基于NDVI数据的江苏省植被物候变化及其影响因子分析
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摘要
以气候变暖为主要特征的全球气候变化与生态系统的相互作用成为影响可持续发展的重要因素。植被作为陆地生态系统的主要组成部分,在生态环境评价及碳水循环等方面具有重要作用。以江苏省为研究区,利用长时间序列的GIMMS NDVI3g数据集和气象数据,采用Logistic函数法提取该区域过去34 a(1982～2015年)植被生长期物候(Start Of Season SOS,End Of Season EOS)变化的时空分布特征,并用相关性分析法定量确定主要气象因子(温度、降水)对物候变化的贡献。结果表明:①空间上,从江苏省南部到北部,SOS呈递增趋势,EOS呈递减趋势;②时间上,大部分(83.1%)像元的SOS提前,主要分布在江苏省中部及北部地区,大多提前1～2 d/a,69.2%像元的EOS延后,大多延后0～1 d/a;③植被生长期开始SOS/EOS对温度、降水有明显响应,70.5%像元的SOS与温度呈负相关,主要位于江苏省北部及少部分南部地区,55.5%像元的SOS与降水呈负相关,55.2%像元的EOS与温度呈正相关,71.2%像元的EOS与降水呈负相关。整体上,温度的升高导致生长期提前,降水对SOS具有双向作用,秋季物候的影响因子更为复杂,温度和降水的变化并不能导致EOS的提前或者推迟。本研究加深对气候变化与植被生态系统相互作用过程的认识,为未来植被及气候变化分析提供参考。
Global climate change characterized by temperature increase has been a hot topic of widespread concern,and environmental protection and governance has become a significant issue affecting sustainable development.As the major component of terrestrial ecosystems,vegetation plays an important role in the aspects of ecologicalenvironment assessment and carbon cycling.based on the logistic function method,the long-term Normalized Difference Vegetation Index(NDVI)from GIMMS3 g and meteorological data over 1982～2015 were used to calculate the start(SOS)and the end(EOS)of the season of Jiangsu province.The spatial and temporal characteristics of vegetation phenological changes were also investigated.The effects of main meteorological factors(temperature and precipitation)on phenological changes were explored by correlation analysis.The results showed that:①spatially,SOS showed an increased trend while EOS decreased from south to north,②temporally,SOS for most regions(83.1%)featured advanced trends with a rate of around 1～2 days per year,while 69.2% of pixels showed a delayed EOS by 0～1 day per year,and ③vegetation phenology responded well to air temperature and precipitation,as 70.5% and 55.5% pixels of SOS had significantly negative correlations with air temperature and precipitation.However,for EOS,more than half areas,i.e.55.2% of pixels and 71.2% of pixels respectively demonstrated positive correlation with air temperature and negative correlation with precipitation.Overall,a higher temperature trigged an earlier SOS but increased precipitation did not necessarily advance SOS.Furthermore,climate changes in autumn showed complicate effects on EOS that neither changes in temperature nor precipitation can lead to one directional changes of EOS.These results will deepen the understanding of the interaction between climate change and vegetation ecosystem,and provide a reference for future vegetation and climate change analysis.
Global climate change characterized by temperature increase has been a hot topic of widespread concern,and environmental protection and governance has become a significant issue affecting sustainable development.As the major component of terrestrial ecosystems,vegetation plays an important role in the aspects of ecologicalenvironment assessment and carbon cycling.based on the logistic function method,the long-term Normalized Difference Vegetation Index(NDVI)from GIMMS3 g and meteorological data over 1982～2015 were used to calculate the start(SOS)and the end(EOS)of the season of Jiangsu province.The spatial and temporal characteristics of vegetation phenological changes were also investigated.The effects of main meteorological factors(temperature and precipitation)on phenological changes were explored by correlation analysis.The results showed that:①spatially,SOS showed an increased trend while EOS decreased from south to north,②temporally,SOS for most regions(83.1%)featured advanced trends with a rate of around 1～2 days per year,while 69.2% of pixels showed a delayed EOS by 0～1 day per year,and ③vegetation phenology responded well to air temperature and precipitation,as 70.5% and 55.5% pixels of SOS had significantly negative correlations with air temperature and precipitation.However,for EOS,more than half areas,i.e.55.2% of pixels and 71.2% of pixels respectively demonstrated positive correlation with air temperature and negative correlation with precipitation.Overall,a higher temperature trigged an earlier SOS but increased precipitation did not necessarily advance SOS.Furthermore,climate changes in autumn showed complicate effects on EOS that neither changes in temperature nor precipitation can lead to one directional changes of EOS.These results will deepen the understanding of the interaction between climate change and vegetation ecosystem,and provide a reference for future vegetation and climate change analysis.
引文
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[24] Guo Zhixing,Zhang Xiaoning,Wang Zongming,et al.Simulation and Variation Pattern of Vegetation Phenology in Northeast China based on Remote Sensing[J].China Journal of Ecology,2010,29(1):165-172[国志兴,张晓宁,王宗明,等.东北地区植被物候期遥感模拟与变化规律[J].生态学杂志,2010,29(1):165-172].
[25] Hou Xuehui,Niu Zheng,Gao Shuai.Phenology of Forest Vegetation in Northeast of China in Ten Years Using Remote Sensing[J].Spectroscopy and Spectral Analysis,2014,34(2):515-519.[侯学会,牛铮,高帅.近十年中国东北森林植被物候遥感监测[J].光谱学与光谱分析,2014,34(2):515-519.]
[26] Ma Yonggang,Chen Xi,Niu Xinmin,et al.The Trend and Comparison of Vegetation Phenological Change in Central Asia based GIMMS and SPOT Vegetation[J].Ecology and Environment Sciences,2014,(12):1889-1896.[马勇刚,陈曦,牛新民,等.基于GIMMS与SPOT Vegetation的中亚物候变化趋势及对比[J].生态环境学报,2014,(12):1889-1896.]
[27] Yu F,Price K P,Ellis J,et al.Response of Seasonal Vegetation Development to Climatic Variations in Eastern Central Asia[J].Remote Sensing of Environment,2003,87(1):42-54.
[28] Chen X,Tan Z,Schwartz M D,et al.Determining the Growing Season of Land Vegetation on the Basis of Plant Phenology and Satellite Data in Northern China[J].International Journal of Biometeorology,2000,44(2):97-101.
[29] Liu Jiyuan,Liu Mingliang,Zhuang Dafang,et al.Spatial Pattern Analysis of Recent Land Use Change in China[J].Science in China:Series D,2002,32(12):1031-1040.[刘纪远,刘明亮,庄大方,等.中国近期土地利用变化的空间格局分析[J].中国科学:D辑,2002,32(12):1031-1040.]
[2] Fu Y,Campioli M,Vitasse Y,et al.Variation in Leaf Flushing Date Influences Autumnal Senescence and Next Year\”S Flushing Date in Two Temperate Tree Species[J].Proceedings of the National Academy of Sciences,2014,111(20):7355-7360.
[3] Li L,Friedl M A,Xin Q,et al.Mapping Crop Cycles in China Using MODIS-EVI Time Series[J].Remote Sensing,2014,6:2473-2493.
[4] Wu C Y,Chen J M,Black T A,et al.Interannual Variability of Net Ecosystem Productivity in Forests is Explained by Carbon Flux Phenology in Autumn[J].Global Ecology & Biogeography,2013,22(8):994-1006.
[5] Lü Bing,Wang Na,Liu Shuju,et al.Reproductive Phenological Characteristics of Hainan Coastal Vaticamangachapoi Forest[J].Actaecologicasinica,2015,35(2):416-423.[吕冰,王娜,刘淑菊,等.海南海岸青皮林繁殖物候特征[J].生态学报,2015,35(2):416-423.]
[6] Xu Y,Dai J,Wang H,et al.Variations of Main Phenophases of Natural Calendar and Analysis of Responses to Climate Change in Harbin in 1985-2012[J].Geographical Research,2015,34(9):1662-1674.
[7] Xiang Mingtao,Wei Wei,Wu Wenbin.Review of Vegetation Phenology Estimation by Using Remote Sensing[J].China Agricultural Informatics,2018,30(1):55-66.[项铭涛,卫炜,吴文斌.植被物候参数遥感提取研究进展评述[J].中国农业信息,2018,30(1):55-66.]
[8] Zhou Yuke,Liu Jianwen.Spatio-temporal Analysis of Vegetation Phenology with Multiple Methodsover 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MODIS 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] Zhu Z,Bi J,Pan Y,et al.Global Data Sets of Vegetation Leaf Area Index(LAI)3g and Fraction of Photosynthetically Active Radiation(FPAR)3g derived from Global Inventory Modeling and Mapping Studies(GIMMS)Normalized Difference Vegetation Index(NDVI3g) for the Period 1981 to 20[J].Remote Sensing,2013,5(2):927-948.
[10] Wu C Y,Peng D,Soudani K,et al.Land Surface Phenology Derived from Normalized Difference Vegetation Index(NDVI) at Global FLUXNET Sites[J].Agricultural & Forest Meteorology,2017,233:171-182.
[11] Guyon D,Guillot M,Vitasse Y,et al.Monitoring Elevation Variations in Leaf Phenology of Deciduous Broadleaf Forests from SPOT/Vegetation Time-series[J].Remote Sensing of Environment,2011,115(2):615-627.
[12] Dash J,Curran P J.Evaluation of the MERIS Terrestrial Chlorophyll Index(MTCI)[J].Advances in Space Research,2007,39(1):100-104.
[13] Hmimina G,DufrêNe E,Pontailler J Y,et al.Evaluation of the Potential of MODIS Satellite Data to Predict Vegetation Phenology in Different Biomes:An Investigation Using Ground-based NDVI Measurements[J].Remote Sensing of Environment,2013,132(6):145-158.
[14] Dragonid,Rahman A F.Trends in Fall Phenology Across the Deciduous Forests of the Eastern USA[J].Agricultural and Forest Meteorology,2012,157:96-105.
[15] Fisher J,Richardson A,Mustard J.Phenology Model from Surface Meteorology Does Not Capture Satellite-based Greenupestimations[J].Global Change Biology,2010,13(3):707-721.
[16] Jonsson P,Eklundh L.Seasonality Extraction by Function Fitting to Time-series of Satellite Sensor Data[J].IEEE Transactions on Geoscience & Remote Sensing,2002,40(8):1824-1832.
[17] Zhang X Y,Friedl M A,Schaaf C B,et al.Monitoring Vegetation Phenology Using MODIS[J].Remote Sensing of Environment,2003,84:471-75.
[18] Liu Ya’nan,Xiao Fei,Du Yun.Improved Logistic Model For Fitting Time-Series NDVI Data[J].Remote Sensing Technology and Application,2015,30(4):737-743.[刘亚南,肖飞,杜耘.Logistic函数方法拟合多时序NDVI数据的改进研究[J].遥感技术与应用,2015,30(4):737-743.]
[19] Fisher J I,Mustard J F.Cross-Scalar Satellite Phenology from Ground,Landsat,and MODIS Data[J].Remote Sensing of Environment,2007,109(3):261-273.
[20] Klosterman S T,Hufkens K,Gray J M,et al.Evaluating Remote Sensing of Deciduous Forest Phenology at Multiple Spatial Scales Using Phenocam Imagery[J].Biogeosciences,2014,11(16):4305-4320.
[21] Gonsamo A,Chen J M,Wu C,et al.Predicting Deciduous Forest Carbon Uptake Phenology by Upscaling FLUXNET Measurements Using Remote Sensing Data[J].Agricultural & Forest Meteorology,2012,165(10):127-135.
[22] Liu Y,Wu C,Peng D,et al.Improved Modeling of Land Surface Phenology Using MODIS Land Surface Reflectance and Temperature at Evergreen Needle Leaf Forests of Central North America[J].Remote Sensing of Environment,2016,176:152-162.
[23] Yuan H,Wu C,Lu L,et al.A New Algorithm Predicting the End of Growth at Five Evergreen Coniferforests based on Nighttime Temperature and the Enhanced Vegetation Index[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,144:390-399.
[24] Guo Zhixing,Zhang Xiaoning,Wang Zongming,et al.Simulation and Variation Pattern of Vegetation Phenology in Northeast China based on Remote Sensing[J].China Journal of Ecology,2010,29(1):165-172[国志兴,张晓宁,王宗明,等.东北地区植被物候期遥感模拟与变化规律[J].生态学杂志,2010,29(1):165-172].
[25] Hou Xuehui,Niu Zheng,Gao Shuai.Phenology of Forest Vegetation in Northeast of China in Ten Years Using Remote Sensing[J].Spectroscopy and Spectral Analysis,2014,34(2):515-519.[侯学会,牛铮,高帅.近十年中国东北森林植被物候遥感监测[J].光谱学与光谱分析,2014,34(2):515-519.]
[26] Ma Yonggang,Chen Xi,Niu Xinmin,et al.The Trend and Comparison of Vegetation Phenological Change in Central Asia based GIMMS and SPOT Vegetation[J].Ecology and Environment Sciences,2014,(12):1889-1896.[马勇刚,陈曦,牛新民,等.基于GIMMS与SPOT Vegetation的中亚物候变化趋势及对比[J].生态环境学报,2014,(12):1889-1896.]
[27] Yu F,Price K P,Ellis J,et al.Response of Seasonal Vegetation Development to Climatic Variations in Eastern Central Asia[J].Remote Sensing of Environment,2003,87(1):42-54.
[28] Chen X,Tan Z,Schwartz M D,et al.Determining the Growing Season of Land Vegetation on the Basis of Plant Phenology and Satellite Data in Northern China[J].International Journal of Biometeorology,2000,44(2):97-101.
[29] Liu Jiyuan,Liu Mingliang,Zhuang Dafang,et al.Spatial Pattern Analysis of Recent Land Use Change in China[J].Science in China:Series D,2002,32(12):1031-1040.[刘纪远,刘明亮,庄大方,等.中国近期土地利用变化的空间格局分析[J].中国科学:D辑,2002,32(12):1031-1040.]