摘要:以GIMMS 3g（the third generation Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index）数据为基础，利用月合成、标准距平和趋势保留预置白方法进行数据预处理，采用季节趋势分析方法提取振幅0、振幅1和相位1季节表征因子，运用MK（Mann-Kendall）和CMK（Contextual Mann-Kendall）趋势检验获取新疆植被年际和季节趋势变化特征，结合土地利用覆盖数据，侦测显著性变化区域的空间分布特点，讨论并分析不同预处理方法和趋势分析方法下的结果差异。研究表明：（1）植被状况趋于退化的区域面积明显大于植被状况转好的面积，植被退化区域主要集中在南北疆荒漠区域的未利用地和草地，转好的区域则主要集中在山区草地、未利用地和耕地区域；（2）新疆植被年内波动幅度有明显增加的趋势，主要分布在塔里木盆地南缘以北的草地、未利用地和耕地；（3）不同预处理方法下的植被状况趋势显著性结果存在明显的影响，按照显著性信息的提取能力排序，标准距平>趋势保留预置白>原始数据>月平均；（4）耕地区域中有87.88%表现出年内波动幅度显著增加的趋势，53.31%生长季开始期显著推迟。
关键词:年际变化/
季节变化/
GIMMS 3g数据/
趋势分析/
新疆
Abstract:Based on GIMMS 3g (the third generation Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index) data, three preprocessing methods including monthly aggregation, standard anomaly computation, and trend-preserving prewhitening were used to develop six data series. Seasonal trend analysis was applied to extract three seasonal representative factors, i.e. amplitude 0, amplitude 1, and phase 1 to detect the characteristic of seasonal trend. The interannual and seasonal trend analysis was conducted using CMK (Contextual Mann-Kendall) and MK (Mann-Kendall) trend test methods. Land use and cover data was used as an accessory to identify spatial distribution pattern of areas with significant changes. The difference between preprocessing method and trend test method was also discussed. The result shows that:(1) The proportional area of vegetation deterioration is higher than the proportional area of vegetation improvement; the former is mainly located at areas of unused lands and grasslands, and the latter is found over grasslands, unused lands and farmlands. (2) The amplitudes of annual variability show a significant increasing trend mainly over grasslands, unused lands and farmlands in the southern margin of the Tarim basin. (3) Different preprocessing methods have obvious impacts on the result of trend analysis. According to the ability of these methods to extract significant trend information, they are in the sequence of standard anomaly > trend-preserving prewhiting > original data > monthly aggregate. (4) 87.88% of farmlands demonstrates a significant increase trend in annual variation amplitude and 53.31% of farmlands shows a significant trend of delayed onset of growing season.
Key words:Interannual change/
Seasonal change/
GIMMS 3g data/
Trend analysis/
Xinjiang
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