藏北高原不同海拔高度高寒草甸植被指数与环境温湿度的关系
2015-12-08沈振西孙维李少伟何永涛付刚张宪洲王江伟
沈振西,孙维,李少伟,何永涛,付刚,张宪洲,王江伟
中国科学院地理科学与资源研究所 生态系统网络观测与模拟重点实验室 拉萨高原生态系统研究站,北京100101
藏北高原不同海拔高度高寒草甸植被指数与环境温湿度的关系
沈振西,孙维,李少伟,何永涛,付刚*,张宪洲,王江伟
中国科学院地理科学与资源研究所 生态系统网络观测与模拟重点实验室 拉萨高原生态系统研究站,北京100101
藏北高寒草甸是全球高寒草地的重要组成部分,是对气候变化最敏感的植被类型之一。关于高寒草地植被指数与环境温湿度因子的关系还存在着诸多不确定性,这限制了准确预测高寒草地植被生长对将来气候变化的响应。定量化高寒草地植被指数与气候因子的关系利于预测将来气候变化对高寒草地植被生长的影响。该研究基于相关分析和多重逐步回归分析探讨了藏北高原不同海拔高度(4300、4500和4700 m)的高寒草甸2011─2014年每年6─9月的归一化植被指数(normalized difference vegetation index,NDVI)、增强型植被指数(Enhanced Vegetation Index,EVI)与土壤温度、土壤湿度、空气温度、相对湿度、饱和水汽压差的相互关系。相关分析表明,3种海拔的NDVI(4 300 m:r=0.79,P=0.000;4 500 m:r=0.80,P=0.000;4 700 m:r=0.52,P=0.005)和EVI(4 300 m:r=0.61,P=0.001;4 500 m:r=0.66,P=0.000;4 700 m:r=0.53,P = 0.004)都随着土壤湿度的增加显著增加;3种海拔的NDVI(4 300 m:r=-0.68,P=0.000;4 500 m:r=-0.56,P=0.002;4 700 m:r=-0.40,P=0.037)和EVI(4 300 m:r=-0.56,P=0.002;4 500 m:r=-0.49,P=0.008;4 700 m:r=-0.46,P=0.014)都随着饱和水汽压差的增加显著降低;植被指数与环境温湿度因子的相关系数随着海拔的变化而变化;NDVI和EVI与环境温湿度因子的相关系数存在差异。多重逐步回归分析表明,土壤湿度一个因子解释了 3种海拔的归一化植被指数、海拔 4 300和4 500 m的增强型植被指数的变异,而海拔4 700 m的土壤湿度和土壤温度共同了解释了增强型植被指数的变异,其中土壤湿度的贡献较大。因此,在藏北高寒草甸,植被指数对气候变化的敏感性可能随着海拔的变化而变化,NDVI和EVI对气候变化的敏感性可能不同,土壤湿度主导着NDVI和EVI的季节变化。
高寒草甸;归一化植被指数;增强型植被指数;藏北高原;气候变化
青藏高原以其高海拔、低温、强辐射等地理特性而享有“世界第三级”的称谓(Zhang et al.,2000)。青藏高原是全球气候变化最为敏感的区域之一,发生在其之上的气候变暖幅度远远大于全球平均水平。强烈的气候变化已经对青藏高原上的各种高寒生态系统产生了非常重大的影响,这些影响反过来又加剧了气候变化(Fu et al.,2015;Zhang et al.,2015)。尽管如此,这些相关影响仍存在着诸多不确定性(Shen et al.,2015;Wang et al.,2012)。作为气候变化的“启动区”,发生在青藏高原上面的各种变化会迅速传播至周边地区(Yao et al.,1991)。
青藏高原上的植被类型主要有高寒草甸、高寒草原、温带草原、森林、灌木和农田等,而各种草地类型是其最重要的植被类型之一(Shen et al.,2014)6766。高寒草甸在青藏高原及其附近区域乃至世界高寒地区都具有典型代表意义(Xu et al.,2007)。约占1/3青藏高原面积的高寒草甸是青藏高原重要的牧场(Cao et al.,2003),在很大程度上影
响着当地畜牧业的发展。
在众多的植被指数中,归一化植被指数(Normalized Difference Vegetation Index,NDVI)是应用最广泛的植被指数(Xiao et al.,2003)385。虽然NDVI已经被广泛应用于生物量的估算和物候的反演等方面,但是NDVI仍存在着饱和现象以及容易受土壤和大气的干扰等缺陷(Xiao et al.,2003)385。为了减少土壤等因素的干扰,相关研究又发现了包括增强型植被指数(Enhanced Vegetation Index,EVI)和土壤调节植被指数在内的多种植被指数。相对于NDVI,EVI削弱了土壤和大气的干扰作用(Xiao et al.,2004)。与土壤调节植被指数相比,EVI可通过蓝色波段修正由大气和土壤等因素所造成的偏差。自1999年12月中分辨率成像光谱 仪 ( moderate resolution imaging spectroradiometer,MODIS)的Terra传感器成功发射以来,MODIS一直以较高的时间分辨率和空间分辨率对全球范围内的NDVI和EVI进行着连续的动态监测,这便于分析NDVI和EVI的时空变异及其与全球变化的关系。
为了探讨气候变化对青藏高原植被生长的影响,很多研究已经分析了EVI尤其是NDVI与各种气候因子的相互关系,但是并没有一致的结论(Shen et al.,20146765;Sun et al.,20131894;Zhang et al.,20131)。此外,目前的研究主要分析了植被指数与气温和降水的相互关系,缺少对植被指数与土壤温度、土壤湿度的相互关系方面的研究。为此,本研究基于MODIS的NDVI和EVI以及常规观测的土壤温湿度、空气温湿度数据,分析了藏北高寒草甸不同海拔高度的植被指数的时间变异及其与环境温湿度的相互关系,这对于更好的预测将来气候变化背景下的高寒生态系统对气候变化的响应及其反馈机制有重要的意义。
1 材料与方法
1.1 研究地概况
本研究区域(30°30′~30°32′N,91°03′~91°04′E)位于拉萨市当雄县草原站(Fu et al.,20142;付刚等,201131),该站距当雄县城约3 km,地处念青唐古拉山的南缘。该地区属于高原性季风气候,降水量有明显的季节之分,80%的降水集中在生长季节的6─8月份(Fu et al.,2012a)158。植被类型属于高寒草甸植被(付刚等,2011)31。土壤类型为高寒草甸土,土层厚度约为0.5~0.7 m,植物根系主要分布在0~20 cm土层内(Fu et al.,2012b)。
1.2 样地设置
在念青唐古拉山的一个南坡,以当雄草原站(海拔4300 m)为基点,沿着海拔每升高200 m布设样地,共设置3个实验样地(图1),每块样地大小约为20 m ×20 m。在每块样地内架设两套微气候观测系统(HOBO Weather Station Data Logger)(付刚等,2011)32,分别位于距样地中心南北两侧约5 m处。
图1 研究站点Fig. 1 Study sites
1.3 环境温湿度
本研究涉及到的环境温湿度数据包括空气温度(Air Temperature,Ta,℃)、相对湿度(Relative Humidity,RH,%)、饱和水汽压差(Vapor Pressure Deficit,VPD,kPa)、土壤温度(Soil Temperature,Ts,℃)和土壤湿度(Soil Moisture,SM,m3·m-3)。其中空气饱和水汽压差是依据空气温度和相对湿度计算得到的(Fu et al.,2012a)159。
1.4 MODIS植被指数
本研究利用了 MODIS的植被指数产品MOD13Q1,本产品的时间分辨率为16 d,空间分辨率为250 m×250 m,在此空间分辨率下,3个实验样地对应的 3个象元间完全没有重叠。2011─2014年每年生长季节(6─9月)的MODIS植被指数数据(NDVI 和 EVI)从网站http://daac.ornl.gov/cgi-bin/MODIS/GLBVIZ_1_Glb/ modis_subset_order_global_col5.pl上下载。下载的NDVI和EVI数据直接用于后续的统计分析。
1.5 统计分析
采用两因子方差分析分析了海拔高度和观测年份对Ts、Ta、SM、RH、VPD、NDVI和EVI的影响。采用相关分析和多重逐步回归分析对植被指数与Ts、Ta、SM、RH、VPD的相互关系进行了统计分析。采用SPSS 16.0软件进行相关的统计分析,采用Sigmaplot 10.0软件作图。
2 结果与分析
2.1 Ts、Ta、SM、RH和VPD的变化
方差分析表明(表1),观测年份及其与海拔的交互作用对Ts、Ta、SM和VPD都无显著影响,交互作用对RH也无显著影响。观测年份对RH的影响达到了显著水平,即2014年的RH显著大于其他3年的RH,而其他3年间的RH无显著差异。海拔高度对Ts、Ta和SM有显著影响,而对RH和VPD无显著影响。具体而言,Ts和Ta都随着海拔的升高而显著降低,且不同海拔间的 Ts和 Ta都有显著差异。海拔4700 m的SM显著大于海拔4300 m的SM,而海拔4500 m的SM与其他两个海拔的SM都无显著差异。
表1 土壤温度、土壤湿度、空气温度、相对湿度、饱和水汽压差、归一化植被指数和增强型植被指数的两因子(海拔、年)方差分析Table 1 ANOVA for soil temperature (Ts), soil moisture (SM), air temperature (Ta), air relative humidity (RH), vapor pressure deficit (VPD), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)
3个海拔间的Ts、Ta、SM、RH和VPD都表现出了相似的季节变化(图2,3)。
2.2 NDVI和EVI的变化
方差分析表明(表1),观测年份、海拔高度与观测年份的交互作用对NDVI和EVI都无显著影响,而海拔高度对NDVI和EVI有显著影响。即海拔4300和4500 m间的NDVI和EVI都无显著差异,却都显著小于海拔4700 m的NDVI和EVI。
3种海拔间的NDVI和EVI都分别表现出了相似的季节变化,即随着时间的推移,先增大后减少(图4)。
表2 归一化植被指数、增强型植被指数与土壤温度、土壤湿度、空气温度、相对湿度以及饱和水汽压差的相关分析Table 2 Correlation analysis between normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) and soil temperature (Ts), soil moisture (SM), air temperature (Ta), air relative humidity (RH) and vapor pressure deficit (VPD), respectively
2.3 NDVI、EVI与Ts、Ta、SM、RH、VPD的相互关系
相关分析表明(表2),3个海拔高度上NDVI和EVI与Ts、Ta和VPD均呈现显著的负相关关系,而与SM和RH呈现显著的正相关关系。海拔4300 m的NDVI随着Ts和Ta的增加而显著减少,海拔4300 m的EVI随着Ts的增加也显著减少,但与Ta
的负相关系数没有达到统计显著水平;海拔 4500和4700 m的NDVI和EVI与Ts和Ta的相关系数没有达到统计显著水平。3种海拔的NDVI和EVI都随着SM的增加而显著增加,而随着VPD的增加而显著减少; EVI都随着RH的增加而显著增加,其中,海拔4300和4500 m的NDVI都随着RH的增加而显著增加,而海拔4700 m的NDVI与RH无显著相关性。NDVI和EVI与SM的相关系数值最大。
图2 4 300 m(a,d),4 500 m(b,e)和4 700 m(c,f)的日均土壤温度和空气温度的季节变化Fig. 2 Seasonal changes of daily mean soil temperature (Ts) and air temperature (Ta) along an elevation gradient (4 300~4 700 m)
多重逐步回归分析表明(表3),3个海拔高度上SM和Ta共同解释了NDVI和EVI的变异,其中SM的贡献大于 Ta的贡献。SM解释了海拔 4300和4500 m的NDVI和EVI以及海拔4700 m的NDVI的变异;SM和Ts共同解释了海拔4700 m的EVI的变异,其中SM的贡献大于Ts的贡献。
3 讨论与结论
3.1 讨论
由于本研究采用的MODIS植被指数产品的空间分辨率为250 m×250 m,而中间海拔的实验样地与最低或最高海拔的实验样地间的海拔高差为 200 m,这可能会对实验结果造成一定的影响。尽管如此,3个实验样地对应的影像是完全独立的3个象
元,因此,由于海拔高度差和MODIS植被指数空间分辨率对实验结果带来的可能影响应该不大。
图3 4 300 m(a,d,g),4 500 m(b,e,h)和4 700 m(c,f,i)的日均土壤湿度、空气相对湿度和饱和水汽压差的季节变化Fig. 3 Seasonal changes of daily soil moisture (SM), air relative humidity (RH) and vapor pressure deficit (VPD) along an elevation gradient (4 300~4 700 m)
表3 归一化植被指数、增强型植被指数与土壤温度、土壤湿度、空气温度、相对湿度以及饱和水汽压差的逐步回归分析Table 3 Multiple stepwise liner analysis between normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) and soil temperature (Ts), soil moisture (SM), air temperature (Ta), air relative humidity (RH) and vapor pressure deficit (VPD)
NDVI和EVI随着海拔的分布特征与地上生物量、根系生物量、土壤全氮、土壤有效氮、土壤微生物量等沿着海拔的分布特征一致(Fu et al.,20145;付刚等,201133)。土壤湿度主导着 NDVI和 EVI的变异,这与前人的研究结果一致。如付刚等(2011)34在当雄草原站的研究发现,空气相对湿度和饱和水汽压差共同控制着地上生物量的变异。
本研究中NDVI最大值小于0.7,这说明NDVI的饱和现象在本研究区域可能不存在,这与我们之前的研究结果一致(Fu et al. 2013)3。与EVI相比,NDVI更容易受大气状态、土壤和植被背景的干扰(Xiao et al.,2003)391,这可能是造成EVI和NDVI与环境温湿度因子的不同的相关程度的原因。
相关的研究(Shen et al.,20146780-6782;Wang et al.,2015437)表明,当雄县气象站点的NDVI或EVI
与空气相对湿度的相关系数均大于降水量与NDVI或EVI的相关系数。在本研究中,NDVI或EVI与土壤湿度的相关系数大于NDVI或EVI与空气相对湿度的相关系数。因此,在本研究区域,与降水量相比,土壤湿度对NDVI或EVI的影响作用可能更大。
图4 4 300 m(a,d),4 500 m(b,e)和4 700 m(c,f)的归一化植被指数和增强型植被指数的季节变化Fig. 4 Seasonal changes of normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) along an elevation gradient (4 300~4 700 m)
3个海拔高度的NDVI和EVI与环境温湿度的相关程度存在着差异,这与前人的研究结果相一致,即植被指数与空气温湿度、降水和水汽压等气象因子的相互关系随着气象站点的不同而不同(Shen et al.,20146780-6782;Sun et al.,20131904;Zhang et al.,20137-9)。空气温度都随着海拔的升高而显著降低,3个海拔间的土壤湿度也存在着差异,而土壤湿度和空气温度共同控制着NDVI和EVI的变异。因此,3个海拔间植被指数与环境温湿度的不同程度的相关关系可能与3个海拔间不同的环境温湿度条件有关。Shen et al. (2014)6777-6778也发现,青藏高原生长季节最大的增强型植被指数的变异与不同气象站点的空气温湿度环境背景条件相关。此外,该结论支持了植被指数与环境温湿度的相互关系在同一植被类型内也存在着差异性的研究结果(Wang et al.,2015)437-439。
相关研究表明(Shen et al.,2014)6770-6776,在过去的13年间(2000─2012),当雄县气象站点的生长季节内的最大增强型植被指数显著降低,这可能主要与其平均气温和饱和水汽压差的显著增加以及相对湿度的显著降低有关。在过去的 13年间(2000─2012),当雄县气象站点的生长季节内的最大增强型植被指数与平均空气温度呈现显著的负相关关系,与最低相对湿度呈现显著的正相关关系(Shen et al.,2014)6780-6782。本研究结果表明,NDVI和EVI随着环境温度的增加而降低,而随着环境湿度的增加而增加(表2)。此外,植被指数能够反映植被生长状况(Shen et al.,20146766;杨鹏万等,2014)。暖干化的气候变化可能对藏北高原高寒草甸的植被指数产生负作用。同时,Fu et al.(2013)1通过模拟增温实验发现,暖干化的微气候环境没有增加总初级生产力和地上生物量。暖干化的微环境可能会降低土壤氮矿化速率和土壤无机氮含量,而植被生长随着土壤无机氮含量的增加而增加(Fu et al.,20146;Yu et al.,2014;Zong et al.,2013)。饱和水汽压差增加会引起气孔关闭,导致气孔阻力加大,叶片光合速率随之降低,进而引起植物光合作用减弱(Almeida et al.,2003)。因此,暖干化的气候变化可能不利于藏北高原高寒草甸的植被生长,而这可能与暖干化对土壤无机氮和光合速率的负效应有关。
3.2 结论
综上,土壤湿度主导着藏北高寒草甸归一化植被指数和增强型植被指数的变异,且这两个植被指数都随着土壤湿度的增加而显著增加。
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Relationships between Vegetation Indices and Environmental Temperature and Moisture in An Alpine Meadow along An Elevation Gradient in the Northern Tibet
SHEN Zhenxi, SUN Wei, LI Shaowei, HE Yongtao, FU Gang, ZHANG Xianzhou, WANG Jiangwei
Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
The alpine meadow in the Northern Tibet is an important component of the alpine grasslands worldwide, and it is also one of the most sensitive vegetation types to climatic changes. There are large uncertainties on the relationship between vegetation indices and environmental temperature and moisture, which limits our ability to accurately predict the responses of the vegetation growth in alpine grasslands to future climatic changes. Quantifying the relationship between vegetation indices and climatic factors improves the prediction of vegetation growth in alpine grasslands under future climatic change. Using the correlation analysis and the multiple stepwise regression analysis, we explored the relationships between vegetation indices (i.e. Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI) and soil temperature, soil moisture, air temperature, relative humidity or vapor pressure deficit at three elevations (4 300, 4 500 and 4 700 m) in an alpine meadow from June─September in 2011─2014. The correlation analyses showed that all the correlation coefficients between vegetation indices and environmental temperature and moisture varied with elevation. The NDVI at elevation 4300 m decreased significantly with increasing soil temperature (r = -0.54, P = 0.003) and air temperature (r = -0.42, P = 0.028). The EVI at elevation 4300 m decreased significantly with increasing soil temperature (r = -0.41, P = 0.030), but was not correlated with air temperature (r = -0.31, P = 0.113). Both the NDVIs and EVIs at elevation 4500 m and 4700 m were not correlated with soil temperature (4 500 m NDVI: r = -0.27, P = 0.165; 4 500 m EVI: r = -0.12, P = 0.529; 4 700 m NDVI: r = 0.23, P = 0.250; 4 700 m EVI: r = 0.28, P = 0.156) and air temperature (4 500 m NDVI: r = -0.21, P = 0.276; 4 500 m EVI: r = -0.06, P = 0.748; 4 700 m NDVI: r = -0.03, P = 0.876; 4 700 m EVI: r = -0.08, P = 0.688).The NDVIs (4 300 m: r = 0.79, P = 0.000; 4 500 m: r = 0.80, P = 0.000; 4 700 m: r = 0.52, P = 0.005)and the EVIs (4 300 m: r = 0.61, P = 0.001; 4 500 m: r = 0.66, P = 0.000; 4 700 m: r = 0.53, P = 0.004) at all the three elevations increased significantly with the increasing soil moisture, but the NDVIs (4 300 m: r = -0.68, P = 0.000; 4 500 m: r = -0.56, P = 0.002; 4 700 m: r = -0.40, P = 0.037) and the EVIs (4 300 m: r = -0.56, P = 0.002; 4 500 m: r = -0.49, P = 0.008; 4 700 m: r = -0.46, P = 0.014)at all the three elevations decreased significantly with the increasing vapor pressure deficit. The EVIs at all the three elevations increased significantly with increasing air relative humidity (4 300 m: r = 0.48, P = 0.010; 4 500 m: r = 0.50, P = 0.006; 4 700 m: r = 0.39, P = 0.039). The NDVIs at elevation 4 300 m (r = 0.63, P = 0.000) and 4500 m (r = 0.57, P = 0.001)increased significantly with increasing air relative humidity, but the NDVI at elevation 4700 m was not correlated with air relative humidity (r = 0.35, P = 0.070). The correlations between the NDVIs or EVIs and environmental temperature or moisture varied with elevation. The correlations between the NDVIs and environmental temperature and moisture were different from those between the EVIs and environmental temperature and moisture. The multiple stepwise regression analyses showed that the soil moisture alone explained the variation of the NDVIs at all three elevations and also explained the variation of the EVIs at 4300 m and at 4500 m, but at 4700 m the soil moisture and the soil temperature together explained the variation of the NDVI with relative greater contribution of soil moisture than soil temperature. Therefore, in the alpine meadow of the Northern Tibet, (1) the sensitivity of vegetation indices to climatic changes may change with elevation; (2) the sensitivity of NDVI to climatic change might differ from that of EVI; and (3) Soil moisture may play a predominant role in determining the seasonal variation of the NDVIs and the EVIs in the alpine meadow in the Northern Tibet.
alpine meadow; normalized difference vegetation index; enhanced vegetation index; the Northern Tibet; climatic change
10.16258/j.cnki.1674-5906.2015.10.001
Q948;X171.1
A
1674-5906(2015)10-1591-08
沈振西,孙维,李少伟,何永涛,付刚,张宪洲,王江伟. 藏北高原不同海拔高度高寒草甸植被指数与环境温湿度的关系[J]. 生态环境学报, 2015, 24(10): 1591-1598.
SHEN Zhenxi, SUN Wei, LI Shaowei, HE Yongtao, FU Gang, ZHANG Xianzhou, WANG Jiangwei. Relationships between Vegetation Indices and Environmental Temperature and Moisture in An Alpine Meadow along An Elevation Gradient in the Northern Tibet [J]. Ecology and Environmental Sciences, 2015, 24(10): 1591-1598.
国家自然科学基金项目(41171084;31470506;31370458);中国科学院西部之光项目“藏北高寒草甸牲畜承载力对气候变化和放牧的响应”;国家星火计划项目“优质饲草种植与奶牛健康养殖技术集成与示范”;西藏饲草专项;科技支撑计划(2013BAC04B01)“西藏高原典型退化生态系统修复技术研究与示范”
沈振西(1963年生),男,副研究员,研究方向为全球变化与高寒草地生态系统。E-mail: shenzx@igsnrr.ac.cn *通信作者:付刚(1984年生),男,助理研究员,博士,研究方向为全球变化与高寒生态系统。E-mail: fugang@igsnrr.ac.cn; fugang09@126.com
2015-06-12