从NDVI序列检测植被“绿化”或“褐化”变化趋势的新方法
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摘要
归一化植被指数(normalized difference vegetation index,NDVI)变化趋势可近似表达植被"绿化(greening)"或"褐化(browning)"的趋势,体现植被对全球变化的适应过程。提出一种将经验模态分解(empirical mode decomposition,EMD)和Mann-Kendall(MK)检验相结合的NDVI变化趋势分析方法,主要包括2步:首先,采用EMD对NDVI时间序列进行分解,分解成若干本征模函数的叠加,这些本征模函数分量包含了原信号不同时间尺度的局部特征信号,第一个分量是高频分量,随后的分量频率逐渐减小,残差分量若是单调函数,即为趋势分量;然后,利用MK检验方法对趋势分量进行单调性变化的显著性检验,得到NDVI"绿化"或"褐化"的趋势结果。对研究区2006—2015年间NDVI序列的趋势进行分析,结果表明该方法是一种有效的时间序列变化趋势分析方法。
Normalized difference vegetation index( NDVI) trends can approximate the trend of "greening "or"browning"of vegetation and reflect the adaptation process of vegetation to global change. In this paper,an NDVI trend analysis method combining empirical mode decomposition( EMD) and Mann-Kendall( MK) significance test is proposed on vegetation monotone trend detection. The method includes mainly two steps: firstly,EMD is used to decompose NDVI time series into a finite number of intrinsic mode functions( IMF),and these components contain the local characteristic information of different time scales of the original signal. The first component is a high-frequency component,the subsequent component frequency gradually decreases,and the residual is a monotonic function,indicating the average trend. From the decomposition,the NDVI variation trend along with time is extracted naturally. Secondly,the MK significance test is used to detect the monotonicity of the trend varied,that is,to detect that the trend is monotonically increasing or monotonically decreasing,the monotonically increasing is corresponding to the trend of vegetation getting "greening ",and the monotonically decreasing is corresponding to the trend of vegetation getting "browning". The test data are MODIS NDVI time series of 16 days from 2006 to 2015. The analysis of the trend detection of those NDVI time series shows that the method proposed in this paper is an effective method for time series trend analysis and has a wide application prospect.
Normalized difference vegetation index( NDVI) trends can approximate the trend of "greening "or"browning"of vegetation and reflect the adaptation process of vegetation to global change. In this paper,an NDVI trend analysis method combining empirical mode decomposition( EMD) and Mann-Kendall( MK) significance test is proposed on vegetation monotone trend detection. The method includes mainly two steps: firstly,EMD is used to decompose NDVI time series into a finite number of intrinsic mode functions( IMF),and these components contain the local characteristic information of different time scales of the original signal. The first component is a high-frequency component,the subsequent component frequency gradually decreases,and the residual is a monotonic function,indicating the average trend. From the decomposition,the NDVI variation trend along with time is extracted naturally. Secondly,the MK significance test is used to detect the monotonicity of the trend varied,that is,to detect that the trend is monotonically increasing or monotonically decreasing,the monotonically increasing is corresponding to the trend of vegetation getting "greening ",and the monotonically decreasing is corresponding to the trend of vegetation getting "browning". The test data are MODIS NDVI time series of 16 days from 2006 to 2015. The analysis of the trend detection of those NDVI time series shows that the method proposed in this paper is an effective method for time series trend analysis and has a wide application prospect.
引文
[1] Foley J A,Levis S,Costa M H,et al. Incorporating dynamic vegetation cover within global climate models[J]. Ecological Applications,2000,10(6):1620-1632.
[2] IPCC. 4th Assessment Report of the Intergovernmental Panel on Climate Change[R]. Geneva:IPCC,2007.
[3] Cai X L,Sharma B R. Integrating remote sensing,census and weather data for an assessment of rice yield,water consumption and water productivity in the Indo-Gangetic River Basin[J]. Agricultural Water Management,2010,97(2):309-316.
[4] Sims D A,Rahman A F,Cordova V D,et al. A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS[J]. Remote Sensing of Environment,2008,112(4):1633-1646.
[5] Yu D Y,Shi P J,Shao H B,et al. Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model[J]. International Journal of Remote Sensing,2009,30(18):4851-4866.
[6] Metternicht G,Zinck J A,Blanco P D,et al. Remote sensing of land degradation:Experiences from Latin America and the Caribbean[J]. Journal of Environmental Quality,2010,39(1):42-61.
[7] Wessels K J,Prince S D,Malherbe J,et al. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa[J]. Journal of Arid Environments,2007,68(2):271-297.
[8] Zika M,Erb K H. The global loss of net primary production resulting from human-induced soil degradation in drylands[J]. Ecological Economics,2009,69(2):310-318.
[9] Prince S D,Tucker C J. Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation[J]. International Journal of Remote Sensing,1986,7(11):1555-1570.
[10] Tucker C J,Vanpraet C L,Sharman M J,et al. Satellite remote sensing of total herbaceous biomass production in the senegalese sahel:1980—1984[J]. Remote Sensing of Environment,1985,17(3):233-249.
[11] Alcaraz-Segura D,Chuvieco E,Epstein H E,et al. Debating the greening vs. browning of the North American boreal forest:Differences between satellite datasets[J]. Global Change Biology,2010,16(2):760-770.
[12] Bai Z G,Dent D L,Olsson L,et al. Proxy global assessment of land degradation[J]. Soil Use and Management,2008,24(3):223-234.
[13] Beurs K D M,Henebry G M. Trend analysis of the pathfinder AVHRR land(PAL)NDVI data for the deserts of central Asia[J]. IEEE Geoscience and Remote Sensing Letters,2004,1(4):282-286.
[14] Verbesselt J,Hyndman R,Zeileis A,et al. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series[J]. Remote Sensing Environment,2010,114(12):2970-2980.
[15] Belle G V,Hughes J P. Non-parametric tests for trend in water quality[J]. Water Resources Research,1984,20(1):127-136.
[16] Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of the Royal Society of London,1998,454:903-995.
[17] Yu D J,Cheng J S,Yang Y. Application of EMD method and Hilbert spectrum to the fault diagnoisis of roller bearings[J]. Mechanical Systems and Signal Processing,2005,19(2):259-270.
[18]吴征镒.中国植被[M].北京:科学出版社,1980.Wu Z Y. Chinese Vegetation[M]. Beijing:Science Press,1980.
[19] Mann H B. Non-parametric tests against trend[J]. Econometrica,1945,13:245-259.
[20] Kendall M G. Rank Correlation Methods[M]. London:Charles Griffin Company,1975:20-22
[21] Douglas E M,Vogel R M,Kroll C N. Trends in floods and low flows in the United States:Impact of spatial correlation[J]. Journal of Hydrology,2000,240(1-2):90-105.
[22] Abdul-Aziz O I,Burn D H. Trends and variability in the hydrological regime of the Mackenzie River Basin[J]. Journal of Hydrology,2006,319(1-4):282-294.
[23]王艳君,姜彤,许崇育.长江流域20 cm蒸发皿蒸发量的时空变化[J].水科学进展,2006,17(6):830-833.Wang Y J,Jiang T,Xu C Y. Spatial-temporal change of 20 cm pan evaporation over the Yangtze River Basin[J]. Advances in Water Science,2006,17(6):830-833.
[24]丛振涛,倪广恒,杨大文,等.“蒸发悖论”在中国的规律分析[J].水科学进展,2008,19(2):147-152.Cong Z T,Ni G H,Yang D W,et al. Evaporation paradox in China[J]. Advances in Water Science,2008,19(2):147-152.
[25] Chen J,Jnsson P,Tamura M,et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment,2004,91(3-4):332-344.
[2] IPCC. 4th Assessment Report of the Intergovernmental Panel on Climate Change[R]. Geneva:IPCC,2007.
[3] Cai X L,Sharma B R. Integrating remote sensing,census and weather data for an assessment of rice yield,water consumption and water productivity in the Indo-Gangetic River Basin[J]. Agricultural Water Management,2010,97(2):309-316.
[4] Sims D A,Rahman A F,Cordova V D,et al. A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS[J]. Remote Sensing of Environment,2008,112(4):1633-1646.
[5] Yu D Y,Shi P J,Shao H B,et al. Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model[J]. International Journal of Remote Sensing,2009,30(18):4851-4866.
[6] Metternicht G,Zinck J A,Blanco P D,et al. Remote sensing of land degradation:Experiences from Latin America and the Caribbean[J]. Journal of Environmental Quality,2010,39(1):42-61.
[7] Wessels K J,Prince S D,Malherbe J,et al. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa[J]. Journal of Arid Environments,2007,68(2):271-297.
[8] Zika M,Erb K H. The global loss of net primary production resulting from human-induced soil degradation in drylands[J]. Ecological Economics,2009,69(2):310-318.
[9] Prince S D,Tucker C J. Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation[J]. International Journal of Remote Sensing,1986,7(11):1555-1570.
[10] Tucker C J,Vanpraet C L,Sharman M J,et al. Satellite remote sensing of total herbaceous biomass production in the senegalese sahel:1980—1984[J]. Remote Sensing of Environment,1985,17(3):233-249.
[11] Alcaraz-Segura D,Chuvieco E,Epstein H E,et al. Debating the greening vs. browning of the North American boreal forest:Differences between satellite datasets[J]. Global Change Biology,2010,16(2):760-770.
[12] Bai Z G,Dent D L,Olsson L,et al. Proxy global assessment of land degradation[J]. Soil Use and Management,2008,24(3):223-234.
[13] Beurs K D M,Henebry G M. Trend analysis of the pathfinder AVHRR land(PAL)NDVI data for the deserts of central Asia[J]. IEEE Geoscience and Remote Sensing Letters,2004,1(4):282-286.
[14] Verbesselt J,Hyndman R,Zeileis A,et al. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series[J]. Remote Sensing Environment,2010,114(12):2970-2980.
[15] Belle G V,Hughes J P. Non-parametric tests for trend in water quality[J]. Water Resources Research,1984,20(1):127-136.
[16] Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of the Royal Society of London,1998,454:903-995.
[17] Yu D J,Cheng J S,Yang Y. Application of EMD method and Hilbert spectrum to the fault diagnoisis of roller bearings[J]. Mechanical Systems and Signal Processing,2005,19(2):259-270.
[18]吴征镒.中国植被[M].北京:科学出版社,1980.Wu Z Y. Chinese Vegetation[M]. Beijing:Science Press,1980.
[19] Mann H B. Non-parametric tests against trend[J]. Econometrica,1945,13:245-259.
[20] Kendall M G. Rank Correlation Methods[M]. London:Charles Griffin Company,1975:20-22
[21] Douglas E M,Vogel R M,Kroll C N. Trends in floods and low flows in the United States:Impact of spatial correlation[J]. Journal of Hydrology,2000,240(1-2):90-105.
[22] Abdul-Aziz O I,Burn D H. Trends and variability in the hydrological regime of the Mackenzie River Basin[J]. Journal of Hydrology,2006,319(1-4):282-294.
[23]王艳君,姜彤,许崇育.长江流域20 cm蒸发皿蒸发量的时空变化[J].水科学进展,2006,17(6):830-833.Wang Y J,Jiang T,Xu C Y. Spatial-temporal change of 20 cm pan evaporation over the Yangtze River Basin[J]. Advances in Water Science,2006,17(6):830-833.
[24]丛振涛,倪广恒,杨大文,等.“蒸发悖论”在中国的规律分析[J].水科学进展,2008,19(2):147-152.Cong Z T,Ni G H,Yang D W,et al. Evaporation paradox in China[J]. Advances in Water Science,2008,19(2):147-152.
[25] Chen J,Jnsson P,Tamura M,et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment,2004,91(3-4):332-344.