黄河三角洲植被空间分布特征及其环境解释

为了解黄河三角洲地区植被空间分布与环境因子之间的关系,通过局地植被样方调查、区域遥感影像提取归一化植被指数(NDVI)及地形高度、地下水位埋深、表层土壤Cl~-含量等环境数据采集,综合样地植被与环境数据进行了除趋势对应分析(DCA)和除趋势典范对应分析(DCCA),并对区域NDVI与主要环境变量进行了单因子相关性分析和多元逐步回归分析。结果显示:DCA排序可将黄河三角洲植被分为翅碱蓬、柽柳-翅碱蓬、芦苇-柽柳、芦苇4个主要群落类型(群丛),DCCA与DCA排序图总体相似,但DCCA更清晰地表明其第一轴主要代表的是潜水Cl~-浓度等关键水盐因子,且随着水土环境系统盐分含量的减小,群落由翅碱蓬逐渐向芦苇演变。区域典型植被群落和NDVI分布格局与变化趋势受地下水位埋深和潜水Cl~-浓度2个环境因素影响较大(NDVI与2个环境变量间建立的二元回归方程R~2=0.57),而土壤Cl~-含量的植被效应实际上受地下水位埋深和潜水Cl~-浓度的影响。在区域地下水普遍浅埋条件下,地下水成为影响植被生长与分布的生态环境最敏感要素,而地下水位埋深和潜水Cl~-浓度是这一要素中的2个关键因子,尤其是后者梯度变化对天然植被分布格局起重要的控制作用。     

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。     

A novel and efficient ligand-based virtual screening approach using the HWZ scoring function and an enhanced shape-density model

In this work, we extend our previous ligand shape-based virtual screening approach by using the scoring function Hamza–Wei–Zhan (HWZ) score and an enhanced molecular shape-density model for the ligands. The performance of the method has been tested against the 40 targets in the Database of Useful Decoys and compared with the performance of our previous HWZ score method. The virtual screening results using the novel ligand shape-based approach demonstrated a favorable improvement (area under the receiver operator characteristics curve AUC?=?.89?±?.02) and effectiveness (hit rate HR^1%?=?53.0%?±?6.3 and HR^10%?=?71.1%?±?4.9). The comparison of the overall performance of our ligand shape-based method with the highest ligand shape-based virtual screening approach using the data fusion of multi queries showed that our strategy takes into account deeper the chemical information of the set of active ligands. Furthermore, the results indicated that our method are suitable for virtual screening and yields superior prediction accuracy than the other study derived from the data fusion using five queries. Therefore, our novel ligand shape-based screening method constitutes a robust and efficient approach to the 3D similarity screening of small compounds and open the door to a whole new approach to drug design by implementing the method in the structure-based virtual screening.     

基于地理探测的黄土高原植被生长对气候的响应

为探讨黄土高原不同植被类型对气候变化的响应机制,以2002-2019年黄土高原归一化植被指数(NDVI)数据为基础,利用趋势分析、Hurst指数、地理探测器等方法分析不同植被类型NDVI变化趋势及其与气象因子的关系.结果 表明:2002-2019年,黄土高原不同植被类型NDVI以增长趋势和同向中持续性为主,仅栽培植被在...     

Edge effects on vegetation and soils in a Virginia old-field

We investigated the effect of proximity to forest edge on plant community structure and ecosystem properties during succession, using field measurements of leaf area index (LAI), species composition, and soil carbon. Data were collected along four transects within a 14-year-old temperate successional field in north-central Virginia over the 2000 growing season. Additionally, the normalized difference vegetation index (NDVI) was calculated from LANDSAT 7 satellite data at a resolution of 30 m for the entire field. Results showed that relative frequencies of trees increased with proximity to forest edge suggesting a more advanced stage of succession in areas close to the adjacent secondary forest. Significant negative relationships were observed between distance from forest edge and both peak season LAI and NDVI. LAI and NDVI values within 60 m of the forest edge, however, were not significantly different from those values in the adjacent mature secondary forest, suggesting that some community level properties may take relatively short periods of time to reach undisturbed states. The presence of several key plant species, particularly Celastrus scandens (climbing bittersweet), exhibited a strong control on the spatial variability of LAI and potentially the aboveground net primary production. Soil carbon levels did not show a significant increase at sites close to the adjacent secondary forest (relative to an adjacent crop field), as seen with LAI and NDVI, suggesting no recovery of soil carbon in these systems after 14 years. This study points to the complexity of factors that influence spatial patterns of succession in old-fields and suggests that invasive species may play an important role in successional pathways and carbon cycling.     

中国东部南北样带主要植被类型归一化植被指数对气候变化的响应及不同时间尺度的差异性

植被的动态变化及其与环境的关系已成为全球变化研究的热点问题。陆地样带是进行全球变化驱动因素梯度分析的有效途径。该研究依托中国东部南北样带(NSTEC), 对南北样带不同时间尺度的气候因子和植被活动变化特征进行了分析, 并重点阐述了具有代表性的12种植被类型对气候因子的响应方式。研究结果表明: 南北样带植被的归一化植被指数(NDVI)的变化同时受控于气温和降水, 但是在不同的空间和时间尺度上植被NDVI的响应方式各异。在年时间尺度上, 只有温带落叶灌丛(TDS)的NDVI受气温控制; 而温带禾草草原(TGS)和亚热带和热带针叶林(STCF)的NDVI同时受气温和降水调控。其他植被类型的年NDVI与年平均气温和年总降水量没有直接显著的联系, 而受年内气温变化和降水分配状况的影响更大。在月时间尺度上, NDVI与气温的关系在不同类型植被之间存在很大差异。一般而言, 植被NDVI与前4个月内的气温关系最为密切, 并且从1月份到4月份气温的滞后时长在缩短。其中, 温带针叶林(TCF)、温带落叶阔叶林(TDBF)、TDS、STCF和亚热带热带草丛(STG)等植被类型, 5-8月的NDVI与气温普遍呈负相关关系。草原和灌丛植被类型当月NDVI与当月降水量主要以正相关为主, 而森林类型当月NDVI与当月降水量主要以负相关为主。     

近20年亚欧大陆植被指数(NDVI)对大尺度气候变化的敏感性

利用1982～2000年4～10月的AVHRR-NDVI数据,分析了大尺度的气候(温度)变化对欧亚大陆植被状况的影响.分析方法为奇异值分解,从温度和NDVI的年际变化中检测出二者最重要和最密切的大尺度空间相关特征.用每个奇异值的平方占总的协方差平方和的比例(解释率),可以度量每对模态的重要性.春季(4和5月)、夏季和秋季(9和10月)的解释率分别是60.9%、39.5%和24.6%,这说明整体上春季植被状况对温度的敏感性高于夏季和秋季.奇异值分解的显著模态中心是二者关系最密切的地区,也就是NDVI对温度最敏感的地区,春季为西西伯利亚和东欧东北部,敏感性为 0.308 0 NDVI/℃;夏季没有特别突出的敏感中心,选择与计算春季相同格点数的高值中心,其敏感性为 0.248 0 NDVI/℃;秋季敏感中心在亚洲东部高纬度地区,相同格点大小范围(110°～140° E,55°～65°N)平均敏感性为 0.087 5 NDVI/℃.这种大尺度的NDVI-气温的关系及其敏感性非常稳定,并不随使用的NDVI的空间分辨率的改变而改变.     

近20年亚欧大陆植被指数(NDVI)对大尺度气候变化的敏感性(英文)

The influence of climate change on the terrestrial vegetation health (condition) is one of themost significant problems of global change study. The vegetation activity plays a key role in the globalcarbon cycle. The authors investigated the relationship of the advanced very high resolution radiometer-normalized difference vegetation index (AVHRR-NDVI) with the large-scale climate variations on the inter-annual time scale during the period 1982-2000 for the growing seasons (April to October). A singular valuedecomposition analysis was applied to the NDV! and surface air temperature data in the time-domain todetect the most predominant modes coupling them. The first paired-modes explain 60.9%, 39.5% and 24.6%of the squared covariance between NDV! and temperature in spring (April and May), summer (June andAugust), and autumn (September to October), respectively, which implies that there is the highest NDVIsensitivity to temperature in spring and the lowest in autumn. The spatial centers, as revealed by themaximum or minimum vector values corresponding to the leading singular values, indicate the highsensitive regions. Only considering the mode 1, the sensitive center for spring is located in westernSiberia and the neighbor eastern Europe with a sensitivity of about 0.308 0 NDVI/℃. For summer, thereare no predominantly sensitive centers, and on average for the relatively high center over 100^o-120^o E by 45^o-60^o N, the (110^o-140^o E,55^o-65^oN)sitivity is 0.248 0 NDVI/℃. For autumn, the center is located over the high latitudes ofeastern Asia (110^o-140^o E, 55^o-65^o N), and the sensitivity is 0.087 5 NDVI/℃. The coherent patters asrevealed by the singular decomposition analysis remain the same when coarser resolution NDVI data wereused, suggesting a robust and stable climate/vegetation relationship.     

nter-annual Changes in Eurasian Continent NDVI and Its ensitivity to the Large-scale Climate Variations in the Last 20 Years

The influence of climate change on the terrestrial vegetation health (condition) is one of the most significant problems of global change study. The vegetation activity plays a key role in the global carbon cycle. The authors investigated the relationship of the advanced very high resolution radiometer-normalized difference vegetation index (AVHRR-NDVI) with the large-scale climate variations on the inter-annual time scale during the period 1982-2000 for the growing seasons (April-October). A singular value decomposition analysis was applied to the NDVI and surface air temperature data in the time-domain to detect the most predominant modes coupling them. The first paired-modes explain 60.9%, 39.5% and 24.6% of the squared covariance between NDVI and temperature in spring (April-May), summer (June-August), and autumn (September-October), respectively, which implies that there is the highest NDVI sensitivity to temperature in spring and the lowest in autumn. The spatial centers, as revealed by the maximum or minimum vector values corresponding to the leading singular values, indicate the high sensitive regions. Only considering the mode 1, the sensitive center for spring is located in western Siberia and the neighbor eastern Europe with a sensitivity of about 0.308 0 NDVI/℃. For summer, there are no predominantly sensitive centers, and on average for the relatively high center over 1000-1200 E by 450-600 N, the sensitivity is 0.248 0 NDVI/℃. For autumn, the center is located over the high latitudes of eastern Asia (1100-1400 E, 550-650 N), and the sensitivity is 0.087 5 NDVI/℃. The coherent patters as revealed by the singular decomposition analysis remain the same when coarser resolution NDVI data were used, suggesting a robust and stable climate/vegetation relationship.     

Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008

Changes in vegetative growing seasons are dominant indicators of the dynamic response of ecosystems to climate change. Therefore, knowledge of growing seasons over the past decades is essential to predict ecosystem changes. In this study, the long‐term changes in the growing seasons of temperate vegetation over the Northern Hemisphere were examined by analyzing satellite‐measured normalized difference vegetation index and reanalysis temperature during 1982–2008. Results showed that the length of the growing season (LOS) increased over the analysis period; however, the role of changes at the start of the growing season (SOS) and at the end of the growing season (EOS) differed depending on the time period. On a hemispheric scale, SOS advanced by 5.2 days in the early period (1982–1999) but advanced by only 0.2 days in the later period (2000–2008). EOS was delayed by 4.3 days in the early period, and it was further delayed by another 2.3 days in the later period. The difference between SOS and EOS in the later period was due to less warming during the preseason (January–April) before SOS compared with the magnitude of warming in the preseason (June–September) before EOS. At a regional scale, delayed EOS in later periods was shown. In North America, EOS was delayed by 8.1 days in the early period and delayed by another 1.3 days in the later period. In Europe, the delayed EOS by 8.2 days was more significant than the advanced SOS by 3.2 days in the later period. However, in East Asia, the overall increase in LOS during the early period was weakened in the later period. Admitting regional heterogeneity, changes in hemispheric features suggest that the longer‐lasting vegetation growth in recent decades can be attributed to extended leaf senescence in autumn rather than earlier spring leaf‐out.