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Deriving Vegetation Dynamics of Natural Terrestrial Ecosystems from MODIS NDVI/EVI Data over Turkey

机译:从土耳其的MODIS NDVI / EVI数据推导自然陆地生态系统的植被动态

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The 16-day composite MODIS vegetation indices (VIs) at 500-m resolution for the period between 2000 to 2007 were seasonally averaged on the basis of the estimated distribution of 16 potential natural terrestrial ecosystems (NTEs) across Turkey. Graphical and statistical analyses of the time-series VIs for the NTEs spatially disaggregated in terms of biogeoclimate zones and land cover types included descriptive statistics, correlations, discrete Fourier transform (DFT), time-series decomposition, and simple linear regression (SLR) models. Our spatio-temporal analyses revealed that both MODIS VIs, on average, depicted similar seasonal variations for the NTEs, with the NDVI values having higher mean and SD values. The seasonal VIs were most correlated in decreasing order for: barren/sparsely vegetated land > grassland > shrubland/woodland > forest; (sub)nival > warm temperate > alpine > cool temperate > boreal = Mediterranean; and summer > spring > autumn > winter. Most pronounced differences between the MODIS VI responses over Turkey occurred in boreal and Mediterranean climate zones and forests, and in winter (the senescence phase of the growing season). Our results showed the potential of the time-series MODIS VI datasets in the estimation and monitoring of seasonal and interannual ecosystem dynamics over Turkey that needs to be further improved and refined through systematic and extensive field measurements and validations across various biomes.
机译:根据土耳其16个潜在自然陆地生态系统(NTE)的估计分布,对2000年至2007年期间500 m分辨率的16天MODIS植被指数(VIs)进行季节性平均。根据生物地​​理气候区和土地覆盖类型在空间上分类的NTE的时间序列VI的图形和统计分析,包括描述统计,相关性,离散傅里叶变换(DFT),时间序列分解和简单线性回归(SLR)模型。我们的时空分析表明,平均而言,两个MODIS VI都显示出NTE的相似季节性变化,其中NDVI值具有较高的平均值和SD值。季节性VI与降序相关性最高,依次为:荒芜/稀疏植被地>草地>灌木丛/林地>森林; (亚)冬季>温带>高山>凉带>北方=地中海;和夏季>春季>秋季>冬季。 MODIS VI对土耳其的响应之间最明显的差异发生在北方和地中海气候区和森林以及冬季(生长季节的衰老阶段)。我们的结果表明,时间序列MODIS VI数据集在估计和监测土耳其的季节性和年际生态系统动态方面具有潜力,需要通过系统和广泛的实地测量以及对各个生物群落的验证来进一步改进和完善这些潜力。

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