Advance and evaluation in the long time series vegetation trends research based on remote sensing

The long time series vegetation trends (LTSVT) research based on remote sensing in large area is the core field of vegetation ecology and an important direction in the global change study. AVHRR, SPOT VGT and MODIS are currently the main data resources of LTSVT research. With volumes of remote sensing data, the analysis and evaluation methods for LTSVT study emerged as an urgent issue. Algebra calculation, Fourier transformation, PCA analysis, wavelet transform, linear trend analysis (LTA), correlation analysis (CA), etc., are the main methods. After the assessing and grouping of the methods, we fo- cused on comparing the LTA and CA, which were well accepted methods, with the newly introduced Sen+Mann-Kendall method. Our review showed Sen+Mann-Kendall had a strong strength of errors resistance and was not constrained by the data statistical distribution.