1982-2012年北半球荒漠草原过渡带植被物候特征及其与气候因子的关系

基于GIMMS（global inventory modeling and mapping studies）NDVI 3g数据,在提取北半球荒漠草原过渡带每年植被物候期的基础上,研究了1982-2012年物候期的时间演化趋势及空间分异特征,并结合全球气候再分析资料,探讨了物候变化的气候驱动因素。结果表明：在1998年之前,荒漠草原过渡带植被物候期变化地区间差异较大,而在1998年之后,北半球荒漠草原过渡带生长季结束期整体提前,平均提前0.41 d/a;同时,除萨赫勒以外的各地区植被生长季长度普遍缩短,平均缩短0.88 d/a。植被物候期与气候因子的相关分析发现,荒漠草原过渡带植被物候变化受气候变化影响显著,且空间差异明显。在中高纬度地区,气温是限制植被活动的关键因子,温度升高可以促进生长季开始期的提前,而降水增加则会妨碍植被生长;在较低纬度地区,水分是影响植被活动的关键因素,高温造成的水分亏缺会导致植被生长季缩短。从植被物候期对各气候因子响应的时滞性来看,荒漠草原过渡带植被的物候期对气温变化的响应最迅速,对蒸散的响应存在一定的滞后性,而对降水的响应不存在时滞差异。     

基于GIMMS 3g NDVI的近30年中国北部植被生长季始期变化研究

基于全球库存建模与绘图研究第三代归一化差值植被指数（GIMMS 3g NDVI）、土地利用和气温降水数据,利用NDVI时间序列谐波分析法(HANTS)重构了中国北部地区原始植被NDVI,用一元六次多项式拟合了植被生长曲线并结合逐像元动态阈值法提取了中国北部地区1983~2012年植被生长季始期并分析了其时空变化及对气温和降水的响应情况。结果表明：①GIMMS 3g NDVI具有较长的时序特征和较好的数据质量,经HANTS时间序列谐波分析后能很好的表现植被生长季曲线特征,可用于后续植被生长季的研究。② 北部地区生长季始期均值主要集中分布在80~150βd之间,全区30βa平均为111.6βd,东北平原、华北平原、河套平原、新疆天山和阿尔泰地区生长季始期早于其它区域。③ 研究时段内北部地区生长季始期总体上呈提前趋势（R²=0.19）,空间上由西北向东北逐渐推移,明显提前的区域主要分布在内蒙古中东部、东北平原、陕西南部和新疆天山的部分地区,明显推迟的区域主要分布在青藏高原高寒地区。④ 因植被类型的不同和区域的差异,生长季始期对气温和降水的响应程度不同,春季气温是影响生长季始期变化的主要自然因素。     

1982-2013年青藏高原植被动态变化时序分析

针对不同的数据源及时间和空间尺度会使植被覆盖度及其与气象因子影响的结果有所差别这一情况,该文基于青藏高原1982-2012年GIMMS NDVI和2001-2013年MODIS NDVI遥感数据集,结合研究区内12个典型的气象站点数据,进行了青藏高原地区植被覆盖时空动态变化规律及其与气象因子响应的时序分析,并利用重合时间段的数据对比分析了两种传感器在青藏高原地区对植被动态变化监测方面的差异.结果表明:近30年来,青藏高原地区植被呈整体改善趋势,尤其是高海拔地区;不同阶段植被的变化趋势有所不同;两种传感器在反映植被动态变化趋势上差异显著,但两者与气候因子的响应规律相同.     

基于1982~2013年NDVI数据的新疆30年植被状况季节与年际趋势分析

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。     

蒙古高原NDVI的空间格局及分异

GIMMS NDVI database and geo-statistics were used to depict the spatial distribu-tion and temporal stability of NDVI on the Mongolian Plateau. The results demonstrated that: (1) Regions of interest with high NDVI indices were distributed primarily in forested moun-tainous regions of the east and the north, areas with low NDVI indices were primarily distrib-uted in the Gobi desert regions of the west and the southwest, and areas with moderate NDVI values were mainly distributed in a middle steppe strap from northwest to southeast. (2) The maximum NDVI values maintained for the past 22 years showed little variation. The average NDVI variance coefficient for the 22-year period was 15.2%. (3) NDVI distribution and vege-tation cover showed spatial autocorrelations on a global scale. NDVI patterns from the vegetation cover also demonstrated anisotropy; a higher positive spatial correlation was in-dicated in a NW-SE direction, which suggested that vegetation cover in a NW-SE direction maintained increased integrity, and vegetation assemblage was mainly distributed in the same specific direction. (4) The NDVl spatial distribution was mainly controlled by structural factors, 88.7% of the total spatial variation was influenced by structural and 11.3% by random factors. And the global autocorrelation distance was 1178 km, and the average vegetation patch length (NW-SE) to width (NE-SW) ratio was approximately 2.4:1.0.     

NDVI spatial pattern and its differentiation on the Mongolian Plateau

GIMMS NDVI database and geo-statistics were used to depict the spatial distribution and temporal stability of NDVI on the Mongolian Plateau.The results demonstrated that:(1) Regions of interest with high NDVI indices were distributed primarily in forested mountainous regions of the east and the north,areas with low NDVI indices were primarily distributed in the Gobi desert regions of the west and the southwest,and areas with moderate NDVI values were mainly distributed in a middle steppe strap from northwest to southeast.(2) The maximum NDVI values maintained for the past 22 years showed little variation.The average NDVI variance coefficient for the 22-year period was 15.2%.(3) NDVI distribution and vegetation cover showed spatial autocorrelations on a global scale.NDVI patterns from the vegetation cover also demonstrated anisotropy;a higher positive spatial correlation was indicated in a NW-SE direction,which suggested that vegetation cover in a NW-SE direction maintained increased integrity,and vegetation assemblage was mainly distributed in the same specific direction.(4) The NDVI spatial distribution was mainly controlled by structural factors,88.7% of the total spatial variation was influenced by structural and 11.3% by random factors.And the global autocorrelation distance was 1178 km,and the average vegetation patch length(NW-SE) to width(NE-SW) ratio was approximately 2.4:1.0.     

近20余年来西北地区植被变化特征分析

利用1982—2003年8 km分辨率的NDVI数据集,选中国西北地区森林、草原、灌溉农业、雨养农业区不同类型植被为研究区,分析了植被年、年际变化特征,并对植被覆盖空间变化进行动态研究.结果表明:森林、草原、灌溉农业区和以春小麦为主的雨养农业区NDVI年变化为单峰型曲线,以冬小麦为主的雨养农业区NDVI曲线呈双峰型;同一类型的植被NDVI受纬度或海拔高度的影响,绿峰出现时间存在1个月的位相差.22 a来森林植被NDVI多呈下降趋势,草原植被区为上升趋势;雨养农业区变化不大,灌溉植被区呈显著的上升趋势.西北东部雨养农业区植被波动频率和幅度最大,是受降水影响最敏感的地区;森林植被次之;有灌溉条件的绿洲植被,年际间波动最小.22 a间西北地区植被以增加趋势为主,增加面积约为20.5%,主要分布在新疆和河西走廊绿洲、黄河沿岸灌区以及青海草区,水分条件充足的绿洲是NDVI增加最显著的区域;NDVI减少地区面积为4.77%,主要分布在西北东部.     

黑河流域天然植被的面积变化研究

植被覆盖面积的大小是反映区域性生态环境状况的重要指标之一,多年植被覆盖面积的变化则直观反映了植被生态环境随时间的变化规律。在前人研究成果的基础上,采用定量遥感技术,应用美国国家航天航空局最新的全球植被指数变化研究数据(GIMMS)作为数据源,对黑河流域1989-2002年间的植被面积变化规律进行了研究。考虑到实际的植被面积难以确定,因此采用等效面积法确定植被覆盖面积,通过统计遥感图像上植被指数(NDVI)值大于0.3的像素点值,以像素点值代表植被的面积,研究黑河流域14 a的天然植被面积变化规律。研究结果证明,黑河流域的天然植被在以每年约3%的速度减少。     

Understanding long-term (1982–2013) patterns and trends in winter wheat spring green-up date over the North China Plain

Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p < 0.01) and lower root mean square error (22 days) and bias (16 days) were observed in GUD from the improved algorithm relative to GUD from the MCD12Q2 phenology product. In spatial terms, GUD increased from the southwest (less than day of year (DOY) 60) to the northeast (more than DOY 90) of the NCP, which corresponded to spatial reductions in temperature and precipitation. GUD advanced in most (78%) of the winter wheat area on the NCP, with significant advances in 37.8% of the area (p < 0.05). GUD occurred later at high altitudes and in coastal areas than in inland areas. At the interannual scale, the average GUD advanced from DOY 76.9 in the 1980s (average 1982–1989) to DOY 73.2 in the 1990s (average 1991–1999), and to DOY 70.3 after 2000 (average 2000–2013), indicating an average advance of 1.8 days/decade (r = 0.35, p < 0.05). Although GUD is mainly controlled by the pre-season temperature, our findings underline that the effect of the pre-season soil moisture on GUD should also be considered. The improved GUD algorithm and satellite-based long-term GUD data are helpful for improving the representation of GUD in terrestrial ecosystem models and enhancing crop management efficiency.     

祁连山区植被物候遥感监测与变化趋势

基于1982-2006年GIMMS NDVI时间序列数据,利用Double Logistic拟合方法提取了祁连山区植被的生长季始期、生长季末期和生长季长度参数,分析了植被物候期的时间变化趋势及空间分异特征。结果表明:祁连山植被从东南向西北逐渐变绿,而从西北到东南逐渐变黄,植被生长季呈现出东南地区比西北地区长、河谷地区比高山地区长的特征。25年内植被年生长季始期呈提前趋势,提前幅度为0.044 d·a^-1,年代趋势为延迟-提前-延迟;年生长季末期也呈提前趋势,提前幅度为0.059 d·a^-1,年代趋势为延迟-提前;生长季长度略有缩短,缩短幅度为0.015 d·a^-1,年代趋势为缩短-延长-缩短。25年内祁连山区植被生长季始期、末期提前不明显的区域主要为高山地区,分别占51.46%、42.77%;生长季始期、末期推迟不明显区域主要为河谷地区,分别占44.41%、52.91%;植被生长季高山地区延长不明显,河谷地区缩短不明显,总体上植被物候没有出现明显变化。