, 焦珂伟Revealing the climatic impacts on spatial heterogeneity of NDVI in China during 1982-2013
GAOJiangbo, JIAOKewei收稿日期:2017-08-21
修回日期:2018-12-6
网络出版日期:2019-03-25
版权声明:2019《地理学报》编辑部本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
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1 引言
植被在陆—气之间的能量传输以及生态系统服务维持和优化等过程中发挥着重要作用[1,2],是全球环境变化的敏感指示器。归一化植被指数（NDVI）是指示大尺度植被覆盖和生产力的重要指标,其数值的大小可以表征植被活动强弱[3,4],进而反映出生态系统结构和功能属性特征,广泛应用于地理学、生态学、全球变化生态学等领域的研究[5,6]。作为植物生长发育所必需的外界环境因子,气候要素（温度和水分等）对植被活动的变化起到重要作用[7,8]。气候条件的改变在促进植被生长发育以及生理生化作用的同时,还可能会对植被活动产生不利影响[9]。因此,揭示植被活动与气候变化的空间非平稳关系及其响应格局,可为应对气候变化、提升生态系统适应能力提供理论依据,成为当前全球变化研究的重要内容[10]。在全球与区域尺度上,气候变化与植被活动之间的关系得到了广泛关注[11,12]。20世纪全球潜在植被的净初级生产力（NPP）在气候变化的影响下增长13%[13],而在区域尺度上,植被活动的响应过程取决于气候条件与背景环境的空间异质性[14,15],例如干旱区的植被NPP在集中降水的影响下有所增加[16],而湿润半湿润地区的植被NPP则在降水增加的条件下受到抑制[17,18];在大多数湿冷的北方地区,最高温对植被NDVI的影响为正相关,在温带干旱地区则表现为负相关,而NDVI对最低温的响应特征表现更为复杂[19]。此外,植被活动对气候变化响应研究中时间尺度的选择同样具有重要意义。冬季和春季温度的升高促进光合作用、延长植被生长期,有利于植被的生长发育和营养物质的累积,而NDVI对秋季温度的升高则呈现出负面效应[20]。因而,影响植被活动的气候因素在时空尺度上的叠加效应,导致了气候—植被关系呈现出不确定性和复杂性特征。
当前,植被活动与气候变化的空间非平稳关系及其响应格局研究,主要聚焦于植被活动与气候变化的趋势性分析与作用过程识别,而针对宏观尺度气候因子与植被活动的时空变异性,通过空间统计分析手段,揭示区域性气候主导因子以及植被活动动态响应机制,从整体上分析NDVI与气候变化关系的空间非平稳性等研究工作,仍有待推进[21,22,23]。基于此,本文应用地理加权回归（GWR）等方法,研究1982-2013年中国植被NDVI与不同气候要素及其变率的空间非平稳关系,以辨识气候要素对植被活动的控制作用及其分布区域,并剖析植被活动动态响应气候变化的空间格局,以期推进植被-气候关系研究领域的深化和拓展。
2 数据来源与研究方法
2.1 气候指标
本文选取的气候指标包括温度指标（平均气温、最高气温、最低气温）以及水分指标（降水量、相对湿度）。气候数据使用中国气象科学数据共享服务网提供的地面气候资料月值数据集,剔除有数据缺失的站点后选取全国范围内652个气象站点,时间跨度为1982-2013年。对各月的温度和相对湿度数据进行算术平均,其中年均最高温和最低温分别为逐月最高、最低温度的年平均值,分别表征白天和夜间的温度状况,对降水量进行求和得到年际气候数据。随后使用Auspline软件将站点气候数据插值为50 km栅格数据,以便之后的分析运算。最后选取插值计算中未使用过的气象站点,随机选取时段,将其气候数据与相应插值格点内的数据进行对比验证,结果表明温度指标插值数据与实测值的相关系数均在0.99以上,而降水量和相对湿度指标的相关系数也达到了0.92和0.86,因此插值精度较好,能够在整体格局上满足研究的需求。此外,本文选择50 km空间分辨率的原因,一方面是保证研究数据在空间分析过程中能够通过共线性检验,另一方面是能够保持与已有研究（尤其是降水格点数据集）[24]的一致性和可比性。2.2 植被NDVI
NDVI数据常用来表征植被生长、覆盖及动态变化等植被活动状况[25]。本文选取1982-2013年GIMMS NDVI数据集作为植被活动指标,该数据集由美国航空航天局（NASA）全球监测与模型研究组（Global Inventory Monitoring and Modeling Studies）提供,空间分辨率为8 km,时间分辨率为15 d。GIMMS NDVI数据集具有精度高、序列长等特点,在全球及区域尺度植被变化的研究中得到广泛应用[26,27]。继而采取最大合成法（Maximum Value Composite, MVC）获得NDVI月值,以各月份的平均值作为该年份NDVI的结果,并使用年均NDVI来反映植被覆盖在气候变化驱动下的演变特征,进而分析其与气候变化的空间关系[20, 28]。为使空间分辨率与气候指标一致,使用ArcGIS 10.3软件进行重采样,将8 km空间分辨率的NDVI转化为50 km栅格数据。2.3 气候与植被NDVI的年际变化分析
本文采用基于栅格尺度的最小二乘法（OLS）分析气候与植被指标的年际变化趋势,通过ArcGIS 10.3中的栅格计算器得以实现。趋势分析的计算公式为：式中：θslope为线性回归的斜率,表示研究对象的变化趋势及速率;n表示研究年份的总数,这里为32;Yi表示第i年气候或植被指标的数值。
2.4 气候—植被NDVI空间非平稳性的表征方法
地理加权回归（Geographically Weighted Regression, GWR）是由Brunsdon等[29]提出的一种简单而实用的局域空间分析方法,有助于揭示研究区域内部空间关系的变化。GWR模型是对普通线性回归（如OLS）的拓展,该方法的参数是空间位置的函数,通过获取局部参数评估自变量与因变量关系在空间尺度上的变异。该模型的表达形式为：式中：yi、xik、εi分别代表空间上i点的因变量、自变量和随机误差;
GWR模型采用局部加权最小二乘法,其中权重是评估点到其他各观测点的空间距离的函数,具有距离衰减效应。参数公式为：
式中：β(μi, vi)是回归系数的无偏估计;W(μi, vi)为权重矩阵;X和Y分别代表自变量和因变量的矩阵。
GWR方法一般采用高斯模型作为权重函数,其中带宽是描述权重值与距离的函数。权重函数表达为：
式中：ωij为i点观测点j的权重;dij代表i到j的欧几里得距离;b是带宽。当观测点间的距离dij大于b值时,权重ωij等于0;而当dij等于0时,ωij等于1。对于最优带宽的选择,本文通过采用高斯模型,并利用AICc信息准则法确定最优带宽。AICc作为评价模型复杂性和精确度的指数,取值越低代表模型的模拟效果越好,通常两模型AICc插值大于3时,则具有较小AICc值模型的带宽b为最优带宽[30]。
3 结果与分析
3.1 中国植被NDVI与气候变化关系的非平稳性验证
GWR方法应用的前提是地理空间关联的自变量和因变量存在空间差异,即回归系数随着自变量的空间位置而变化[31]。为了验证植被NDVI及其动态与气候变化的关系在空间上是否平稳,分别对均值和变率的回归参数进行空间自相关分析（表1）。结果表明,本文所使用的GWR模型回归参数的自相关指数均大于0,说明选取的参数具有正的空间自相关,即这些回归参数在空间上具有非平稳性,且Z值均大于2.58,说明在0.01显著水平下具有统计意义。因此,GWR方法能够较为全面的体现出各气候要素在不同地理位置对NDVI及其动态的定量影响。Tab. 1
表1
表1各要素GWR回归参数的空间自相关指数Moran's I 与Z值
Tab. 1Moran's I and Z scores of regression coefficients for each factor in GWR model
| 回归参数 | 平均气温 | 最高气温 | 最低气温 | 总降水量 | 相对湿度 | |
|---|---|---|---|---|---|---|
| Moran's I | 均值 | 0.75 | 0.75 | 0.74 | 0.76 | 0.76 |
| 变率 | 0.68 | 0.73 | 0.70 | 0.70 | 0.63 | |
| Z | 均值 | 41.58 | 42.03 | 41.22 | 42.07 | 41.99 |
| 变率 | 34.18 | 39.72 | 36.64 | 37.28 | 30.54 | |
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3.2 中国植被NDVI与温度指标的空间非平稳关系
1982-2013年中国年均NDVI的空间格局分布如图1a所示。可以看出,在1982至2013年的30多年,中国年均NDVI呈现出由东南到西北方向递减的变化趋势。由NDVI与温度指标的地理加权回归结果可知,在东北、西北以及东南部地区,NDVI与气温在空间上呈负相关关系（图1b、1c、1d）,说明在这些地区,植被活动的空间分布随温度升高而受到抑制作用。
显示原图|下载原图ZIP|生成PPT图11982-2013年中国NDVI空间格局及其与温度指标的GWR空间回归参数
-->Fig. 1The spatial pattern of NDVI (a) and its spatial regression coefficients with temperature (b: average; c: maximum; d: minimum) in GWR model
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在东北和西北地区,3个温度指标对植被NDVI均起到了负面效应,在空间上植被活动的衰弱是由于气温的升高加速了土壤水分的蒸散,增强了干旱程度。而在东南地区,尤其是在巫山—雪峰山以及两湖平原一带,年均最低气温对植被NDVI的负面作用最为明显（图1d）,说明植被活动的抑制是由于夜间气温的升高促进了呼吸作用,加速了干物质的消耗所引起的。此外,除东南沿海的极少部分地区外,1982-2013年中国植被NDVI与水分指标在空间上呈正相关关系,说明降水和相对湿度等水分要素的变化总体上对植被活动起到积极作用。
3.3 中国不同区域植被NDVI空间分布的主导气候因子
为消除量纲影响,实现温度与水分指标之间的比较,需要将NDVI与气候指标的数值进行0~1标准化后再通过GWR计算标准化回归系数,得到植被活动对不同气候指标的响应格局。最后结合植被与气候指标的变化趋势,比较不同指标的标准化系数,确定影响程度最高的气候指标,识别出植被活动空间分布的主导气候因子及其作用区域（图2）。
显示原图|下载原图ZIP|生成PPT图2不同气候要素对中国植被活动影响的作用区域
-->Fig. 2The effect regions of climatic factors to vegetation activity in China
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从整体格局来看,在华北平原、长江中下游平原、四川盆地以及云贵高原等地区,植被NDVI受到温度的控制作用更为显著（表2）。其中年均最高温度对植被NDVI的作用面积最大,占全国总面积的24.81%。在多数地区,温度在空间上逐步升高促进了植被活动的增强,但位于西北和西南部分地区的温度作用区域中,植被NDVI与年均温和年均最高气温在空间上呈负相关关系（图1b、1c）,因此,这些地区植被活动的减弱主要是受到温度升高的驱动作用。
Tab. 2
表2
表2不同气候要素作用区域在中国的分布比例
Tab. 2The distribution proportion of effect regions of climatic factors in China
| 平均气温 | 最高气温 | 最低气温 | 总降水量 | 相对湿度 | 其它要素 | |
|---|---|---|---|---|---|---|
| 栅格数量 | 187 | 743 | 131 | 1278 | 454 | 202 |
| 比例(%) | 6.24 | 24.81 | 4.37 | 42.67 | 15.16 | 6.74 |
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在东北平原、内蒙古高原、吕梁山、天山以及青藏高原等地区,植被NDVI空间特征受到水分的影响则更加强烈。其中降水对植被NDVI的作用区域分布最广,占全国总面积的42.67%,而相对湿度对植被NDVI作用区域的分布则较为零散（图2）。结合过去30多年的降水变化趋势（图3b）进行分析可以看出,东北平原西北部的降水减少,植被活动在空间上的减弱受到水分条件的驱动作用,而气温的升高又使得蒸散加快,导致干旱化,水热条件对于植被活动的抑制作用进一步加剧;而在吕梁山一带地区NDVI上升趋势最为明显（图3a）,降水同样呈现出上升趋势,充沛的水分和热量对植被活动逐步增强起到了推动作用。
显示原图|下载原图ZIP|生成PPT图31982-2013年中国NDVI、降水量与相对湿度的变化趋势分布
-->Fig. 3The spatial distribution of NDVI (a), precipitation (b) and relative humidity trend (c) during 1982-2013
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3.4 植被NDVI动态对气候变率的响应格局
由气候与植被NDVI变率的GWR计算结果可以看出,NDVI变率与3个温度指标变率均为空间负相关关系的地区主要分布在松嫩平原至大兴安岭南部,以及东部和南部沿海地区（图4）。除东部沿海部分地区外,其余地区的NDVI略有上升（图3a）,即增温速率越快的地方,植被活动的增强速率越慢。然而这些地区大部分没有位于温度指标的作用区域（图2）,因此植被活动受到水分或其它因素的影响程度要大于温度。可见在这些地区,增温速率的升高加速了土壤水分以及植被自身水分的散失,对区域性植被活动起到了抑制作用。
显示原图|下载原图ZIP|生成PPT图4中国NDVI变率与不同温度指标变率的GWR空间回归参数
-->Fig. 4The spatial regression coefficients between NDVI variability and temperature variability (a: average; b: maximum; c: minimum) from GWR model
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在云贵高原北部以及长江中下游平原西部,NDVI的变化多为上升趋势（图3a）。NDVI变率与最低气温变率在空间上为正相关关系,而与平均/最高气温变率却为负相关（图4）,因此,植被活动受到温度变率的不利影响主要是由最高气温变率所引起。在空间上,最高气温变率的逐步增加,通过增强植被呼吸作用、减弱光合作用的方式,对植被活动产生抑制作用。这两个地区的水分指标均为下降趋势（图3b、3c）,NDVI变率对水分变率的空间关系有所不同：前者的NDVI变率与降水和湿度变率以空间负相关影响为主,即水分要素下降速率快的区域,植被活动增强的速率越快;而后者的NDVI变率对水分变率均以正相关关系为主,即植被活动的增强速率随着水分下降速率的增加而减慢（图5）。
显示原图|下载原图ZIP|生成PPT图5中国NDVI变率与不同水分指标变率的GWR空间回归参数
-->Fig. 5The spatial regression coefficients between NDVI variability and moisture variability from GWR model
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在东北的大兴安岭北部、长白山地区,NDVI与水分指标的变化均呈现出下降趋势（图3）。NDVI变率与最高气温变率在空间上为正相关关系,而与平均/最低气温变率却为负相关（图4）,因此,植被活动受到温度变率的不利影响主要是由最低气温变率所引起。最低气温变率的提高通过加速植被夜间同化速率的方式来加快生物量的消耗,成为植被活动减弱的主要驱动因素。同时植被NDVI变率与水分变率在空间上呈正相关关系（图5）,说明该地区植被活动的减弱受到温度和水分速率变化的共同影响;而同样是在最低温度变率影响较为显著的区域,黄土高原地区的NDVI与降水均有所上升（图3a、3b）,NDVI变率对降水变率的关系同样为正相关（图5a）,因此该地区植被活动的增强受到降水变率的影响要更为显著,在一定程度上削弱了温度变率对植被活动的抑制效应。这也与该地区处于降水作用区域的结果相一致（图2）。
4 结论与讨论
中国不同区域的已有研究表明,随着温度的升高,植被活动对温度的响应越来越明显[32,33]。在达到光合作用最适宜温度前,温度的升高会促进光合作用[34];当超过这一温度时,一方面会提高植被的呼吸作用,加速营养物质的消耗,另一方面会引起水分蒸散的加快,干物质的积累减少[35]。此外,最高气温和最低气温对植被活动的作用过程在形式上也存在差异。水分的变化可以在一定程度上调节植被活动,而水分的增加也可能通过云量和相对湿度的增加抑制植被活动。因此,植被活动在响应气候变化过程中体现出非线性过程和复合性效应,需要从多因子综合分析的角度检测植被活动空间分布与时间动态规律。本文模拟了植被活动与气候要素的空间非平稳关系,并基于NDVI动态与水热指标变率的回归,厘清了不同区域的气候主导因子及其作用机制。水分对植被NDVI的主导作用区域主要集中在我国北方以及青藏高原地区,这些地区的植被生长受到水分的限制,降水和湿度的增加能够促进植被生长,而温度升高则可能引起水分蒸散加强,加剧干旱趋势[36];温度对植被NDVI的作用区域则集中在华东、华中及西南地区,这些地区的土壤水分含量相对高,降水充沛,温度的升高有利于植被生长季的延长和干物质量的积累,因此植被活动与温度的关系更为密切[5]。
气候变率的回归分析是植被活动对气候要素响应的有力补充,体现了植被活动对气候变化响应的动态过程,能够更好的解释温度和水分与植被活动之间的动态关系。增温速率的升高对植被NDVI起到抑制作用,且不同温度指标的影响程度有所不同,区域差异十分明显;而水分变率的响应格局分布不均,在一定程度上能够平衡植被生长所必需的水热组合,对植被活动状况起到重要的调节作用。植被活动的强弱取决于光合、呼吸过程对气候要素的响应速率[37],当光合速率的增加超过呼吸作用时,气候变率的正相关作用得以体现。可见,以植被NDVI的变化和气候变率作为研究对象进行分析,能够更好的体现植被活动动态与气候变化关系,突出了气候变化对植被活动机制的影响。
然而,植被NDVI受到气候和非气候因素的综合影响,本文在关注不同区域NDVI空间异质性的影响因子时仅考虑了气候要素,事实上,许多其它的环境因素也会影响到植被活动的空间异质性,包括地形和土壤条件等,例如某些地区的植被覆盖与高程具有显著正相关,植被退化的地区主要出现在低海拔和低坡度上[38],而土壤理化性质的差异也会影响植被属性与类型的空间异质性[39]。此外,大规模的生态工程也是影响生态环境的重要因素[31, 40]。本文所采用的GWR模型,其内涵是基于局部范围的空间回归,而人类活动在邻域范围内具有一定程度的相似性,故通过该方法能够揭示气候因子对于植被NDVI空间异质性的影响;进而,由于GWR方法难以完全规避人类活动对生态系统的影响,因而通过本文所获得的植被NDVI与气候因子空间回归参数也能在一定程度上间接反映人类活动的影响程度及其空间差异性。
The authors have declared that no competing interests exist.
参考文献 原文顺序
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| [1] | . Vegetation coverage is an important indicator ofweighting ecological environment;grassland ecosystem plays animportant role in regional ecological safety and sustainabledevelopment.Statistics was used to analysis the grasslandcoverage changes in Maqu County.Results showed that:1)thegrassland coverage show decreasing trend overall,and has periodcharacteristics;2)changes in grass coverage both have positiveand negative conversion,depending on using rationality ofgrassland resource;3)Climate warming and drying andirrational exploitation activities are the main driving factors ofecological environment deterioration;4)Prohibition grazingproject have obvious effect on grassland restoration. |
| [2] | . 生物多样性和生态系统服务评估是生态系统管理与决策制定的重要依据,指标体系是开展评估的主要工具.中国在生物多样性与生态系统服务评估指标体系建设方面,由于没有形成统一的指标体系和技术方法,导致不同区域间的评估结果可比性差,区域和国家尺度上的集成研究难以开展.因此,构建一套适用于中国国家尺度的科学化、系统性和规范化的生物多样性和生态系统服务评估指标体系,便成为当前迫切需要研究解决的问题.本文参考国内外生物多样性与生态系统服务评估的主要研究成果,在充分考虑“生物多样性—生态系统结构—过程与功能—服务”级联关系基础上,建立了生物多样性与生态系统服务评估指标体系构建的主要原则,构建了中国生物多样性与生态系统服务评估指标体系. . 生物多样性和生态系统服务评估是生态系统管理与决策制定的重要依据,指标体系是开展评估的主要工具.中国在生物多样性与生态系统服务评估指标体系建设方面,由于没有形成统一的指标体系和技术方法,导致不同区域间的评估结果可比性差,区域和国家尺度上的集成研究难以开展.因此,构建一套适用于中国国家尺度的科学化、系统性和规范化的生物多样性和生态系统服务评估指标体系,便成为当前迫切需要研究解决的问题.本文参考国内外生物多样性与生态系统服务评估的主要研究成果,在充分考虑“生物多样性—生态系统结构—过程与功能—服务”级联关系基础上,建立了生物多样性与生态系统服务评估指标体系构建的主要原则,构建了中国生物多样性与生态系统服务评估指标体系. |
| [3] | . Climate change is a major driver of vegetation activity but its complex ecological relationships impede research efforts. In this study, the spatial distribution and dynamic characteristics of climate change effects on vegetation activity in China from the 1980s to the 2010s and from 2021 to 2050 were investigated using a geographically weighted regression (GWR) model. The GWR model was based on combined datasets of satellite vegetation index, climate observation and projection, and future vegetation productivity simulation. Our results revealed that the significantly positive precipitation-vegetation relationship was and will be mostly distributed in North China. However, the regions with temperature-dominated distribution of vegetation activity were and will be mainly located in South China. Due to the varying climate features and vegetation cover, the spatial correlation between vegetation activity and climate change may be altered. There will be different dominant climatic factors for vegetation activity distribution in some regions such as Northwest China, and even opposite correlations in Northeast China. Additionally, the response of vegetation activity to precipitation will move southward in the next three decades. In contrast, although the high warming rate will restrain the vegetation activity, precipitation variability could modify hydrothermal conditions for vegetation activity. This observation is exemplified in the projected future enhancement of vegetation activity in the Tibetan Plateau and weakened vegetation activity in East and Middle China. Furthermore, the vegetation in most parts of North China may adapt to an arid environment, whereas in many southern areas, vegetation will be repressed by water shortage in the future. |
| [4] | 正The global carbon cycle is one of the most important biogeochemical cycles. Through photosynthesis, green plants absorb CO2 from the atmosphere to produce organic matters, |
| [5] | . Variations in vegetation activity during the past 18 years in China were investigated using the normalized difference vegetation index (NDVI) derived from the 3rd generation time series dataset of NOAA-AVHRR from 1982 to 1999. In order to eliminate the effects of non-vegetation factors, we characterized areas with NDVI < 0.1 as “sparsely vegetated areas” and areas with NDVI ≥0.1 as “vegetated areas”. The results showed that increasing NDVI trends were evident, to varying extents, in almost all regions in China in the 18 years, indicating that vegetation activity has been rising in recent years in these regions. Compared to the early 1980s, the vegetated area increased by 3.5% by the late 1990s, while the sparsely vegetated area declined by 18.1% in the same period. The national total mean annual NDVI increased by 7.4% during the study period. Extended growing seasons and increased plant growth rates accounted for the bulk of these increases, while increases in temperature and summer rainfall, and strengthening agricultural activity were also likely important factors. NDVI changes in China exhibited relatively large spatial heterogeneity; the eastern coastal regions experienced declining or indiscernibly rising trends, while agricultural regions and western China experienced marked increases. Such a pattern was due primarily to urbanization, agricultural activity, regional climate characteristics, and different vegetation responses to regional climate changes. |
| [6] | . The normalised difference vegetation index (NDVI) is a useful tool for studying vegetation activity and ecosystem performance at a large spatial scale. In this study we use the Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) estimates to examine temporal variability of the NDVI across Australia. We aim to demonstrate a new method that reveals the moisture dependence of vegetation cover at different temporal resolutions. Time series of monthly GRACE TWS anomalies are decomposed into different temporal frequencies using a discrete wavelet transform and analysed against time series of the NDVI anomalies in a stepwise regression. The results show that combinations of different frequencies of decomposed GRACE TWS data explain NDVI temporal variations better than raw GRACE TWS alone. Generally, the NDVI appears to be more sensitive to interannual changes in water storage than shorter changes, though grassland-dominated areas are sensitive to higher-frequencies of water-storage changes. Different types of vegetation, defined by areas of land use type, show distinct differences in how they respond to the changes in water storage, which is generally consistent with our physical understanding. This unique method provides useful insight into how the NDVI is affected by changes in water storage at different temporal scales across land use types. |
| [7] | . Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. |
| [8] | . react-text: 350 i) to setup models framework to simulate 139 small rivers in Xinjiang Province; ii)to transform the hydrological parameters from existing calibrated watershed; to the ungauged watersheds; /react-text react-text: 351 /react-text |
| [9] | . <p>地表过程复杂多样、涉及广泛。针对当前地表过程研究现状，从地球系统科学和全球变化的视角讨论了与地表过程相关的一些概念及研究内涵。在分析单要素地表过程和多要素地表过程的基础上，从研究内容的综合性、多国合作的国际性、针对区域突出问题的区域性、以过程变化为核心的动态性及研究方法的多样性等方面论述了地表过程研究的特点,总结性地给出了国内外研究的总体趋势。在上述分析基础上，进一步从国际科学发展趋势、国家需要和信息、技术等方面分析了中国地表过程研究所面临的机遇；从多学科交叉、自然与人文过程的定量与有机耦合等方面讨论了地表过程研究所面临的挑战。</p> . <p>地表过程复杂多样、涉及广泛。针对当前地表过程研究现状，从地球系统科学和全球变化的视角讨论了与地表过程相关的一些概念及研究内涵。在分析单要素地表过程和多要素地表过程的基础上，从研究内容的综合性、多国合作的国际性、针对区域突出问题的区域性、以过程变化为核心的动态性及研究方法的多样性等方面论述了地表过程研究的特点,总结性地给出了国内外研究的总体趋势。在上述分析基础上，进一步从国际科学发展趋势、国家需要和信息、技术等方面分析了中国地表过程研究所面临的机遇；从多学科交叉、自然与人文过程的定量与有机耦合等方面讨论了地表过程研究所面临的挑战。</p> |
| [10] | . Global assessments of variation in plant functional traits and the way that these traits influence competitive interactions provide a launching pad for future ecological studies. See Article p.167 & Letter p.204 |
| [11] | . Global climate change has emerged as a major driver of ecosystem change. Here, we present evidence for globally consistent responses in vegetation dynamics to recent climate change in the world's mountain ecosystems located in the pan-tropical belt (30°N–30°S). We analyzed decadal-scale trends and seasonal cycles of vegetation greenness using monthly time series of satellite greenness (Normalized Difference Vegetation Index) and climate data for the period 1982–2006 for 47 mountain protected areas in five biodiversity hotspots. The time series of annual maximum NDVI for each of five continental regions shows mild greening trends followed by reversal to stronger browning trends around the mid-1990s. During the same period we found increasing trends in temperature but only marginal change in precipitation. The amplitude of the annual greenness cycle increased with time, and was strongly associated with the observed increase in temperature amplitude. We applied dynamic models with time-dependent regression parameters to study the time evolution of NDVI–climate relationships. We found that the relationship between vegetation greenness and temperature weakened over time or was negative. Such loss of positive temperature sensitivity has been documented in other regions as a response to temperature-induced moisture stress. We also used dynamic models to extract the trends in vegetation greenness that remain after accounting for the effects of temperature and precipitation. We found residual browning and greening trends in all regions, which indicate that factors other than temperature and precipitation also influence vegetation dynamics. Browning rates became progressively weaker with increase in elevation as indicated by quantile regression models. Tropical mountain vegetation is considered sensitive to climatic changes, so these consistent vegetation responses across widespread regions indicate persistent global-scale effects of climate warming and associated moisture stresses. |
| [12] | |
| [13] | . Net primary production (NPP), the difference between CO fixed by photosynthesis and CO lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (~ 100 150 g C.m-2.yr-1) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be ~28 Pg and ~46 Pg C/yr, respectively. The NCEAS model estimated an ~13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns. |
| [14] | . The global hydrological cycle is predicted to become more intense in future climates, with both larger precipitation events and longer times between events in some regions. Redistribution of precipitation may occur both within and across seasons, and the resulting wide fluctuations in soil water content (SWC) may dramatically affect plants. Though these responses remain poorly understood, recent research in this emerging field suggests the effects of redistributed precipitation may differ from predictions based on previous drought studies. We review available studies on both extreme precipitation (redistribution within seasons) and seasonal changes in precipitation (redistribution across seasons) on grasslands and forests. <br><br> Extreme precipitation differentially affected above-ground net primary productivity (ANPP), depending on whether extreme precipitation led to increased or decreased SWC, which differed based on the current precipitation and aridity index of the site. Specifically, studies to date reported that extreme precipitation decreased ANPP in mesic sites, but, conversely, increased ANPP in xeric sites, suggesting that plant-available water is a key factor driving responses to extreme precipitation. Similarly, the effects of seasonal changes in precipitation on ANPP, phenology, and leaf and fruit development varied with the effect on SWC. Reductions in spring or summer generally had negative effects on plants, associated with reduced SWC, while subsequent reductions in autumn or winter had little effect on SWC or plants. Similarly, increased summer precipitation had a more dramatic impact on plants than winter increases in precipitation. <br><br> The patterns of response suggest xeric biomes may respond positively to extreme precipitation, while comparatively mesic biomes may be more likely to be negatively affected. Moreover, seasonal changes in precipitation during warm or dry seasons may have larger effects than changes during cool or wet seasons. Accordingly, responses to redistributed precipitation will involve a complex interplay between plant-available water, plant functional type and resultant influences on plant phenology, growth and water relations. These results highlight the need for experiments across a range of soil types and plant functional types, critical for predicting future vegetation responses to future climates. |
| [15] | . The identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global relevance. Here we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognizing that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the vegetation sensitivity index, that identifies areas sensitive to climate variability over the past 14 years. The metric uses time series data derived from the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index, and three climatic variables that drive vegetation productivity (air temperature, water availability and cloud cover). Underlying the analysis is an autoregressive modelling approach used to identify climate drivers of vegetation productivity on monthly timescales, in addition to regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the Arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems--be they natural or with a strong anthropogenic signature--to environmental variability, which is the first step towards addressing why some regions appear to be more sensitive than others, and what impact this has on the resilience of ecosystem service provision and human well-being. |
| [16] | . Aridland ecosystems are predicted to be responsive to both increases and decreases in precipitation. In addition, chronic droughts may contribute to encroachment of native C-3 shrubs into C-4-dominated grasslands. We conducted a long-term rainfall manipulation experiment in native grassland, shrubland and the grass-shrub ecotone in the northern Chihuahuan Desert, USA. We evaluated the effects of 5 years of experimental drought and 4 years of water addition on plant community structure and dynamics. We assessed the effects of altered rainfall regimes on the abundance of dominant species as well as on species richness and subdominant grasses, forbs and shrubs. Nonmetric multidimensional scaling and MANOVA were used to quantify changes in species composition in response to chronic addition or reduction of rainfall. We found that drought consistently and strongly decreased cover of Bouteloua eriopoda, the dominant C-4 grass in this system, whereas water addition slightly increased cover, with little variation between years. In contrast, neither chronic drought nor increased rainfall had consistent effects on the cover of Larrea tridentata, the dominant C-3 shrub. Species richness declined in shrub-dominated vegetation in response to drought whereas richness increased or was unaffected by water addition or drought in mixed- and grass-dominated vegetation. Cover of subdominant shrubs, grasses and forbs changed significantly over time, primarily in response to interannual rainfall variability more so than to our experimental rainfall treatments. Nevertheless, drought and water addition shifted the species composition of plant communities in all three vegetation types. Overall, we found that B. eriopoda responded strongly to drought and less so to irrigation, whereas L. tridentata showed limited response to either treatment. The strong decline in grass cover and the resistance of shrub cover to rainfall reduction suggest that chronic drought may be a key factor promoting shrub dominance during encroachment into desert grassland. |
| [17] | . <p>研究气候变化背景下植被变化趋势及其与水热因子的关系, 对于黄河源区的生态恢复和生态建设具有重要意义。采用基于FAO Penman-Monteith的降水蒸散比来描述区域的干湿状况, 划分了黄河上游地区的干湿气候区。在此基础上, 利用AVHRR归一化植被指数(<em>NDVI</em>)和GLOPEM净初级生产力(<em>NPP</em>)数据集和同期的气候资料, 分析了黄河上游植被覆盖、植被生产力和气候变化的趋势, 探讨了不同干湿气候区影响植被变化的主要气候因子。结果表明, 研究区域东南部为半湿润气候区, 其余为半干旱气候区, 干湿气候分界线与450 mm降水等值线较接近; 1981–2006年区域气候趋于干暖化, 尤其是气温的升高趋势明显; 半湿润地区<em>NDVI</em>和<em>NPP</em>显著增加, 半干旱地区略有增加; 半湿润地区的<em>NDVI</em>多与气温显著正相关, 与降水量的相关性较弱, 气温是植被生长的主要气候制约因素; 半干旱地区的<em>NDVI</em>则与降水量的正相关性更强, 对降水量的变化较为敏感。<em>NPP</em>对气候变化的响应模式与<em>NDVI</em>相似。植被对气候变化的响应部分依赖于研究区域所具备的水热条件, 干湿气候划分有助于更好地解释植被对气候变化响应的空间差异。</p> . <p>研究气候变化背景下植被变化趋势及其与水热因子的关系, 对于黄河源区的生态恢复和生态建设具有重要意义。采用基于FAO Penman-Monteith的降水蒸散比来描述区域的干湿状况, 划分了黄河上游地区的干湿气候区。在此基础上, 利用AVHRR归一化植被指数(<em>NDVI</em>)和GLOPEM净初级生产力(<em>NPP</em>)数据集和同期的气候资料, 分析了黄河上游植被覆盖、植被生产力和气候变化的趋势, 探讨了不同干湿气候区影响植被变化的主要气候因子。结果表明, 研究区域东南部为半湿润气候区, 其余为半干旱气候区, 干湿气候分界线与450 mm降水等值线较接近; 1981–2006年区域气候趋于干暖化, 尤其是气温的升高趋势明显; 半湿润地区<em>NDVI</em>和<em>NPP</em>显著增加, 半干旱地区略有增加; 半湿润地区的<em>NDVI</em>多与气温显著正相关, 与降水量的相关性较弱, 气温是植被生长的主要气候制约因素; 半干旱地区的<em>NDVI</em>则与降水量的正相关性更强, 对降水量的变化较为敏感。<em>NPP</em>对气候变化的响应模式与<em>NDVI</em>相似。植被对气候变化的响应部分依赖于研究区域所具备的水热条件, 干湿气候划分有助于更好地解释植被对气候变化响应的空间差异。</p> |
| [18] | . Abstract Climate change forecasts of more frequent climate extremes suggest that such events will become increasingly important drivers of future ecosystem dynamics and function. Because the rarity and unpredictability of naturally occurring climate extremes limits assessment of their ecological impacts, we experimentally imposed extreme drought and a mid-summer heat wave over two years in a central U.S. grassland. While the ecosystem was resistant to heat waves, it was not resistant to extreme drought, which reduced aboveground net primary productivity (ANPP) below the lowest level measured in this grassland in almost 30 years. This extreme reduction in ecosystem function was a consequence of reduced productivity of both C4 grasses and C3 forbs. However, the dominant forb was negatively impacted by the drought more than the dominant grass, and this led to a reordering of species abundances within the plant community. Although this change in community composition persisted post-drought, ANPP recovered completely the year after drought due to rapid demographic responses by the dominant grass, compensating for loss of the dominant forb. Overall, these results show that an extreme reduction in ecosystem function attributable to climate extremes (e.g., low resistance) does not preclude rapid ecosystem recovery. Given that dominance by a few species is characteristic of most ecosystems, knowledge of the traits of these species and their responses to climate extremes will be key for predicting future ecosystem dynamics and function. |
| [19] | . Temperature data over the past five decades show faster warming of the global land surface during the night than during the day(1). This asymmetric warming is expected to affect carbon assimilation and consumption in plants, because photosynthesis in most plants occurs during daytime and is more sensitive to the maximum daily temperature, T-max, whereas plant respiration occurs throughout the day(2) and is therefore influenced by both T-max and the minimum daily temperature, T-min. Most studies of the response of terrestrial ecosystems to climate warming, however, ignore this asymmetric forcing effect on vegetation growth and carbon dioxide (CO2) fluxes(3-6). Here we analyse the interannual covariations of the satellite-derived normalized difference vegetation index (NDVI, an indicator of vegetation greenness) with Tmax and Tmin over the Northern Hemisphere. After removing the correlation between Tmax and Tmin, we find that the partial correlation between Tmax and NDVI is positive in most wet and cool ecosystems over boreal regions, but negative in dry temperate regions. In contrast, the partial correlation between Tmin and NDVI is negative in boreal regions, and exhibits a more complex behaviour in dry temperate regions. We detect similar patterns in terrestrial net CO2 exchange maps obtained from a global atmospheric inversion model. Additional analysis of the long-term atmospheric CO2 concentration record of the station Point Barrow in Alaska suggests that the peak-to-peak amplitude of CO2 increased by 23 +/- 11% for a +1 degrees C anomaly in T-max from May to September over lands north of 51 degrees N, but decreased by 28 +/- 14% for a +1 degrees C anomaly in T-min. These lines of evidence suggest that asymmetric diurnal warming, a process that is currently not taken into account in many global carbon cycle models, leads to a divergent response of Northern Hemisphere vegetation growth and carbon sequestration to rising temperatures. |
| [20] | . Abstract Satellite-derived Normalized Difference Vegetation Index (NDVI), a proxy of vegetation productivity, is known to be correlated with temperature in northern ecosystems. This relationship, however, may change over time following alternations in other environmental factors. Here we show that above 30 N, the strength of the relationship between the interannual variability of growing season NDVI and temperature (partial correlation coefficient RNDVI-GT) declined substantially between 1982 and 2011. This decrease in RNDVI-GT is mainly observed in temperate and arctic ecosystems, and is also partly reproduced by process-based ecosystem model results. In the temperate ecosystem, the decrease in RNDVI-GT coincides with an increase in drought. In the arctic ecosystem, it may be related to a nonlinear response of photosynthesis to temperature, increase of hot extreme days and shrub expansion over grass-dominated tundra. Our results caution the use of results from interannual time scales to constrain the decadal response of plants to ongoing warming. |
| [21] | . We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches. |
| [22] | . 未来地球计划是目前国际上关于全球环境变化前沿研究的综合科学计划,集国际科学理事会(ICSU)所领导的四大科学计划为一体,旨在将自然科学与社会科学结合在一起,并加强决策支持和研究交流,寻求地球系统可持续途径,全球环境变化研究与人类学、社会学合作构建综合集成平台,推进科学研究为社会经济可持续发展服务。本文剖析陆地表层格局特点,分析陆地表层格局的国内外关注焦点及其研究理念的转变与应用领域的拓展。分析表明:陆地表层是未来地球计划关注的重点之一,陆地表层要素与过程相互作用并在人类活动驱动下形成的格局,可作为未来地球计划进一步研究的区域基础框架。未来,陆地表层格局研究应力求方法论的突破,为自然地理学综合研究的发展提供支撑。 . 未来地球计划是目前国际上关于全球环境变化前沿研究的综合科学计划,集国际科学理事会(ICSU)所领导的四大科学计划为一体,旨在将自然科学与社会科学结合在一起,并加强决策支持和研究交流,寻求地球系统可持续途径,全球环境变化研究与人类学、社会学合作构建综合集成平台,推进科学研究为社会经济可持续发展服务。本文剖析陆地表层格局特点,分析陆地表层格局的国内外关注焦点及其研究理念的转变与应用领域的拓展。分析表明:陆地表层是未来地球计划关注的重点之一,陆地表层要素与过程相互作用并在人类活动驱动下形成的格局,可作为未来地球计划进一步研究的区域基础框架。未来,陆地表层格局研究应力求方法论的突破,为自然地理学综合研究的发展提供支撑。 |
| [23] | . <p align="justify">在ArcGIS支撑下, 基于1982—2010年8 km分辨率的AVHRR NDVI及气温和降水数据, 应用最小二乘法和地理加权回归方法, 构建中国NDVI与气候因子的地理加权回归模型, 定量分析中国NDVI与气温和降水的相互关系, 获取各个回归参数的空间格局, 并将模拟结果与全局性回归结果进行对比。结果表明, 与线性回归模型相比, 地理加权回归模型的拟合效果显著提高, 拟合优度从0.3提高到0.6。气候因子对NDVI的影响具有空间异质性: 从北到南, 气候因子对NDVI的影响逐渐减小; 西北内陆等干旱荒漠地带, 气候因子对NDVI的影响较大。对中国大部分地区而言, 气温对NDVI的影响超过降水。各区NDVI与主导气候因子发生作用的特征尺度不同。</p> . <p align="justify">在ArcGIS支撑下, 基于1982—2010年8 km分辨率的AVHRR NDVI及气温和降水数据, 应用最小二乘法和地理加权回归方法, 构建中国NDVI与气候因子的地理加权回归模型, 定量分析中国NDVI与气温和降水的相互关系, 获取各个回归参数的空间格局, 并将模拟结果与全局性回归结果进行对比。结果表明, 与线性回归模型相比, 地理加权回归模型的拟合效果显著提高, 拟合优度从0.3提高到0.6。气候因子对NDVI的影响具有空间异质性: 从北到南, 气候因子对NDVI的影响逐渐减小; 西北内陆等干旱荒漠地带, 气候因子对NDVI的影响较大。对中国大部分地区而言, 气温对NDVI的影响超过降水。各区NDVI与主导气候因子发生作用的特征尺度不同。</p> |
| [24] | . 基于2012年6月更新的高质量2 400个台站降水资料,采用薄盘样条法,制定了采用3个自变量(经度、纬度、海拔高度)、降水量开平方预处理、3次样条的插值方案,并引入数字高程资料,以减弱中国独特地形条件下高程对降水空间插值精度的影响,并对1961—2010年中国区域地面降水站点资料进行了空间内插,得到了中国地面降水0.5°×0.5°格点数据集。经数据集的质量评估结果表明:分析值与站点观测值均方根误差平均为0.49 mm,相关系数平均达0.93(通过0.01的显著性检验),夏季插值误差高于冬季,东南地区误差普遍高于其他地区。冬、春、夏、秋季绝大多数台站绝对误差在±10 mm/月以内。冬、春、夏、秋季分别有60%、82%、54%、77%的台站相对误差在±10%之间。插值后的格点化降水资料能够比较细致、准确地描述中国大陆年平均降水场的东南多、西北少的主要空间特征,但也平滑掉了范围很小的降水极值中心。台站分布越密集的地方,插值效果越好,并且最近距离小于40 km的台站插值精度较高,大于40 km插值精度衰减较快。 . 基于2012年6月更新的高质量2 400个台站降水资料,采用薄盘样条法,制定了采用3个自变量(经度、纬度、海拔高度)、降水量开平方预处理、3次样条的插值方案,并引入数字高程资料,以减弱中国独特地形条件下高程对降水空间插值精度的影响,并对1961—2010年中国区域地面降水站点资料进行了空间内插,得到了中国地面降水0.5°×0.5°格点数据集。经数据集的质量评估结果表明:分析值与站点观测值均方根误差平均为0.49 mm,相关系数平均达0.93(通过0.01的显著性检验),夏季插值误差高于冬季,东南地区误差普遍高于其他地区。冬、春、夏、秋季绝大多数台站绝对误差在±10 mm/月以内。冬、春、夏、秋季分别有60%、82%、54%、77%的台站相对误差在±10%之间。插值后的格点化降水资料能够比较细致、准确地描述中国大陆年平均降水场的东南多、西北少的主要空间特征,但也平滑掉了范围很小的降水极值中心。台站分布越密集的地方,插值效果越好,并且最近距离小于40 km的台站插值精度较高,大于40 km插值精度衰减较快。 |
| [25] | . We present an approach to regional environmental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index (NDVI) data over the period 2001-2008 and land cover change (LCC) analysis of the 2001and 2008 MODIS Global Land Cover product (MCD12Q1).NDVI trends were overwhelmingly negative across the grain belt with statistically significant (p ≤ 0.05)positive trends covering only 1％ of the land surface.LCC was dominated by transitions between three classes;cropland,grassland,and a mixed cropland/natural vegetation mosaic.Combining our analyses of NDVI trends and LCC,we found a pattern of agricultural abandonment (cropland to grassland) in the southern range of the grain belt coinciding with statistically significant (p≤0.05)negative NDVI trends and likely driven by regional drought.In the northern range of the grain belt we found an opposite tendency toward agricultural intensification; in this case,represented by LCC from cropland mosaic to pure cropland,and also associated with statistically significant (p≤0.05) negative NDVI trends.Relatively small clusters of statistically significant (p ≤ 0.05) positive NDVI trends corresponding with both localized land abandonment and localized agricultural intensification show that land use decision making is not uniform across the region.Land surface change in the Northern Eurasian grain belt is part of a larger pattern of land cover land use change (LCLUC) in Eastern Europe,Russia,and former territories of the Soviet Union following realignment of socialist land tenure and agricultural markets.Here,we show that a combined analysis of LCC and NDVI trends provides a more complete picture of the complexities of LCLUC in the Northern Eurasian grain belt,involving both broader climatic forcing,and narrower anthropogenic impacts,than might be obtained from either analysis alone. |
| [26] | . On the basis of AVHRR GIMMS NDVI and MODIS NDVI, we constructed monthly NDVI sequences covering Northeast China from 1982 to 2009 using a per-pixel unary linear regression model. The expanded NDVI passed the consistency check and were well used for analysis. The monthly NDVI trends were highly correlated with climatic changes. Spatially averaged NDVI in summer exhibited a downward trend with increased temperature and significantly decreased precipitation in the 28 years. NDVI trends were spatially heterogeneous, corresponding with the regional climatic features of different seasons. NDVI for the 95 meteorological stations exhibited significant correlations with monthly mean temperature and monthly precipitation during the study period. The NDVI–temperature correlation was stronger than NDVI–precipitation correlation in most stations and for all vegetation types. Different vegetation types showed various spatial responses to climatic changes. |
| [27] | . 根据NDVI3g数据,本文定义了18种植被物候指标研究植被物候变化情况。根据1：100万植被区划,把青藏高原划分为8个植被区分。对物候变化比较显著的区域,采用最高温度、最低温度、平均温度、降水、太阳辐射数据,运用偏最小二乘法回归（PLS）研究物候变化的气候成因。结果表明：1青藏高原生长季初期物候指标,转折发生在1997-2000年,转折前初期物候指标平均提前2~3 d/10a;青藏高原末期物候指标转折发生在2004-2007年左右,生长季长度物候指标突变发生在2005年左右,转折前末期物候指标平均延迟1~2 d/10a、生长季长度平均延长1~2 d/10a;转折之后生长季初期物候指标推迟趋势的显著性水平仅为0.1,生长季末期物候指标、生长季长度指标趋势不显著。2高寒草甸与高寒灌木草甸是青藏高原物候变化最剧烈的植被分区。高寒草甸区生长季长度的延长主要是由生长季初期物候指标提前导致的。高寒灌木草甸区生长季长度的延长主要是由于初期物候指标的提前,以及末期物候指标的推迟共同作用导致的。3采用PLS进一步分析气象因素对高寒草甸与高寒灌木草甸物候剧烈变化的影响。表明,温度对物候的影响占主导地位,两植被分区均显示上年秋季、冬初温度对生长季初期物候具有正的影响,该时段温度一方面会导致上年末期物候指标推迟,间接推迟生长季开始时间;另一方面高温不利用冬季休眠。除夏季外,其余月份最小温度对植被物候的影响与平均温度、最高温度的影响类似。降水对植被物候的影响不同月份波动较大,上年秋冬季节降水对初期物候指标具有负的影响,春初降水对初期物候指标具有正的影响。8月份限制植被生长季的主要因素是降水,此时降水与末期物候指标模型系数为正。太阳辐射对植被物候的影响主要在夏季与秋初。PLS方法在物候变化研究中具有17 . 根据NDVI3g数据,本文定义了18种植被物候指标研究植被物候变化情况。根据1：100万植被区划,把青藏高原划分为8个植被区分。对物候变化比较显著的区域,采用最高温度、最低温度、平均温度、降水、太阳辐射数据,运用偏最小二乘法回归（PLS）研究物候变化的气候成因。结果表明：1青藏高原生长季初期物候指标,转折发生在1997-2000年,转折前初期物候指标平均提前2~3 d/10a;青藏高原末期物候指标转折发生在2004-2007年左右,生长季长度物候指标突变发生在2005年左右,转折前末期物候指标平均延迟1~2 d/10a、生长季长度平均延长1~2 d/10a;转折之后生长季初期物候指标推迟趋势的显著性水平仅为0.1,生长季末期物候指标、生长季长度指标趋势不显著。2高寒草甸与高寒灌木草甸是青藏高原物候变化最剧烈的植被分区。高寒草甸区生长季长度的延长主要是由生长季初期物候指标提前导致的。高寒灌木草甸区生长季长度的延长主要是由于初期物候指标的提前,以及末期物候指标的推迟共同作用导致的。3采用PLS进一步分析气象因素对高寒草甸与高寒灌木草甸物候剧烈变化的影响。表明,温度对物候的影响占主导地位,两植被分区均显示上年秋季、冬初温度对生长季初期物候具有正的影响,该时段温度一方面会导致上年末期物候指标推迟,间接推迟生长季开始时间;另一方面高温不利用冬季休眠。除夏季外,其余月份最小温度对植被物候的影响与平均温度、最高温度的影响类似。降水对植被物候的影响不同月份波动较大,上年秋冬季节降水对初期物候指标具有负的影响,春初降水对初期物候指标具有正的影响。8月份限制植被生长季的主要因素是降水,此时降水与末期物候指标模型系数为正。太阳辐射对植被物候的影响主要在夏季与秋初。PLS方法在物候变化研究中具有17 |
| [28] | . Global climate change has led to significant vegetation changes in the past half century. North China Plain, the most important grain production base of china, is undergoing a process of prominent warming and drying. The vegetation coverage, which is used to monitor vegetation change, can respond to climate change (temperature and precipitation). In this study, GIMMS (Global Inventory Modelling and Mapping Studies)-NDVI (Normalized Difference Vegetation Index) data, MODIS (Moderate-resolution Imaging Spectroradiometer) – NDVI data and climate data, during 1981–2013, were used to investigate the spatial distribution and changes of vegetation. The relationship between climate and vegetation on different spatial (agriculture, forest and grassland) and temporal (yearly, decadal and monthly) scales were also analyzed in North China Plain. (1) It was found that temperature exhibiting a slight increase trend (0.20°C/10a, P<0.01). This may be due to the disappearance of 0°C isotherm, the rise of spring temperature. At the same time, precipitation showed a significant reduction trend (611.75mm/10a, P>0.05). The climate mutation period was during 1991–1994. (2) Vegetation coverage slight increase was observed in the 55% of total study area, with a change rate of 0.00039/10a. Human activities may not only accelerate the changes of the vegetation coverage, but also c effect to the rate of these changes. (3) Overall, the correlation between the vegetation coverage and climatic factor is higher in monthly scale than yearly scale. The correlation analysis between vegetation coverage and climate changes showed that annual vegetation coverage was better correlatend with precipitation in grassland biome; but it showed a better correlated with temperature i the agriculture biome and forest biome. In addition, the vegetation coverage had sensitive time-effect respond to precipitation. (4) The vegetation coverage showed the same increasing trend before and after the climatic variations, but the rate of increase slowed down. From the vegetation coverage point of view, the grassland ecological zone had an obvious response to the climatic variations, but the agricultural ecological zones showed a significant response from the vegetation coverage change rate point of view. The effect of human activity in degradation region was higher than that in improvement area. But after the climate abruptly changing, the effect of human activity in improvement area was higher than that in degradation region, and the influence of human activity will continue in the future. |
| [29] | . Spatial nonstationarity is a condition in which a simple 090008global090009 model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at different points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigating the null hypothesis that the data may be described by a global model rather than a non-stationary one and also for testing whether individual regression coefficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U.K. census relating car ownership rates to social class and male unemployment. The paper concludes by discussing ways in which the technique can be extended. |
| [30] | . Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R (2) and lower corrected Akaike information criterion (AIC(C)). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R (2) and lower AIC(C). The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships. |
| [31] | . 我国西南喀斯特地区石漠化面积已实现净减少,植被状况具有明显改善.为了更清晰的了解该区植被变化情况及其影响因素的区域差异,采用长时间序列遥感数据,综合运用空间自相关分析、主成分分析(PCA)和地理加权回归(GWR)等研究方法,分析生态工程实施以来滇桂黔喀斯特植被变化及其主要影响因素的空间非平稳性.结果表明:与1982-2000年相比,2001-2011年生长季归一化植被指数(GSN)在整个研究区域都有增加且具有显著的空间集聚性(Ig为0.90),但增加程度在空间上具有差异性(变异系数为43％);影响滇桂黔植被变化的主要因素包括气候因子、土壤质地、人类活动、水分有效性、土壤养分和社会经济条件,且对植被变化的影响程度随地理位置的变化而变化.不同工程地貌类型区内,影响植被变化的主导因素不同,且存在显著的空间差异性,需综合考虑植被变化主导因素的区域差异来调整或改进后续生态工程措施. . 我国西南喀斯特地区石漠化面积已实现净减少,植被状况具有明显改善.为了更清晰的了解该区植被变化情况及其影响因素的区域差异,采用长时间序列遥感数据,综合运用空间自相关分析、主成分分析(PCA)和地理加权回归(GWR)等研究方法,分析生态工程实施以来滇桂黔喀斯特植被变化及其主要影响因素的空间非平稳性.结果表明:与1982-2000年相比,2001-2011年生长季归一化植被指数(GSN)在整个研究区域都有增加且具有显著的空间集聚性(Ig为0.90),但增加程度在空间上具有差异性(变异系数为43％);影响滇桂黔植被变化的主要因素包括气候因子、土壤质地、人类活动、水分有效性、土壤养分和社会经济条件,且对植被变化的影响程度随地理位置的变化而变化.不同工程地貌类型区内,影响植被变化的主导因素不同,且存在显著的空间差异性,需综合考虑植被变化主导因素的区域差异来调整或改进后续生态工程措施. |
| [32] | . Monitoring changes in vegetation growth has been the subject of considerable research during the past several decades, because of the important role of vegetation in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of satellite-derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio-temporal patterns of changes in vegetation growth and their linkage with changes in temperature and precipitation in temperate and boreal regions of Eurasia (> 23.5°N) from 1982 to 2006. At the continental scale, although a statistically significant positive trend of average growing season NDVI is observed (0.5 × 10613 year611, P = 0.03) during the entire study period, there are two distinct periods with opposite trends in growing season NDVI. Growing season NDVI has first significantly increased from 1982 to 1997 (1.8 × 10613 year611, P < 0.001), and then decreased from 1997 to 2006 (611.3 × 10613 year611, P = 0.055). This reversal in the growing season NDVI trends over Eurasia are largely contributed by spring and summer NDVI changes. Both spring and summer NDVI significantly increased from 1982 to 1997 (2.1 × 10613 year611, P = 0.01; 1.6 × 10613 year611P < 0.001, respectively), but then decreased from 1997 to 2006, particularly summer NDVI which may be related to the remarkable decrease in summer precipitation (612.7 mm yr611, P = 0.009). Further spatial analyses supports the idea that the vegetation greening trend in spring and summer that occurred during the earlier study period 1982–1997 was either stalled or reversed during the following study period 1997–2006. But the turning point of vegetation NDVI is found to vary across different regions. |
| [33] | . Current predictions of extinction risks from climate change vary widely depending on the specific assumptions and geographic and taxonomic focus of each study. I synthesized published studies in order to estimate a global mean extinction rate and determine which factors contribute the greatest uncertainty to climate change nduced extinction risks. Results suggest that extinction risks will accelerate with future global temperatures, threatening up to one in six species under current policies. Extinction risks were highest in South America, Australia, and New Zealand, and risks did not vary by taxonomic group. Realistic assumptions about extinction debt and dispersal capacity substantially increased extinction risks. We urgently need to adopt strategies that limit further climate change if we are to avoid an acceleration of global extinctions. Author: Mark C. Urban |
| [34] | . Abstract Variation in terrestrial net primary production (NPP) with climate is thought to originate from a direct influence of temperature and precipitation on plant metabolism. However, variation in NPP may also result from an indirect influence of climate by means of plant age, stand biomass, growing season length and local adaptation. To identify the relative importance of direct and indirect climate effects, we extend metabolic scaling theory to link hypothesized climate influences with NPP, and assess hypothesized relationships using a global compilation of ecosystem woody plant biomass and production data. Notably, age and biomass explained most of the variation in production whereas temperature and precipitation explained almost none, suggesting that climate indirectly (not directly) influences production. Furthermore, our theory shows that variation in NPP is characterized by a common scaling relationship, suggesting that global change models can incorporate the mechanisms governing this relationship to improve predictions of future ecosystem function. |
| [35] | . [1] The historical surface temperature data set HadCRUT provides a record of surface temperature trends and variability since 1850. A new version of this data set, HadCRUT3, has been produced, benefiting from recent improvements to the sea surface temperature data set which forms its marine component, and from improvements to the station records which provide the land data. A comprehensive set of uncertainty estimates has been derived to accompany the data: Estimates of measurement and sampling error, temperature bias effects, and the effect of limited observational coverage on large-scale averages have all been made. Since the mid twentieth century the uncertainties in global and hemispheric mean temperatures are small, and the temperature increase greatly exceeds its uncertainty. In earlier periods the uncertainties are larger, but the temperature increase over the twentieth century is still significantly larger than its uncertainty. |
| [36] | . Revegetation of degraded ecosystems provides opportunities for carbon sequestration and bioenergy production. However, vegetation expansion in water-limited areas creates potentially conflicting demands for water between the ecosystem and humans. Current understanding of these competing demands is still limited. Here, we study the semi-arid Loess Plateau in China, where the `Grain to Green large-scale revegetation programme has been in operation since 1999. As expected, we found that the new planting has caused both net primary productivity (NPP) and evapotranspiration (ET) to increase. Also the increase of ET has induced a significant (p < 0.001) decrease in the ratio of river runoff to annual precipitation across hydrological catchments. From currently revegetated areas and human water demand, we estimate a threshold of NPP of 400 +/- 5 g C myrabove which the population will suffer water shortages. NPP in this region is found to be already close to this limit. The threshold of NPP could change by -36% in the worst case of climate drying and high human withdrawals, to +43% in the best case. Our results develop a new conceptual framework to determine the critical carbon sequestration that is sustainable in terms of both ecological and socio-economic resource demands in a coupled anthropogenic-biological system. |
| [37] | . |
| [38] | . <p>以HJ卫星CCD影像为数据源，计算和分析赣南2008 和2011 年植被覆盖演变和空间分布特征及与地貌因子关系。结果表明：红壤区域植被覆盖度与高程在2008、2011 年相关系数分别为0.946 1、0.954 5，具有强正相关性；植被退化主要集中在高植被覆盖区域，100~300 m高程、1~5°坡度、306~360°坡向范围分别占总退化面积的88.84%，88.41%，30.73%；50~150 m高程、13°以下坡度和27°坡度以上区域为退化最剧烈区域。量化了植被覆盖与地貌因子的关系，为红壤区域环境治理和监测提供科学依据，具有一定的实用价值。</p> . <p>以HJ卫星CCD影像为数据源，计算和分析赣南2008 和2011 年植被覆盖演变和空间分布特征及与地貌因子关系。结果表明：红壤区域植被覆盖度与高程在2008、2011 年相关系数分别为0.946 1、0.954 5，具有强正相关性；植被退化主要集中在高植被覆盖区域，100~300 m高程、1~5°坡度、306~360°坡向范围分别占总退化面积的88.84%，88.41%，30.73%；50~150 m高程、13°以下坡度和27°坡度以上区域为退化最剧烈区域。量化了植被覆盖与地貌因子的关系，为红壤区域环境治理和监测提供科学依据，具有一定的实用价值。</p> |
| [39] | . Vegetation plays an important role in improving and restoring fragile ecological environments. In the Antaibao opencast coal mine, located in a loess area, the eco-environment has been substantially disturbed by mining activities, and the relationship between the vegetation and environmental factors is not very clear. Therefore, it is crucial to understand the effects of soil and topographic factors on vegetation restoration to improve the fragile ecosystems of damaged land. An investigation of the soil, topography and vegetation in 50 reclamation sample plots in Shanxi Pingshuo Antaibao opencast coal mine dumps was performed. Statistical analyses in this study included one-way ANOVA and significance testing using SPSS 20.0, and multivariate techniques of detrended correspondence analysis (DCA) and redundancy analysis (RDA) using CANOCO 4.5. The RDA revealed the environmental factors that affected vegetation restoration. Various vegetation and soil variables were significantly correlated. The available K and rock content were good explanatory variables, and they were positively correlated with tree volume. The effects of the soil factors on vegetation restoration were higher than those of the topographic factors. |
| [40] | . Ecological conservation and restoration are necessary to mitigate environmental degradation problems. China has taken great efforts in such actions. To understand the ecological transition during 2000-2010 in China, this study analysed trends in vegetation change using remote sensing and linear regression. Climate and socioeconomic factors were included to screen the driving forces for vegetation change using correlation or comparative analyses. Our results indicated that China experienced both vegetation greening (restoration) and browning (degradation) with great spatial heterogeneity. Socioeconomic factors, such as human populations and economic production, were the most significant factors for vegetation change. Nature reserves have contributed slightly to the deceleration of vegetation browning and the promotion of greening; however, a large-scale conservation approach beyond nature reserves was more effective. The effectiveness of the Three-North Shelter Forest Program lay between the two above approaches. The findings of this study highlighted that vegetation trend detection is a practical approach for large-scale ecological transition assessments, which can inform decision-making that promotes vegetation greening via proper socioeconomic development and ecosystem management. |
