武汉市植被物候变化规律及影响因素分析
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摘要
植被物候作为植被生长响应气候变化的指示器,对于研究气候变化以及城市化进程具有重要意义.城市内部植被通常分布破碎,因此公里级的低分辨率遥感影像难以实现植被的精细识别与分析,而十米级空间分辨率的遥感数据在时间分辨率方面又难以满足物候分析的要求.为此,该文基于遥感数据时空融合技术,缓解高时间分辨率与高空间分辨率之间的矛盾,进行城市植被物候变化规律的分析.基于非局部滤波融合方法,生成武汉市空间分辨率30 m、时间分辨率8 d的地表反射率及EVI(增强型植被指数)序列,进一步采用移动加权谐波分析方法对EVI序列进行重建,并通过动态阈值方法提取2006年～2014年武汉市植被物候信息.实验结果表明:1)武汉市植被由中心向郊区呈现生长期开始时间(SOS)逐渐推迟、结束时间(EOS)逐渐提前、生长期长度(LOS)逐渐延长的空间分布规律,且整体呈现出SOS提前、EOS推迟、LOS延长的时间变化趋势;2)植被物候和平均气温相关性并不显著,但EOS和LOS受气温年平均日较差影响显著,气温年平均日较差每增加1℃,EOS推迟约12 d,LOS延长约16 d,降水主要影响SOS和LOS,平均降水量每升高100 mm,SOS提前约5 d,LOS延长约9 d.
As the indicator of responses of vegetation growth to climate changes, phenology is of much significance in studying climate changes and urbanization. As the distributions of vegetation inside urban areas are usually scattered, low-resolution Remote Sensing images of the kilometer level hardly recognize vegetation and conduct analyses well, while the Remote Sensing data of 10 m spatial resolution are difficult to meet the requirements of phenological analysis in terms of time resolution. To solve the above problem, a spatial and temporal fusion method of Remote Sensing data is applied in this paper to alleviate the contradiction between time resolution and spatial resolution, and phenological mapping together with analysis of affecting factors are carried out. Based on the non-local means filter fusion method, the surface reflectance and EVI(Enhanced Vegetation Index) time series with spatial resolution of 30 m and time resolution of 8 d in Wuhan are generated. The EVI time series are reconstructed by Moving Weighted Harmonic Analysis, and vegetation phenology of Wuhan from 2006 to 2014 are extracted by dynamic threshold method. Through analysis, it is found that: 1) from 2006 to 2014, the vegetation phenology of Wuhan from urban to suburb shows the spatial distribution pattern of the gradual delay of the start of season(SOS), the gradual advance of the end of season(EOS), and the extension of the growing period(LOS), with the overall trend of SOS advancement, EOS delay, and LOS extension observed on temporal scales; 2) the correlation between vegetation phenology and average temperature is not significant, but EOS and LOS are significantly affected by the annual average daily temperature amplitude. For an average daily amplitude of 1°C, EOS is delayed by about 12 d, and LOS is extended by about 16 d. Precipitation mainly affects SOS and LOS. For every 100 mm increase in precipitation, SOS is about 5 d ahead of schedule, and LOS is extended for about 9 d.
As the indicator of responses of vegetation growth to climate changes, phenology is of much significance in studying climate changes and urbanization. As the distributions of vegetation inside urban areas are usually scattered, low-resolution Remote Sensing images of the kilometer level hardly recognize vegetation and conduct analyses well, while the Remote Sensing data of 10 m spatial resolution are difficult to meet the requirements of phenological analysis in terms of time resolution. To solve the above problem, a spatial and temporal fusion method of Remote Sensing data is applied in this paper to alleviate the contradiction between time resolution and spatial resolution, and phenological mapping together with analysis of affecting factors are carried out. Based on the non-local means filter fusion method, the surface reflectance and EVI(Enhanced Vegetation Index) time series with spatial resolution of 30 m and time resolution of 8 d in Wuhan are generated. The EVI time series are reconstructed by Moving Weighted Harmonic Analysis, and vegetation phenology of Wuhan from 2006 to 2014 are extracted by dynamic threshold method. Through analysis, it is found that: 1) from 2006 to 2014, the vegetation phenology of Wuhan from urban to suburb shows the spatial distribution pattern of the gradual delay of the start of season(SOS), the gradual advance of the end of season(EOS), and the extension of the growing period(LOS), with the overall trend of SOS advancement, EOS delay, and LOS extension observed on temporal scales; 2) the correlation between vegetation phenology and average temperature is not significant, but EOS and LOS are significantly affected by the annual average daily temperature amplitude. For an average daily amplitude of 1°C, EOS is delayed by about 12 d, and LOS is extended by about 16 d. Precipitation mainly affects SOS and LOS. For every 100 mm increase in precipitation, SOS is about 5 d ahead of schedule, and LOS is extended for about 9 d.
引文
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[23] ZENG C,SHEN H,ZHANG L.Recovering missing pixels for Landsat ETM+SLC-off imagery using multi-temporal regression analysis and a regularization method[J].Remote Sensing of Environment,2013,131:182-194.
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[25] 罗亚,徐建华,岳文泽,等.植被指数在城市绿地信息提取中的比较研究[J].遥感技术与应用,2006,21(3):212-219.LUO Y,XU J H,YUE W Z,et al.A comparative study of extracting urban vegetation information by vegetation indices from thematicmapper images[J].Remote Sensing Technology and Application,2006,21(3):212-219.(Ch).
[26] 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.XIANG M T,WEI W,WU W B.Review of vegetation phenology estimation by using Remote Sensing[J].China Agricultural Informatics,2018,30(1):55-66.(Ch).
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[28] WHITE M A,THORNTON P E,RUNNING S W.A continental phenology model for monitoring vegetation responses to interannual climatic variability[J].Global Biogeochemical Cycles,1997,11(2):217-234.
[29] WU C,WANG X,WANG H,et al.Contrasting responses of autumn-leaf senescence to daytime and night-time warming[J].Nature Climate Change,2018,8(12):1092-1096.
[30] VITASSE Y,DELZON S,DUFRêNE E,et al.Leaf phenology sensitivity to temperature in European trees:Do within-species populations exhibit similar responses?[J].Agricultural and Forest Meteorology,2009,149(5):735-744.
[2] MENZEL A,FABIAN P.Growing season extended in Europe[J].Nature,1999,397(6721):659.
[3] CHEN F,KUSAKA H,BORNSTEIN R,et al.The integrated WRF/urban modelling system:development,evaluation,and applications to urban environmental problems[J].International Journal of Climatology,2011,31(2):273-288.
[4] 范德芹,赵学胜,朱文泉,等.植物物候遥感监测精度影响因素研究综述[J].地理科学进展,2016,35(3):304-319.FAN D Q,ZHAO X S,ZHU W Q,et al.Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J].Progress In Geography,2016,35(3):304-319.(Ch).
[5] 武永峰,李茂松,宋吉青.植物物候遥感监测研究进展[J].气象与环境学报,2008,24(3):51-58.WU Y F,LI M S,SONG J Q.Advance in vegetation phenology monitoring based on remote sensing[J].Journal of Meteorology and Environment,2008,24(3):51-58.(Ch).
[6] 左璐,王焕炯,刘荣高,等.基于不同光谱指数的植被物候期遥感监测差异[J].应用生态学报,2018,29(2):599-606.ZUO L,WANG H J,LIU R G,et al.Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices[J].Chinese Journal of Applied Ecology,2018,29(2):599-606.(Ch).
[7] TOWNSHEND J R G,JUSTICE C O.Analysis of the dynamics of African vegetation using the Normalized Difference Vegetation Index[J].International Journal of Remote Sensing,1986,7(11):1435-1445.
[8] MATSUSHITA B,YANG W,CHEN J,et al.Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects:a case study in high-density cypress forest[J].Sensors,2007,7(11):2636-2651.
[9] 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(1-2):195-213.
[10] BARR A G,BLACK T A,HOGG E H,et al.Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production[J].Agricultural and Forest Meteorology,2004,126(3-4):237-255.
[11] DELBART N,LE TOAN T,KERGOAT L,et al.Remote sensing of spring phenology in boreal regions:A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004)[J].Remote Sensing of Environment,2006,101(1):52-62.
[12] LIU R,SHANG R,LIU Y,et al.Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory,protection of key point,noise resistance and curve stability[J].Remote Sensing of Environment,2017,189:164-179.
[13] 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.
[14] 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.
[15] IMHOFF M L,BOUNOUA L,DEFRIES R,et al.The consequences of urban land transformation on net primary productivity in the United States[J].Remote Sensing of Environment,2004,89(4):434-443.
[16] ZHANG X Y,FRIEDL M A,SCHAAF C B,et al.The footprint of urban climates on vegetation phenology[J].Geophysical Research Letters,2004,31(12):1-4.
[17] ZHOU D,ZHAO S,ZHANG L,et al.Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities[J].Remote Sensing of Environment,2016,176:272-281.
[18] LIU S,ZHAO W,SHEN H,et al.Regional-scale winter wheat phenology monitoring using multisensor spatio-temporal fusion in a South Central China growing area[J].Journal of Applied Remote Sensing,2016,10(4):046029.
[19] 刘慧琴,吴鹏海,沈焕锋,等.一种基于非局部滤波的遥感时空信息融合方法[J].地理与地理信息科学,2015,31(4):27-32.LIU H Q,WU P H,SHEN H F,et al.A spatio-temporal information fusion method based on non-local means filter[J].Geography and Geo-Information Science,2015,31(4):27-32.(Ch).
[20] YANG G,SHEN H,ZHANG L,et al.A moving weighted harmonic analysis method for reconstructing high-quality SPOT VEGETATION NDVI time-series data[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(11):6008-6021.
[21] JONSSON P,EKLUNDH L.Seasonality extraction by function fitting to time-series of satellite sensor data[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(8):1824-1832.
[22] 周宇,史军,孙国武,等.1873—2009年上海气温日较差变化及其影响因素[J].气象与环境学报,2012,28(1):24-30.ZHOU Y,SHI J,SUN G W,et al.Changes of diurnal temperature range and its influencing factors from 1873 to 2009 in Shanghai[J].Journal of Meteorology and Environment,2012,28(1):24-30.(Ch).
[23] ZENG C,SHEN H,ZHANG L.Recovering missing pixels for Landsat ETM+SLC-off imagery using multi-temporal regression analysis and a regularization method[J].Remote Sensing of Environment,2013,131:182-194.
[24] FENG G,MASEK J,SCHWALLER M,et al.On the blending of the Landsat and MODIS surface reflectance:predicting daily Landsat surface reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(8):2207-2218.
[25] 罗亚,徐建华,岳文泽,等.植被指数在城市绿地信息提取中的比较研究[J].遥感技术与应用,2006,21(3):212-219.LUO Y,XU J H,YUE W Z,et al.A comparative study of extracting urban vegetation information by vegetation indices from thematicmapper images[J].Remote Sensing Technology and Application,2006,21(3):212-219.(Ch).
[26] 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.XIANG M T,WEI W,WU W B.Review of vegetation phenology estimation by using Remote Sensing[J].China Agricultural Informatics,2018,30(1):55-66.(Ch).
[27] 袁沫汐.湖北省植被动态变化与物候对气候变化的响应[D].武汉:武汉大学,2017.YUAN M X.Dynamic Change of Vegetation and Phenology Response to Climate Change in Hubei Province[D].Wuhan:Wuhan University,2017.(Ch).
[28] WHITE M A,THORNTON P E,RUNNING S W.A continental phenology model for monitoring vegetation responses to interannual climatic variability[J].Global Biogeochemical Cycles,1997,11(2):217-234.
[29] WU C,WANG X,WANG H,et al.Contrasting responses of autumn-leaf senescence to daytime and night-time warming[J].Nature Climate Change,2018,8(12):1092-1096.
[30] VITASSE Y,DELZON S,DUFRêNE E,et al.Leaf phenology sensitivity to temperature in European trees:Do within-species populations exhibit similar responses?[J].Agricultural and Forest Meteorology,2009,149(5):735-744.