中国温带地区植被物候期时空变化特征及对总初级生产力的影响
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摘要
陆地表面物候(Land Surface Phenology,LSP)被定义为陆地表面植被的生命周期事件在区域或全球尺度上的各季节时间,对于中国温带地区的植被物候,已有研究大多仅针对某一地区的单一植被类型,对长时间、大尺度的陆表物候时空变化的研究较为少见,植被物候对总初级生产力(Gross Primary Productivity,GPP)的影响也有待明确。采用2001～2014年中等分辨率成像光谱仪MODIS的MCD12Q2数据以及MOD17A3 GPP数据,利用回归分析以及相关分析的方法对中国温带地区(>30°N)植被物候期时空变化特征进行分析,并研究物候变化对GPP的影响。结果表明:我国温带地区整体植被生长季开始日期(Start of the Season,SOS)、结束日期(End of the Season,EOS)和生长季长度(Length of the Season,LOS)的14 a平均值和标准差分别为第121±10 d、第270±12 d和153±12 d;SOS提前趋势(r=-0.36,p=0.21)、EOS滞后趋势(r=0.41,p=0.15)与LOS增长趋势(r=0.51,p=0.06)均不明显;但是年均LOS与GPP相关性显著(r=0.74,p=0.002 5),且呈正相关关系。2001～2014年LOS与GPP年均值的空间分布均呈现出由西北向东南增加的趋势;研究区SOS、EOS与LOS的年际变化呈现显著变化(p<0.05)的地区分别占研究区的13%、21%、13.2%。SOS、EOS、LOS与GPP显著相关(p<0.05)的地区分别占研究区的8.31%、9.33%、8.72%,三者与GPP的相关关系均以中度相关为主(p<0.05,0.5<|r|<0.8),二者高度相关(p<0.05,|r|≥0.8)的地区很少。研究区植被物候期变化显著影响(p<0.05)植被年均GPP的地区仅有4.29%。
Land surface phenology is defined as the seasonal timing of life cycle events of vegetated land surface on local or global scale.Most studies of vegetation phenology in China's temperate zone are focused on single vegetation type in certain area,the studies about long-time vegetation phenology on large scale is rare.The influence of vegetation phenology on GPP(gross primary productivity) remains to be determined.Using Moderate Resolution Imaging Spectroradiometer(MODIS) MCD12 Q2 data from 2001 to 2014,start of growing season(SOS),end of growing season(EOS) and length of growing season(LOS) in temperate China(>30°N) are obtained.GPP from MODIS MOD17 A3 data for the same period is also obtained.Using regression analysis and correlation analysis methods,spatial and temporal patterns of SOS,EOS and LOS are analyzed.The impacts of SOS,EOS and LOS on interannual variability of GPP are also analyzed.Results show that the average and standard deviation of SOS,EOS and LOS from 2001 to 2014 are 121±10,270±12 and 153±12 days,respectively.The trend of earlier SOS,delayed EOS and increased LOS are not significant(p>0.05),but LOS shows positively correlated to GPP.The spatial distribution of annual average LOS and GPP from 2001 to 2014 presents an increase trend from northwest to southeast.Regions with significant interannual variation(p<0.05) of SOS,EOS and LOS are 13%,21% and 13.2%,respectively.Regions of significant correlation(p<0.05) of SOS,EOS and LOS to GPP account for 8.31%,9.33% and 8.72% of the study area.GPP has mainly medium correlations(p<0.05,0.5<|r|<0.8) to SOS,EOS and LOS.
Land surface phenology is defined as the seasonal timing of life cycle events of vegetated land surface on local or global scale.Most studies of vegetation phenology in China's temperate zone are focused on single vegetation type in certain area,the studies about long-time vegetation phenology on large scale is rare.The influence of vegetation phenology on GPP(gross primary productivity) remains to be determined.Using Moderate Resolution Imaging Spectroradiometer(MODIS) MCD12 Q2 data from 2001 to 2014,start of growing season(SOS),end of growing season(EOS) and length of growing season(LOS) in temperate China(>30°N) are obtained.GPP from MODIS MOD17 A3 data for the same period is also obtained.Using regression analysis and correlation analysis methods,spatial and temporal patterns of SOS,EOS and LOS are analyzed.The impacts of SOS,EOS and LOS on interannual variability of GPP are also analyzed.Results show that the average and standard deviation of SOS,EOS and LOS from 2001 to 2014 are 121±10,270±12 and 153±12 days,respectively.The trend of earlier SOS,delayed EOS and increased LOS are not significant(p>0.05),but LOS shows positively correlated to GPP.The spatial distribution of annual average LOS and GPP from 2001 to 2014 presents an increase trend from northwest to southeast.Regions with significant interannual variation(p<0.05) of SOS,EOS and LOS are 13%,21% and 13.2%,respectively.Regions of significant correlation(p<0.05) of SOS,EOS and LOS to GPP account for 8.31%,9.33% and 8.72% of the study area.GPP has mainly medium correlations(p<0.05,0.5<|r|<0.8) to SOS,EOS and LOS.
引文
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[2] Myneni R B,Keeling C D,Tucker C J,et al.Increased Plant Growth in the Northern High Latitudes from 1981 to 1991[J].Nature,1997,386:698-702.
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[7] Rupiya Xilacr,Yang Liao.Monitoring Spatial-temporal Change of Cotton Phonology in Xinjiang and lts Response to Climate Change[J].Remote Sensing Technology and Application,2018,33C5):923-931.[茹皮亚·西拉尔,杨辽.新疆棉花物候时空变化遥感监测及气温影响分析[J].遥感技术与应用,2018,33(5):923-931.]
[8] Zhou Yuke,Liu Jianwen.Spatio-temporal Analysis of Vegetation Phonology with Multiple Methods over the Tibetan Plateau based on MODIS NDVI Data[J].Remote Sensing Technology and Application,2018,33(3):486-498.[周玉科,刘建文.基于M()DIS NDVI和多方法的青藏高原植被物候时空特征分析[J].遥感技术与应用,2018,33(3):486-498.]
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[11] Zhang X Y,Friedl M A,Schaaf C B,et al.Monitoring the Response of Vegetation Phenology to Precipitation in Africa by Coupling MODIS and TRMM Instruments[J].Journal of Geophysical Research Atmospheres,2005,110,D12103,doi:10.1029/2004JD005263.
[12] Liu H,Tian F,Hu H C,et al.Soil Moisture Controls on Patterns of Grass Green-up in Inner Mongolia:An Index based Approach[J].Hydrology & Earth System Sciences,2013,17(2):805-815.
[13] Cong N,Wang T,Nan H J,et al.Changes in Satellite-derived Spring Vegetation Green-up Date and Its Linkage to Climate in China from 1982 to 2010:A Multimethod Analysis[J].Global Change Biology,2013,19(3):881–891.
[14] Kimball J S,Mcdonald K C,Zhao M.Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing[J].Earth Interactions,2006,10:1-22.
[15] Ueyama M,Iwata H,Harazono Y.Autumn Warming Reduces the CO2 Sink of a Black Spruce Forest in Interior Alaska Based on a Nine-Year Eddy Covariance Measurement.[J].Global Change Biology,2014,20(4):1161-1173.
[16] Wu C Y,Hou X H,Peng D L,et al.Land Surface Phenology of China’s Temperate Ecosystems Over 1999-2013:Spatial-Temporal Patterns,Interaction Effects,Covariation With Climate and Implications For Productivity[J].Agricultural and Forest Meteorology,2016,216:177-187.
[17] Running S W,Nemani R R,Heinsch F A,et al.A Continuous Satellite-derived Measure of Global Terrestrial Primary Production[J].Bioscience,2004,54(6):547-560.
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[19] Wu C Y,Gonsamo A,Gough C M,et al.Modeling Growing Season Phenology in North American Forests Using Seasonal Mean Vegetation Indices from MODIS[J].Remote Sensing of Environment,2014,147(18):79-88.
[20] Piao S,Fang J,Zhou L,et al.Variations in Satellite-derived Phenology in China’s Temperate Vegetation[J].Global Change Biology,2006,12(4):672-685.
[21] Piao S L,Fang J Y,Ciais P,et al.The Carbon Balance of Terrestrial Ecosystems in China[J].Nature,2009,458(7241):1009-1013.
[22] Chen B Z,Xu G,Coops N C,et al.Changes in Vegetation Photosynthetic Activity Trends Across the Asia-Pacific Region Over the Last Three Decades[J].Remote Sensing of Environment,2014,144:28-41.
[23] Piao S L,Ciais P,Huang Y,et al.The Impacts of Climate Change on Water Resources and Agriculture in China[J].Nature,2010,467:43-51.
[24] Zhang X,Friedl M A,Schaaf C B.Global Vegetation Phenology from Moderate Resolution Imaging Spectroradiometer(MODIS):Evaluation of Global Patterns and Comparison With in Situ Measurements[J].Journal of Geophysical Research Biogeosciences,2006,111,G04017,Doi:10.1029/2006JG000217.
[25] Huete A,Didan K,Miura T,et al.Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices[J].Remote Sensing of Environment,2002,83(1):195-213.
[26] Zhang X Y,Friedl M A,Schaaf C B,et al.Monitoring Vegetation Phenology Using MODIS[J].Remote Sensing of Environment,2003,84:471-475.
[27] Myneni R B,Hoffman S,Knyazikhin Y,et al.Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data[J].Remote Sensing of Environment,2002,83:214-231.
[28] Zhao M,Running S W,Nemani R R.Sensitivity of Moderate Resolution Imaging Spectroradiometer(MODIS) Terrestrial Primary Production to the Accuracy of Meteorological Reanalyses[J].Journal of Geophysical Research Biogeosciences,2006,111,G01002,Doi:10.1029/2004JG000004.
[29] Friedl M A,Mciver D K,Hodges J C F,et al.Global Land Cover Mapping from MODIS:Algorithms and Early Results[J].Remote Sensing of Environment,2002,83:287-302.
[30] Feng Min,Li Zaiming,Qiu Bingwen,et al.Spatio-temporal Difference of Crops and Natural Vegetation Phenology in China At the Beginning of the 21st Century[J].Remote Sensing Technology and Application,2016,31(5):1003-1012.[封敏,李再明,邱炳文,等.21世纪初中国农作物与自然植被物候时空差异[J].遥感技术与应用,2016,31(5):1003-1012.].
[31] Bao Gang,Bao Yulong,Alateng Tuya,et al.Spatio-temporal Dynamics of Vegetation Penolohgy in the Mongolian Pateau During 1982～2011[J].Remote Sensing Technology and Application,2017,32(5):866-874.[包刚,包玉龙,阿拉腾图娅,等.1982～2011年蒙古高原植被物候时空动态变化[J].遥感技术与应用,2017,32(5):866-874.].
[32] Han Hongzhu,Bai Jianjun,Zhang Bo,et al.Spatial-temporal Characteristics of Vegetation Phenology in Shaanxi Province Based on MODIS Time Series[J].Remote Sensing For Land and Resources,2018,30(4):125 - 131.[韩红珠,白建军,张波,等.基于 MODIS 时序的陕西省植被物候时空变化特征分析[J].国土资源遥感,2018,30(4):125-131.]
[33] Yang Y,Guan H,Shen M,et al.Changes in Autumn Vegetation Dormancy Onset Date and the Climate Controls Across Temperate Ecosystems in China from 1982 to 2010[J].Global Change Biology,2015,21:652-665.
[34] Ganguly S,Friedl M A,Tan B,et al.Land Surface Phenology from MODIS:Characterization of the Collection 5 Global Land Cover Dynamics Product[J].Remote Sensing of Environment,2010,114:1805-1816.
[2] Myneni R B,Keeling C D,Tucker C J,et al.Increased Plant Growth in the Northern High Latitudes from 1981 to 1991[J].Nature,1997,386:698-702.
[3] Zhu Kezhen,Wan Minwei.Phenology[M].Beijing:Science Press,1973.[竺可桢,宛敏渭.物候学[M].北京:科学出版社,1973.]
[4] Richardson A R,Anderson R S,Arain M A,et al.Terrestrial Biosphere Models Need Better Representation of Vegetation Phenology:Results from the North American Carbon Program Site Synthesis[J].Global Change Biology,2012,18:566-584.
[5] Kim Y,Kimball J S,Zhang K,et al.Satellite Detection of Increasing Northern Hemisphere non-frozen Seasons from 1979 to 2008:Implications for Regional Vegetation Growth[J].Remote Sensing of Environment,2012,121:472-487.
[6] Chmielewski F M,Rotzer T.Response of Tree Phenology to Climate Change Across Europe.[J].Agricultural and Forest Meteorology,2001,108:101-112.
[7] Rupiya Xilacr,Yang Liao.Monitoring Spatial-temporal Change of Cotton Phonology in Xinjiang and lts Response to Climate Change[J].Remote Sensing Technology and Application,2018,33C5):923-931.[茹皮亚·西拉尔,杨辽.新疆棉花物候时空变化遥感监测及气温影响分析[J].遥感技术与应用,2018,33(5):923-931.]
[8] Zhou Yuke,Liu Jianwen.Spatio-temporal Analysis of Vegetation Phonology with Multiple Methods over the Tibetan Plateau based on MODIS NDVI Data[J].Remote Sensing Technology and Application,2018,33(3):486-498.[周玉科,刘建文.基于M()DIS NDVI和多方法的青藏高原植被物候时空特征分析[J].遥感技术与应用,2018,33(3):486-498.]
[9] Yu H Y,Luedeling E,Xu J C.Winter and Spring Warming Result in Delayed Spring Phenology on the Tibetan Plateau[J].Proceedings of the National Academy of Sciences of the United States of America,2010,107(51):22151-22156.
[10] Guo L,Dai J H,Wang M C,et al.Responses of Spring Phenology in Temperate Zone Trees to Climate Warming:A Case Study of Apricot Flowering in China[J].Agricultural and Forest Meteorology,2015,201(201):1-7.
[11] Zhang X Y,Friedl M A,Schaaf C B,et al.Monitoring the Response of Vegetation Phenology to Precipitation in Africa by Coupling MODIS and TRMM Instruments[J].Journal of Geophysical Research Atmospheres,2005,110,D12103,doi:10.1029/2004JD005263.
[12] Liu H,Tian F,Hu H C,et al.Soil Moisture Controls on Patterns of Grass Green-up in Inner Mongolia:An Index based Approach[J].Hydrology & Earth System Sciences,2013,17(2):805-815.
[13] Cong N,Wang T,Nan H J,et al.Changes in Satellite-derived Spring Vegetation Green-up Date and Its Linkage to Climate in China from 1982 to 2010:A Multimethod Analysis[J].Global Change Biology,2013,19(3):881–891.
[14] Kimball J S,Mcdonald K C,Zhao M.Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing[J].Earth Interactions,2006,10:1-22.
[15] Ueyama M,Iwata H,Harazono Y.Autumn Warming Reduces the CO2 Sink of a Black Spruce Forest in Interior Alaska Based on a Nine-Year Eddy Covariance Measurement.[J].Global Change Biology,2014,20(4):1161-1173.
[16] Wu C Y,Hou X H,Peng D L,et al.Land Surface Phenology of China’s Temperate Ecosystems Over 1999-2013:Spatial-Temporal Patterns,Interaction Effects,Covariation With Climate and Implications For Productivity[J].Agricultural and Forest Meteorology,2016,216:177-187.
[17] Running S W,Nemani R R,Heinsch F A,et al.A Continuous Satellite-derived Measure of Global Terrestrial Primary Production[J].Bioscience,2004,54(6):547-560.
[18] Richardson A D,Black T A,Ciais P,et al.Influence of Spring and Autumn Phenological Transitions on Forest Ecosystem Productivity[J].Philosophical Transactions of the Royal Society of London,2010,365(1555):3227-3246.
[19] Wu C Y,Gonsamo A,Gough C M,et al.Modeling Growing Season Phenology in North American Forests Using Seasonal Mean Vegetation Indices from MODIS[J].Remote Sensing of Environment,2014,147(18):79-88.
[20] Piao S,Fang J,Zhou L,et al.Variations in Satellite-derived Phenology in China’s Temperate Vegetation[J].Global Change Biology,2006,12(4):672-685.
[21] Piao S L,Fang J Y,Ciais P,et al.The Carbon Balance of Terrestrial Ecosystems in China[J].Nature,2009,458(7241):1009-1013.
[22] Chen B Z,Xu G,Coops N C,et al.Changes in Vegetation Photosynthetic Activity Trends Across the Asia-Pacific Region Over the Last Three Decades[J].Remote Sensing of Environment,2014,144:28-41.
[23] Piao S L,Ciais P,Huang Y,et al.The Impacts of Climate Change on Water Resources and Agriculture in China[J].Nature,2010,467:43-51.
[24] Zhang X,Friedl M A,Schaaf C B.Global Vegetation Phenology from Moderate Resolution Imaging Spectroradiometer(MODIS):Evaluation of Global Patterns and Comparison With in Situ Measurements[J].Journal of Geophysical Research Biogeosciences,2006,111,G04017,Doi:10.1029/2006JG000217.
[25] Huete A,Didan K,Miura T,et al.Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices[J].Remote Sensing of Environment,2002,83(1):195-213.
[26] Zhang X Y,Friedl M A,Schaaf C B,et al.Monitoring Vegetation Phenology Using MODIS[J].Remote Sensing of Environment,2003,84:471-475.
[27] Myneni R B,Hoffman S,Knyazikhin Y,et al.Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data[J].Remote Sensing of Environment,2002,83:214-231.
[28] Zhao M,Running S W,Nemani R R.Sensitivity of Moderate Resolution Imaging Spectroradiometer(MODIS) Terrestrial Primary Production to the Accuracy of Meteorological Reanalyses[J].Journal of Geophysical Research Biogeosciences,2006,111,G01002,Doi:10.1029/2004JG000004.
[29] Friedl M A,Mciver D K,Hodges J C F,et al.Global Land Cover Mapping from MODIS:Algorithms and Early Results[J].Remote Sensing of Environment,2002,83:287-302.
[30] Feng Min,Li Zaiming,Qiu Bingwen,et al.Spatio-temporal Difference of Crops and Natural Vegetation Phenology in China At the Beginning of the 21st Century[J].Remote Sensing Technology and Application,2016,31(5):1003-1012.[封敏,李再明,邱炳文,等.21世纪初中国农作物与自然植被物候时空差异[J].遥感技术与应用,2016,31(5):1003-1012.].
[31] Bao Gang,Bao Yulong,Alateng Tuya,et al.Spatio-temporal Dynamics of Vegetation Penolohgy in the Mongolian Pateau During 1982～2011[J].Remote Sensing Technology and Application,2017,32(5):866-874.[包刚,包玉龙,阿拉腾图娅,等.1982～2011年蒙古高原植被物候时空动态变化[J].遥感技术与应用,2017,32(5):866-874.].
[32] Han Hongzhu,Bai Jianjun,Zhang Bo,et al.Spatial-temporal Characteristics of Vegetation Phenology in Shaanxi Province Based on MODIS Time Series[J].Remote Sensing For Land and Resources,2018,30(4):125 - 131.[韩红珠,白建军,张波,等.基于 MODIS 时序的陕西省植被物候时空变化特征分析[J].国土资源遥感,2018,30(4):125-131.]
[33] Yang Y,Guan H,Shen M,et al.Changes in Autumn Vegetation Dormancy Onset Date and the Climate Controls Across Temperate Ecosystems in China from 1982 to 2010[J].Global Change Biology,2015,21:652-665.
[34] Ganguly S,Friedl M A,Tan B,et al.Land Surface Phenology from MODIS:Characterization of the Collection 5 Global Land Cover Dynamics Product[J].Remote Sensing of Environment,2010,114:1805-1816.
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