Previous works have shown that the combination of vegetation indices with land surface temperature (LST) improves the analysis of vegetation changes. Here, global MODIS-Terra monthly data from 2000 to 2011 were downloaded and organized into LST, NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) time series. These time series were then corrected from cloud and atmospheric residual contamination through the IDR (iterative Interpolation for Data Reconstruction) method. Then, statistics were retrieved from both corrected time series, and the YLCD (Yearly Land Cover Dynamics) approach has been applied to data sources (NDVI-LST and EVI-LST) to analyze changes in the vegetation. Finally, trends were retrieved and their statistical significance was assessed through the Mann-Kendall statistical framework. Global statistics show that both data sets lead to similar trends, as is the case for the spatial distribution of observed trends. These trends confirm previous results as well as prediction of climate warming consequences, such as a marked increase in boreal temperatures.