1982—2015年黄土高原NDVI时空演变及其对气候变化的季节响应

基于GIMMS NDVI、温度和降水数据,利用集合经验模态分解(EEMD)、线性回归分析、偏相关分析等方法分析了1982—2015年黄土高原植被覆盖时空变化及其对气候变化的季节响应。结果表明:年际变化趋势上,1982—2015年黄土高原生长季、春、夏、秋季NDVI均呈显著增长趋势,且各个季节NDVI增加速率逐年升高,尤其以夏季增加速率的变化最为明显;空间上,生长季、春、夏、秋季NDVI均呈由西北向东南递增的趋势,且在大部分地区呈显著上升趋势;线性回归表明,生长季、春、夏、秋季温度均呈显著上升趋势;生长季、秋季降水呈增加趋势,春、夏季降水呈减少趋势。EEMD分析进一步表明,生长季、春、夏、秋季温度均先升高后降低,降水均呈先减少后增加的趋势;空间变化趋势上,温度在生长季、春、夏、秋季大部分地区呈显著上升趋势,降水仅秋季有部分区域呈显著上升趋势; NDVI与温度在黄土高原东北部及西南部地区呈显著正相关,与降水在黄土高原北部及西北部地区呈显著正相关。     

中国不同植被区对极端气候的响应差异

分析不同区域植被对极端气候的响应对于加深对植被与气候之间关系的理解以及制定应对极端气候条件的措施尤为重要。基于2001—2020年气候数据和归一化植被指数(NDVI)数据,以植被区划为分析单元,分析中国8个植被区的NDVI和27个极端气候指数的时空变化趋势,探究各植被区植被NDVI对极端气候的响应特征与差异性。结果表明：(1)整个研究区及各植被区的平均NDVI年最大值呈显著增加趋势,其中,温带针叶、落叶阔叶混交林区增加趋势最明显,青藏高原高寒植被区增加趋势最弱。(2)极端高温指数多呈升高趋势。极端降水指数在研究区东部呈升高趋势,在西南部呈减少趋势。(3)在不同植被区对NDVI影响最大的极端气候指数不同,其中在寒温带针叶林区影响最大的指数为温暖时间持续指数(WSDI);在温带针叶、落叶阔叶混交林区和热带季风雨林、雨林区影响最大的指数为最高低温(TNx);在暖温带落叶阔叶林区和亚热带常绿阔叶林区为简单降水强度指数(SDII);在温带草原区为最高高温(TXx);在温带荒漠区为年总降水量(PRCPTOT);在青藏高原高寒植被区为结冰天数(ID)。     

Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change

Field observations and time series of vegetation greenness data from satellites provide evidence of changes in terrestrial vegetation activity over the past decades for several regions in the world. Changes in vegetation greenness over time may consist of an alternating sequence of greening and/or browning periods. This study examined this effect using detection of trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008. Time series of 648 fortnightly images were analyzed using a trend breaks analysis (BFAST) procedure. Both abrupt and gradual changes were detected in large parts of the world, especially in (semi‐arid) shrubland and grassland biomes where abrupt greening was often followed by gradual browning. Many abrupt changes were found around large‐scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. The net global figure – considered over the full length of the time series – showed greening since the 1980s. This is in line with previous studies, but the change rates for individual short‐term segments were found to be up to five times higher. Temporal analysis indicated that the area with browning trends increased over time while the area with greening trends decreased. The Southern Hemisphere showed the strongest evidence of browning. Here, periods of gradual browning were generally longer than periods of gradual greening. Net greening was detected in all biomes, most conspicuously in croplands and least conspicuously in needleleaf forests. For 15% of the global land area, trends were found to change between greening and browning within the analysis period. This demonstrates the importance of accounting for trend changes when analyzing long‐term NDVI time series.     

2001-2020年新疆阿勒泰地区归一化植被指数时空变化特征及其对气候变化的响应

气候变化是干旱区植被变化的重要驱动因素,探究干旱区气候与植被关系的时空变化,有助于理解生态系统演化特征。基于MODIS-NDVI与CRU数据集中气候数据（降水、平均气温、最高气温、最低气温、水汽压及潜在蒸散）,采用Sen+Mann-kendall、Hurst指数及相关分析法,在不同时间尺度评价了阿勒泰地区NDVI的时空变化特征及其对气候变化的响应。结果表明：（1）在年尺度上,植被NDVI整体呈上升趋势,但存在弱反持续特征。区域内植被退化现象严重（12.11%）,植被改善区域与退化区域呈破碎化分布。（2）月尺度与季尺度上,NDVI与降水、气温、极端气温、水汽压和潜在蒸散呈正相关,其中降水因素在季尺度上的相关性高于月尺度。（3）不同土地利用方式下NDVI与气候因子的滞后效应表现为短期正效应与长期负效应。     

青藏高原植被绿度变化及其对干湿变化的响应

青藏高原是全球气候变化的敏感区,特殊的自然环境孕育了极端脆弱的植被及其生态系统,已成为研究植被对气候变化响应的一个理想区域。植被易受气候变化的影响且响应可能因季节和植被类型而异。该研究将标准化降水蒸散指数(SPEI)和MODIS归一化植被指数(NDVI)分别作为干湿度和植被绿度指标,采用Sen’s斜率估计、BFAST模型和相关分析,分析了2000–2018年青藏高原植被绿度变化的时空格局特征,并探讨了植被绿度对干湿变化的响应。结果表明:2000–2018年青藏高原植被绿度呈上升趋势,但变化速率空间差异显著。大部分高原地区植被绿度于2012–2015年间存在突变,突变后普遍呈上升趋势,以藏北地区最为突出。青藏高原植被生长季NDVI与不同时间尺度SPEI整体呈正相关关系,且在生长季的中后期相关性逐渐增强。青藏高原植被对SPEI的响应表现出一定的年内周期性,草本植被(草甸和草原)区尤为显著。相对于森林和灌丛植被,草本植被对SPEI响应更为敏感,且在生长季的不同阶段对不同时间尺度的SPEI的响应存在明显差异。     

2000—2016年云南地区植被覆盖时空变化及其对水热因子的响应

基于2000-2016年MODIS-NDVI数据,利用趋势分析法以及线性相关分析等方法对云南地区植被月变化趋势、年际变化趋势进行详细分析;探讨植被覆盖变化与主要气候水热因子的关系。结果表明：研究区大部分地区植被覆盖良好,年NDVI的平均值为0.55,其中NDVI较高值（> 0.8）区域主要分布于南部,而西北部和中部城市地区NDVI值较低;自2000年开始,研究区NDVI总体呈显著（P < 0.05）增加趋势,年NDVI的变化斜率为0.0036,植被覆盖呈增加趋势的区域占研究区总面积79.80%;不同季节（春、夏、秋、冬）和生长季的植被状况均呈良性发展趋势;湿润指数和水热综合因子在滇西北与NDVI多呈负相关,在滇中地区以正相关为主;春、夏、秋3个季节NDVI受降水影响较大,而冬季NDVI则受气温影响较大;受降水影响较大的区域主要分布在中部和南部,受气温影响较大区域主要分布在滇西北、滇东北地区;NDVI在不同月份对气候因子的滞后时间存在差异,NDVI与当月气温的相关性强于与当月降水的相关性,植被生长对气温的响应无明显滞后效应,对降水存在3个月的滞后期。     

近30年中国陆地生态系统NDVI时空变化特征

气候变化已明显影响到陆地植被的活动,但在不同生态系统间存在差异,研究不同陆地生态系统归一化植被指数(NDVI)的时空变化特征,不仅可揭示各生态系统植被活动对气候变化的响应规律,而且可为我国不同生态区制定应对气候变化的策略和生态文明建设提供科学依据。基于1982—2012年GIMMS NDVI3g和中国陆地生态系统类型数据,利用一元线性回归、集合经验模态分解和相关分析等方法,研究了近30年中国各陆地生态系统NDVI的时空变化特征,分析了其与气候事件的关系。结果表明,近30年中国植被活动显著上升,年平均归一化植被指数(ANDVI)的上升幅度为0.0029/10a(P0.05),年最大归一化植被指数(MNDVI)的上升幅度为0.0076/10a(P0.01);植被活动显著增强的区域主要是分布在东部季风区的农田和森林生态系统,显著下降的区域主要是分布于西北的荒漠生态系统和东北的森林生态系统;尽管ANDVI和MNDVI线性趋势的显著性有所差异,但农田、森林、草地和水体与湿地生态系统的NDVI总体呈非稳定的上升趋势,上升过程中伴随着较大波动,荒漠生态系统的NDVI呈下降趋势,植被退化显著;与线性趋势不同,各生态系统植被活动的残差趋势包含"上升—下降"两个阶段,并相继于20世纪90年代到21世纪初发生转折;上述5类生态系统的植被活动存在不同尺度的周期特征,年际周期波动特征(1.9—7.6a)比较显著,而年代际周期(10.7a和22.2a)的显著性相对较差;各生态系统的空间异质性在趋强过程中存在2.1—7.1a的年际周期节律;海洋与大气环流的短周期脉动与各生态系统植被活动的周期性节律有着明显关联,ENSO事件和太阳活动是推动植被活动周期性振荡的重要因素。     

云南省植被NDVI时间变化特征及其对干旱的响应

基于云南省74个气象站点的1997—2012年逐日降水资料和逐旬SPOT-NDVI值,利用标准化降水蒸散指数(SPEI)多尺度分析了云南省干旱时间和强度演变与NDVI时间动态特征及其相关性分析,进而探讨气候变化对植被的影响。结果表明,1999—2013年云南省年平均NDVI值和年最大NDVI值均呈现波浪式的发展趋势,其趋势线斜率分别为0.0017和0.0011;NDVI年内各月变化情况大体上相同;不同季节NDVI的年际变化特征呈现出显著差异。1997—2012年不同时间尺度SPEI均体现出干旱化加剧的趋势,并随SPEI的时间尺度增大而增大;3个月尺度的SPEI值(SPEI3)结果表明,各月的变化呈现先增大后减小的趋势;SPEI3反映出多年季节水平的干旱强度为:冬季秋季春季夏季。总体上,云南省的年均NDVI与SPEI的相关性极弱,年最大NDVI与SPEI呈正相关;多年月均NDVI与不同尺度SPEI的相关性较强且存在滞后性;不同季节NDVI与SPEI的相关性及滞后性有较大差异,其中冬季NDVI、秋季NDVI与其当年当季SPEI的负相关性较强。     

Asynchronous Response of Tropical Forest Leaf Phenology to Seasonal and El Ni?o-Driven Drought

The Hawaiian Islands are an ideal location to study the response of tropical forests to climate variability because of their extreme isolation in the middle of the Pacific, which makes them especially sensitive to El Niño-Southern Oscillation (ENSO). Most research examining the response of tropical forests to drought or El Niño have focused on rainforests, however, tropical dry forests cover a large area of the tropics and may respond very differently than rainforests. We use satellite-derived Normalized Difference Vegetation Index (NDVI) from February 2000-February 2009 to show that rainforests and dry forests in the Hawaiian Islands exhibit asynchronous responses in leaf phenology to seasonal and El Niño-driven drought. Dry forest NDVI was more tightly coupled with precipitation compared to rainforest NDVI. Rainforest cloud frequency was negatively correlated with the degree of asynchronicity (Δ_NDVI) between forest types, most strongly at a 1-month lag. Rainforest green-up and dry forest brown-down was particularly apparent during the 2002–003 El Niño. The spatial pattern of NDVI response to the NINO 3.4 Sea Surface Temperature (SST) index during 2002–2003 showed that the leeward side exhibited significant negative correlations to increased SSTs, whereas the windward side exhibited significant positive correlations to increased SSTs, most evident at an 8 to 9-month lag. This study demonstrates that different tropical forest types exhibit asynchronous responses to seasonal and El Niño-driven drought, and suggests that mechanisms controlling dry forest leaf phenology are related to water-limitation, whereas rainforests are more light-limited.     

Vegetation variation of mid-subtropical forest based on MODIS NDVI data — A case study of Jinggangshan City,Jiangxi Province

Vegetation variation is an important topic of global change research, which is of great significance to deeply understand the relationship between vegetation and global change or human activities, and to disclose regional environment evolution and transition. The dynamics of forest vegetation in the mid-subtropical zone have received little attention. Thus, this paper takes the typical distribution area of the subtropical forest ecosystem — Jinggangshan City in Jiangxi Province as a study area. The changes within the year, inter-annual changes trend and spatial variation of the mid-subtropical forest vegetation index during the recent 10 years are analyzed based on MODIS NDVI data from 2000–2011 with the spatial resolution of 250 m. The Savitzky–Golay filter is used to smooth the original MODIS NDVI data. The forest distribution data is taken as the mask to eliminate the impact of non-forest cover area. The results showed that: (1) The changes of forest vegetation index within the year present a single peak mode with the maximum value in July; in the past 10 years, the forest vegetation index fluctuated with a downward trend; NDVI values were high and stable in summer and autumn, but low and unstable in winter; (2) The distribution of NDVI values of forest vegetation had great spatial difference. The NDVI values were low in the area nearby non-forest area in the north, where the non-forest vegetation is widely distributed. The NDVI values were high in the northwestern and southeastern areas. The distribution of NDVI values are comparatively even in the middle area with the NDVI values of more than 0.7; (3) High NDVI values (>0.75) distributed most in the northwestern and southeastern areas with the altitude of 400–600 m. Low NDVI values (<0.65) distributed mostly in the northern areas with the altitude less than 400 m. As for different altitude zones, NDVI values are high in the area with altitude of 400–800 m and low in the area with altitude below 400 m or above 1200 m. There is an agreement between the spatial distribution of the NDVI value of forest vegetation and regional topography, because topography has great impacts on the distribution of forest types which are different in coverage; (4) The NDVI value of forest vegetation presents a downward trend in the northern area, but an increasing trend in the southern area. The vegetation coverage tends to decrease with high population density and intensive distribution of township and scenic spot.     

Ground and remote sensing-based measurements of leaf area index in a transitional forest and seasonal flooded forest in Brazil

Leaf area index (LAI) is a key driver of forest productivity and evapotranspiration; however, it is a difficult and labor-intensive variable to measure, making its measurement impractical for large-scale and long-term studies of tropical forest structure and function. In contrast, satellite estimates of LAI have shown promise for large-scale and long-term studies, but their performance has been equivocal and the biases are not well known. We measured total, overstory, and understory LAI of an Amazon-savanna transitional forest (ASTF) over 3 years and a seasonal flooded forest (SFF) during 4 years using a light extinction method and two remote sensing methods (LAI MODIS product and the Landsat-METRIC method), with the objectives of (1) evaluating the performance of the remote sensing methods, and (2) understanding how total, overstory and understory LAI interact with micrometeorological variables. Total, overstory and understory LAI differed between both sites, with ASTF having higher LAI values than SFF, but neither site exhibited year-to-year variation in LAI despite large differences in meteorological variables. LAI values at the two sites have different patterns of correlation with micrometeorological variables. ASTF exhibited smaller seasonal variations in LAI than SFF. In contrast, SFF exhibited small changes in total LAI; however, dry season declines in overstory LAI were counteracted by understory increases in LAI. MODIS LAI correlated weakly to total LAI for SFF but not for ASTF, while METRIC LAI had no correlation to total LAI. However, MODIS LAI correlated strongly with overstory LAI for both sites, but had no correlation with understory LAI. Furthermore, LAI estimates based on canopy light extinction were correlated positively with seasonal variations in rainfall and soil water content and negatively with vapor pressure deficit and solar radiation; however, in some cases satellite-derived estimates of LAI exhibited no correlation with climate variables (METRIC LAI or MODIS LAI for ASTF). These data indicate that the satellite-derived estimates of LAI are insensitive to the understory variations in LAI that occur in many seasonal tropical forests and the micrometeorological variables that control seasonal variations in leaf phenology. While more ground-based measurements are needed to adequately quantify the performance of these satellite-based LAI products, our data indicate that their output must be interpreted with caution in seasonal tropical forests.     

中国东北多年冻土区植被生长季NDVI时空变化及其对气候变化的响应

多年冻土对气候变化十分敏感,尤其是多年冻土上的植被,易受气候变化影响.东北多年冻土区位于北半球中、高纬度地区,是我国第二大多年冻土区,同时也是欧亚大陆多年冻土带的南缘.本文基于1981—2014年LTDR和MODIS 两种数据集对东北多年冻土区植被生长季归一化植被指数(NDVI)时空变化特征进行分析,同时结合气象数据,分析植被对气候变化的响应.结果表明: 研究期间,东北多年冻土区植被生长季平均NDVI呈显著增加趋势,年增加0.0036.空间逐像元NDVI变化趋势具有明显的空间异质性. 研究区80.6%区域的植被NDVI具有显著增加趋势(P<0.05),7.7%的区域呈显著减少趋势(P<0.05).不同类型多年冻土区的植被NDVI增加强度不同,依次为连续多年冻土区>不连续多年冻土区>稀疏岛状多年冻土区>季节冻土区,NDVI增加趋势最大值(>0.004)所占的面积比例依次为连续多年冻土区>不连续多年冻土区>稀疏岛状多年冻土区>季节冻土区.多年冻土全区尺度下,植被生长季NDVI与平均气温呈显著正相关关系(r=0.79,P<0.01),与降水呈较弱的负相关,表明气温是东北多年冻土区植被生长的主控因子.研究区的多年冻土退化对植被生长起到积极的促进作用,尤其是在连续多年冻土区和不连续多年冻土区,植被NDVI增加强度更为剧烈.尽管增加的地表温度可以加快植被生长、增加植被覆盖,但长期来看,多年冻土退化甚至消失会阻碍植被生长.     

1982-2003年东北林区森林植被NDVI与水热条件的相关分析

以气象站点为研究单元,将1982—2003年东北林区森林植被月平均、季平均和年平均NDVI数据与其对应的水热条件(温度和降水)进行相关、偏相关和复相关分析。结果表明:温度是影响东北林区森林植被NDVI的最主要气候因子。春季、秋季不同森林植被平均NDVI与温度和降水呈极显著相关(P<0.01),其与温度的相关性高于其与降水的相关性。寒温带针叶林NDVI在生长季与温度和降水呈极显著相关(P<0.01),其与降水的相关性略高于其与温度的相关性,而全年温度对寒温带针叶林生长的影响高于降水。寒温带针叶林NDVI在4月份与降水的时滞偏相关性高于其他月份,相关系数达-0.385。温带针阔叶混交林NDVI在4—7月与温度的时滞偏相关性较高,相关系数分别为0.581,0.490,-0.266和-0.297。暖温带落叶阔叶林NDVI在4月份与温度的时滞偏相关性高于其他月份,相关系数为0.571;在7月份与降水时滞偏相关性高于其他月份,相关系数为-0.367。森林植被生长增长阶段NDVI受综合水热条件(温度和降水)的滞后影响显著。     

怒江-萨尔温江流域植被覆盖时空变化趋势及驱动力

植被作为陆地生态系统的主要组成部分,对区域生态系统环境变化、全球碳循环和气候调节具有非常重要的作用。怒江-萨尔温江流域是东南亚最重要的跨境河流之一,其植被变化会影响区域生态系统和气候。研究以怒江-萨尔温江流域为研究区,基于2000—2021年MODIS NDVI数据,利用BFAST模型、Hurst指数以及地理探测器研究了其植被覆盖时空演变趋势和未来可持续性以及驱动因子。结果表明：(1)2000—2021年,怒江-萨尔温江流域植被覆盖总体呈波动上升趋势,多年平均植被覆盖度FVC(Fractional Vegetation Cover)为0.73,以高植被覆盖和较高植被覆盖为主。植被分布具有明显的空间异质性,下游和中游植被覆盖明显优于上游。(2)BFAST趋势表明,近22年怒江-萨尔温江流域植被覆盖改善和退化的区域面积占比分别为71.24%、28.76%,改善的区域远大于退化的区域,说明研究区植被得到较好的保护。Hurst指数显示,未来植被将持续改善和退化的区域占比分别为94.89%、2.76%。BFAST与Hurst二者叠加共耦合了17种植被覆盖的未来趋势情形,整体上未来植被呈持续改善为...     

发展NECT土地覆盖特征数据集的原理、方法和应用

着重探讨了建立中国东北样带 (NortheastChinatransect, NECT) 土地覆盖特征数据集的原理、方法及其在全球变化研究方面的重要应用。NECT土地覆盖特征数据集是以多时相的 1km分辨率的NOAA/AVHRR归一化植被指数NDVI (Normalizeddifferencevegetationindex) 数字影像为基础, 同时采用高程、气候、土壤、植被、土地利用、土地资源、生态区域、行政边界、经济、社会等多源数据作为数据源, 并经过标准化处理 (如数字化、空间插值、几何配准、投影转换 ) 集成而成。在土地覆盖特征数据集的主要应用方面, 如 :1) 利用多时相、1km分辨率的NOAA/AVHRR影像完成了中国东北样带土地覆盖分类图。一级分类系统包括森林、草原、荒漠和沙地、灌丛、农田、混合覆盖 类型、城镇和水体等 8类, 二级分类体系包括 12类。经过地面采样进行精度检验, 分类精度达到 81.6 1%。 2 ) 对主要植被类型的植物生长季变化进行的研究。利用多时相的遥感影像构造了能够反映植被年际、季节生长变化的遥感植被指数ND VImax、NDVI变幅xam以及NDVI的标准偏差x′s 等, 分析这 3个参数 1983~ 1999年的 17年中的变化情况。该数据集的建立是研究该样带土地覆盖特征及其变化规律的基础, 对基于样带的全球变化研究有重要的意义。     

Assessing the relative role of climate and human activities on vegetation cover changes in the up–down stream of Danjiangkou,China

气候与人类活动对丹江口上下游植被覆盖变化的影响 丹江口水库是南水北调中线工程的水源地,研究丹江口水库植被覆盖动态变化及其影响因素,对于了解南水北调中线工程的生态环境及制定保护措施具有重要意义。本研究采用归一化植被指数(NDVI)分析了1982–2018年丹江口大坝上下游植被的动态变化及其影响因素。研究结果表明,研究 时段内NDVI呈0.017year^-1的增加趋势(P < 0.05),显著增加的区域位于大坝上游河谷附近,显著减少的区 域主要分布在大坝下游流域和中心城区周围。气候变化和人类活动对NDVI变化的综合贡献率分别为92.03%和7.97%。大坝上游的人类活动主要体现在退耕还林等生态措施的实施,大坝下游的人类活动主要表现为城市扩张、建设用地占用耕地和林地等。     

NDVI‐based vegetation monitoring in Mau forest complex,Kenya

The normalized difference vegetation index (NDVI) measures vegetation health and density using plant reflectance characteristics recorded by satellite imagery. Dekadal NDVI data were obtained for January 1999–December 2009 from 1‐km resolution SPOT‐VEGETATION sensor for closed woody vegetation type in four blocks of the Mau forest complex. Vegetation response to yearly seasonal variations was plotted and used to compare deviations by specific years. Subnormal vegetation conditions were recorded by the standardized vegetation index (SVI) and persistently low SVI values indicated a drought season or degraded vegetation. The general linear trend of the vegetation was plotted for the study period to identify trends towards degradation or vegetation recovery. Analysis of variance was used to compare forest blocks and shows spatial vegetation variations and also among years to identify vegetation variations with time. Rainfall data recorded for 2002–2009 in east Mau were used to confirm rainfall‐related vegetation variations block. Results show that NDVI patterns within an year follow cyclic trends with a strong dependence on rainfall seasons. The forest vegetation indicated negligible changes over the study period but effects of extended dry periods in 2000 and 2009 were evident. There were significant differences (P < 0.05) in NDVI between forest blocks. East Mau had significantly inferior vegetation that can be attributed to forest type, level of human degradation prior to the study and the lower rainfall. There were significant variations (P < 0.05) of NDVI among years but the forests showed a natural resilience to disturbance and can retain original vegetation vigour once stress is removed. The study proposes further monitoring of the forests including other vegetation types that are more vulnerable to climatic variations and anthropogenic effects.     

Satellite‐based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air‐photo studies have documented recent changes in high‐latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24‐year (1986–2010) Landsat time series in a latitudinal transect across the boreal forest‐tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last 40 years. Using a per‐pixel (30 m) trend analysis, 30% of the observable (cloud‐free) land area experienced a significant (P < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak‐summer conditions. The average NDVI trend (0.007 yr^?1) corresponds to a leaf‐area index (LAI) increase of ~0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer. Across the entire transect, the area‐averaged LAI increase was ~0.2 during 1986–2010. A higher area‐averaged LAI change (~0.3) within the shrub‐tundra portion of the transect represents a 20–60% relative increase in LAI during the last two decades. Our Landsat‐based analysis subdivides the overall high‐latitude greening trend into changes in peak‐summer greenness by cover type. Different responses within and among shrub, graminoid, and tree‐dominated cover types in this study indicate important fine‐scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low‐biomass vegetation types over multi‐decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.     

1982-2012年中亚地区植被时空变化特征及其与气候变化的相关分析

干旱区植被生态系统对气候变化极为敏感,并且干旱区的植被变化研究对全球碳循环具有重要意义。然而近几十年来,中亚干旱区植被对气候变化的响应机制尚不甚明朗。利用归一化植被指数NDVI数据集和MERRA（Modern-Era Retrospective Analysis for Research and Applications）气象数据,采用经验正交函数（EOF,Empirical Orthogonal Function）和最小二乘法等方法系统分析了31a（1982-2012年）来中亚地区NDVI在不同时间尺度的时空变化特征。进一步分析和研究NDVI与气温和降水的相关性,结果表明：1982-2012年,中亚地区年NDVI总体呈现缓慢增长趋势,而1994年以后年NDVI呈现明显下降趋势,尤其在哈萨克斯坦北部草原地区下降趋势尤为突出。这可能是由于过去30年间,中亚地区降水累计量的持续减少造成的。NDVI的季节变化表明春季NDVI增长最为明显,冬季则显著下降。与平原区相比,中亚山区的NDVI值增长幅度最大,并且山区年NDVI与季节NDVI呈现显著增加趋势（P < 0.05）。中亚地区年NDVI与年降水量正相关,而年NDVI与气温变化存在弱负相关。年NDVI和气温的正相关中心在中亚南部地区,负相关中心则出现在哈萨克斯坦的西部和北部地区;NDVI和降水的相关性中心刚好与气温相反。此外,在近30年间的每年6月至9月,中亚地区NDVI与气温存在近一个月的时间延迟现象。本研究为中亚干旱区生态系统变化和中亚地区碳循环的估算提供科学依据。     

The greening and browning of Alaska based on 1982–2003 satellite data

Aim To examine the trends of 1982–2003 satellite‐derived normalized difference vegetation index (NDVI) values at several spatial scales within tundra and boreal forest areas of Alaska. Location Arctic and subarctic Alaska. Methods Annual maximum NDVI data from the twice monthly Global Inventory Modelling and Mapping Studies (GIMMS) NDVI 1982–2003 data set with 64‐km² pixels were extracted from a spatial hierarchy including three large regions: ecoregion polygons within regions, ecozone polygons within boreal ecoregions and 100‐km climate station buffers. The 1982–2003 trends of mean annual maximum NDVI values within each area, and within individual pixels, were computed using simple linear regression. The relationship between NDVI and temperature and precipitation was investigated within climate station buffers. Results At the largest spatial scale of polar, boreal and maritime regions, the strongest trend was a negative trend in NDVI within the boreal region. At a finer scale of ecoregion polygons, there was a strong positive NDVI trend in cold arctic tundra areas, and a strong negative trend in interior boreal forest areas. Within boreal ecozone polygons, the weakest negative trends were from areas with a maritime climate or colder mountainous ecozones, while the strongest negative trends were from warmer basin ecozones. The trends from climate station buffers were similar to ecoregion trends, with no significant trends from Bering tundra buffers, significant increasing trends among arctic tundra buffers and significant decreasing trends among interior boreal forest buffers. The interannual variability of NDVI among the arctic tundra buffers was related to the previous summer warmth index. The spatial pattern of increasing tundra NDVI at the pixel level was related to the west‐to‐east spatial pattern in changing climate across arctic Alaska. There was no significant relationship between interannual NDVI and precipitation or temperature among the boreal forest buffers. The decreasing NDVI trend in interior boreal forests may be due to several factors including increased insect/disease infestations, reduced photosynthesis and a change in root/leaf carbon allocation in response to warmer and drier growing season climate. Main conclusions There was a contrast in trends of 1982–2003 annual maximum NDVI, with cold arctic tundra significantly increasing in NDVI and relatively warm and dry interior boreal forest areas consistently decreasing in NDVI. The annual maximum NDVI from arctic tundra areas was strongly related to a summer warmth index, while there were no significant relationships in boreal areas between annual maximum NDVI and precipitation or temperature. Annual maximum NDVI was not related to spring NDVI in either arctic tundra or boreal buffers.