基于GIMMS与SPOT vegetation的中亚物候变化趋势及对比

The Trend and Comparison of Vegetation Phenological Change in Central Asia Based GIMMS and SPOT Vegetation

植被物候是反映生态系统受全球气候变化影响的重要证据。作为生态与水资源系统最为脆弱的地区之一,中亚干旱区植被物候对气候变化的响应情况是当前的全球环境变化研究热点。文章以GIMMS和SPOT vegetation数据为基础,在TIMESAT物候信息提取软件的支持下,以动态阈值法提取了1982─2006年和1999─2012年中亚地区植被物候空间信息。结合Mann-Kendall趋势分析方法,对中亚地区2个时期的植被开始期,停止期和生长季长度的3种典型物候参数的历史变化情况和空间分布进行识别;同时,通过二维散点图和最小二乘一维线性回归的统计分析方法,开展了对1999─2006年8年重叠期期间GIMMS和SPOT vegetation所提取的3种物候数据对比分析。结果表明:1中亚研究区在1982─2006年和1999─2012年2个分段时期没有发生显著的整体性植被物候变化,其未发生显著性变化面积分别占研究区总面积的90%和95%;2农作物种植区域是中亚地区植被物候发生显著变化的主要区域;3对GIMMS与SPOT vegetation数据提取3种物候参数进行空间相关性分析结果表明,GIMMS和SPOT vegetation在提取的物候数据存在差异,开始期,停止期和生长季长度的相关性分别为[0.36,0.56],[0.32,0.49]和[0.28,0.45],且植被覆盖度高的区域要比覆盖度低的区域差异小,这也说明了不同遥感数据源在中亚干旱区植被物候信息提取一致性较差,其原因可能尺度差异和土壤背景值的严重影响。

Vegetation phenology is the most compelling evidence that ecosystems are being influenced by global climate change. The response of vegetation phenology on climate change in Central Asia, which is one of regions with the most fragile ecological and hydrological system, has become a hot issue in global environmental research worldwide. Based on GIMMS （Global Inventory Modeling and Mapping Studies） data from 1982 to 2006 and SPOT vegetation S10 data from 1998 to 2012, the vegetation phenology information was extracted by dynamic threshold method with Timesat phenology extract software. Mann-Kendall trend analysis method was used to assess spatial-temporal change trend of Start of Season （SOS）, End of Season （EOS） and Length of Season （LOS）. Moreover, combined the 2 d scatterplot and linear regression algorithm of least square, The three metrics in the overlapped 8 year was compared for examining the difference between two set of vegetation phenology data derived from GIMMS and SPOT vegetation. The result indicated that, （1） vegetation phenology characters in 90% and 95% area of Central Asia did not shown a significant change trend for 1982─2012; （2） agriculture land was found as a main land cover type with a significant change of SOS, EOS and LOS; （3） there are obvious difference between two set of phonological data based on GIMMS and SPOT vegetation; the range of correlation coefficients between tow set of SOS, EOS and LOS are [0.36,0.56],[0.32,0.49] and [0.28,0.45], and in the area with sparse vegetation, two set of the three metrics show a lower consistency than the area with higher vegetation coverage. It suggested the consistency can be influenced by different scale and soil background.