基于遥感的植被长时序变化特征是植被生态学研究的核心领域, 也是全球变化研究的重点方向。AVHRR、SPOT VGT和MODIS是当前研究植被长时序趋势变化的主要数据源。海量数据不断积累的同时, 植被长时序趋势特征研究方法却缺乏对比评价和分析。当前常用的方法有代数运算法、傅里叶变换、主成分分析、小波变换法、回归分析法和相关系数分析法等。在对各种方法评述和分析的基础上, 重点讨论和对比了主流方法中的回归分析法和相关系数分析与新兴方法Sen＋Mann-Kendall法。结果表明, Sen＋Mann-Kendall能克服主流方法的不足, 不需要数据服从某一特定分布, 并且对数据的误差具有较强的抵抗能力。

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 focused 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.