游植被冠层降水截留的空间模拟

 通过对岷江上游实地踏查和定位观测研究,结合MODIS遥感数据,利用“3S”技术对岷江上游植被冠层降水截留进行了空间模拟。研究结果表明：岷江上游植被叶面积指数（LAI）与增强性植被指数（EVI）以二项式关系拟合效果较好。由于归一化植被指数（NDVI）存在的饱和问题,研究采用EVI反演LAI,统计结果表明：岷江上游LAI值在0～2之间的占28.57%,在2～4.5之间的占63.06%,大于4.5的占8.37%,其中LAI最大值为7.394;从冠层最大降水截留模拟结果来看: 植被较好的地区,如卧龙、米亚罗的植被冠层最大降水截留量较大,而干旱河谷、上游高山草甸等地的植被冠层最大降水截留量相对较低;附加冠层降水截留与降雨量呈线性相关,模型验证时以此为基础,模型模拟的结果较为理想。     

基于植被指数的典型草原区生物量模型——以内蒙古锡林浩特市为例

利用遥感估测地上生物量是国内外生态学与地理学的研究热点。但基于植被指数的生物量回归模型结果差异较大,究竟哪种植被指数与哪种模型更适合典型草原的生物量反演,是现代草地遥感急需解决的问题之一。该文基于TM影像数据的不同植被指数(VI)差异性,分别选取了RVI(比值植被指数)、NDVI(归一化差异植被指数)、SAVI(土壤调节植被指数)、MASVI(修改型土壤调整植被指数)和RSR(简化比率植被指数)5种植被指数,与同期的内蒙古典型草原区地面实测地上生物量做相关分析,分别建立了5种植被指数与地上生物量的线性及3种非线性(对数、二次多项式、三次多项式)回归模型。研究结果表明:对于中国北方典型草原区而言,地上生物量与5种植被指数(RVI、NDVI、SAVI、MSAVI和RSR)均呈现出显著相关,但地上生物量与后4种植被指数是正相关,与RVI为负相关;利用5种植被指数(RVI、NDVI、SAVI、MSAVI和RSR)监测草地植被生物量的复相关系数均大于0.6,充分说明利用植被指数检测典型草原生物量是一种简单可行的方法;NDVI建立的生物量回归模型,其复相关系数大于其它4类植被指数(RVI、SAVI、MSAVI和RSR),说明NDVI-生物量模型优于植被指数RVI、SAVI、MSAVI和RSR 模型,其模拟地表生物量的效果好;对于TM影像来说,植被生物量的线性模型与3种非线性模型(三次多项式生物量模型、二次多项式生物量模型、对数模型)都表现出较好的模拟效果,都通过了0.01的显著性检验,而且该研究的结果显示出三次多项式生物量回归模型最优,其次是二次多项式生物量模型,再次是线性模型,相对较差的是对数模型。通过NDVI-生物量三次多项式回归模型模拟锡林浩特草原的生物量,可以看出整个研究区的地上生物量基本上是东高西低、东南高西北低的趋势,这与研究区的地形、气候及土地利用等多种因素有关。     

基于遥感与实地调查对潜在可造林区乔木林和灌草地的比较

气候变化引起祁连山东部地区可适植被类型改变,探究植被类型转换的效果对生态环境可持续发展十分重要,但其转换方式及效果仍有待研究,此外传统植被调查的方法有诸多局限性,不能满足大尺度持续的监测,而遥感监测可以弥补这一劣势。基于遥感和样地调查以祁连山生态交错区甘沟小流域为研究地点,对原有灌草地和植树造林的乔木林进行比较,探究二者土壤理化性质、草本植物多样性及植被归一化指数（NDVI）,增强植被指数（EVI）,植被水分指数（NDMI）,水分胁迫指数（MSI）,叶绿素红外指数（CI）,陆地叶绿素指数（MTCI）的差异。结果表明仅有水分相关指标有显著性差异,其中造林造成浅层土壤水分显著降低（P<0.01）,4-5月份MSI和NDMI造林区植被水分高于灌草地（P<0.01）,7-8月份两种植被类型水分指数以及其余指数无显著性差异,另外造林后的土壤有机质出现了轻微下降（P>0.05）。遥感指数和样地调查指标相关性分析中,土壤有机质和Shannon多样性指数与CI成正相关（P<0.05）,植被覆盖度与NDMI成负相关（P<0.05）,由于覆盖度较低的灌草地EVI和NDVI被高估,覆盖度和EVI与NDVI相关性不显著。综合遥感指数和实地调查分析,短时间造林时间内乔木林牺牲了部分土壤水分,提高了植被盖度,且目前造林并未对当地环境产生胁迫,但对生态环境的改善并不明显。基于遥感和样地调查揭示了潜在植被类型转换区原有灌草地和植树造林区的差异,并探讨遥感在小尺度范围内植被监测上的适用性,为植被建设和遥感监测植被状况提供借鉴。     

Greater phenological sensitivity to temperature on higher Scottish mountains: new insights from remote sensing

Mountain plants are considered among the species most vulnerable to climate change, especially at high latitudes where there is little potential for poleward or uphill dispersal. Satellite monitoring can reveal spatiotemporal variation in vegetation activity, offering a largely unexploited potential for studying responses of montane ecosystems to temperature and predicting phenological shifts driven by climate change. Here, a novel remote‐sensing phenology approach is developed that advances existing techniques by considering variation in vegetation activity across the whole year, rather than just focusing on event dates (e.g. start and end of season). Time series of two vegetation indices (VI), normalized difference VI (NDVI) and enhanced VI (EVI) were obtained from the moderate resolution imaging spectroradiometer MODIS satellite for 2786 Scottish mountain summits (600–1344 m elevation) in the years 2000–2011. NDVI and EVI time series were temporally interpolated to derive values on the first day of each month, for comparison with gridded monthly temperatures from the preceding period. These were regressed against temperature in the previous months, elevation and their interaction, showing significant variation in temperature sensitivity between months. Warm years were associated with high NDVI and EVI in spring and summer, whereas there was little effect of temperature in autumn and a negative effect in winter. Elevation was shown to mediate phenological change via a magnification of temperature responses on the highest mountains. Together, these predict that climate change will drive substantial changes in mountain summit phenology, especially by advancing spring growth at high elevations. The phenological plasticity underlying these temperature responses may allow long‐lived alpine plants to acclimate to warmer temperatures. Conversely, longer growing seasons may facilitate colonization and competitive exclusion by species currently restricted to lower elevations. In either case, these results show previously unreported seasonal and elevational variation in the temperature sensitivity of mountain vegetation activity.     

Modeling gross primary productivity of alpine meadow in the northern Tibet Plateau by using MODIS images and climate data

The Vegetation Photosynthesis Model (VPM) was used to simulate the gross primary productivities (GPP) of the alpine meadow ecosystem in the northern Tibet Plateau at three different spatial resolutions of 0.5 km, 1.5 km and 2.5 km, respectively. The linear relationships between enhanced vegetation indices (EVI) and GPP, with higher correlative coefficients, were better than those between normalized difference vegetation indices (NDVI) and GPP at the three resolutions. VPM could well simulate the seasonal changes and inter-annual variations of GPP, with similar trends at the three resolutions. There were significant differences (P < 0.0001) among the three modeled GPP with the three resolutions. Therefore, the modeled GPP at high resolution could not be directly extrapolated to low resolution, and vice versa. The contribution levels of different model parameters, including photosynthetically active radiation (PAR), air temperature (Ta), NDVI, EVI and land surface water indices (LSWI), to modeled GPP could vary with spatial resolution based on multiple stepwise linear regression analysis. This indicated that it was important to choose parameters properly and consider their effects on modeled GPP.