Journal of Geo-information Science >
Detecting Long-range Correlations in NDVI over Greater Khingan Mountains
Received date: 2012-10-09
Revised date: 2012-12-13
Online published: 2013-02-25
The spatial-temporal power-law distributions are found in many natural systems. In this paper, multi-temporal series of satellite GIMMS Normalized Difference of Vegetation Index (NDVI) data from 1982 to 2006 were exploited for studying long-range correlations in the Greater Khingan Mountains. We used Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. Firstly, anomaly NDVI fluctuations are the nonstationary signal and a complex driving system. Secondly, the first-order detrended fluctuation analysis is suitable for GIMMS NDVI datasets, and in NDVI fluctuations little time-scale won't be ignored. NDVI fluctuations are found to be persistent long-range power-law correlations, with exponent 0.85 (significance 99%), for time scales longer than 2 months and shorter than 15 months in the Greater Khingan Mountains. The DFA1 was applied to randomly shuffled versions of each of the original series. The exponent for the shuffled version of NDVI is about 0.5 (significance 99%). The result suggests that NDVI have self-similarity characteristics. Finally, the four fluctuation curves, calculated for the north, northwest, middle and south of the Greater Khingan Mountains present two scaling regions with crossover timescales at about 6.5 to 8.5 months. Break points of northwest and south came earlier than north and middle in the Greater Khingan Mountains. The first timescale regions are characterized by scaling exponents α1 varying from 0.93 to 1.17. It is shown that the interval sequences of NDVI closely resemble that of 1/f noise at a small timescale. The second scaling regions are featured by exponents α2 ranging between 0.61 and 0.84 and exhibit persistent long-range power-law correlations. North and middle correlations are stronger than northwest and south. Vegetation type and climate may affect the long-range correlation in some way. These findings could be proved useful in testing the results of climate-vegetation models.
Key words: NDVI; DFA; long-range correlations; Greater Khingan Mountains
GUO Xiao-Yi, LIU De-Ying, ZHANG Hong-Yan . Detecting Long-range Correlations in NDVI over Greater Khingan Mountains[J]. Journal of Geo-information Science, 2013 , 15(1) : 152 -158 . DOI: 10.3724/SP.J.1047.2013.00152
[1] Cramer W P, Leemans R. Assessing impacts of climate change vegetation using climate classification system[M]. // Solomon A M, Shuart H H (eds.). Vegetation Dynamics and Global Change. London: Chapman and Hall,1993,190-217.
[2] DeFries R S, Field C B, Fung I, et al. Mapping the land-surface for global atmosphere-biosphere models: Toward continuous distributions of vegetation's functional properties[J]. Journal of Geophysical Research,1995,100: 20867-20882.
[3] 王启光,侯威,郑志海,等.东亚区域大气长程相关性[J].物理学报,2009,58(9):6640-6650.
[4] 刘亚龙,王庆,张明明,等.山东地区NDVI与气象因子持续性分析[J].资源科学,2010,32(9):1777-1782.
[5] Luciano T, Rosa L. Quantifying intra-annual persistent behaviour in SPOT-VEGETATION NDVI data for Mediterranean ecosystems of southern Italy[J]. Remote Sensing of Environment, 2006,101:95-103.
[6] Peng C K, Buldyrev S V, Havlin S, et al. Mosaic organization of DNA nucleotides[J]. Physical Review E,1994,49(2):1685-1689.
[7] 张秀丽,孙燕,祁文.北京逐日气温和降水量的长程变化特征[J].气象科学, 2008, 28(4):421-425.
[8] Li Z., Zhang Y H. Quantifying fractal dynamics of groundwater systems with detrended fluctuation analysis[J]. Journal of Hydrology,2007,336, 139-146.
[9] Varotsos C A, Ondov J M, Cracknell A P. Long-range Persistence in global Aerosol Index dynamics[J]. International Journal of Remote Sensing,2006,27(16):3593-3603.
[10] 郑度.中国生态地理区域系统研究[M].北京:商务出版社, 2008.
[11] Tucker C J, Pinzon J E, Brown M E, et al. An Extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data[J]. International Journal of Remote Sensing, 2005, 26(20): 4485-4498.
[12] 宋怡,马明国.基于GIMMS AVHRR NDVI数据的中国寒旱区植被动态及其与气候因子的关系[J].遥感学报,2008,12(3):499-505.
[13] Julien Y, Sobrino J A. Global land surface phenology trends from GIMMS database[J]. International Journal of Remote Sensing, 2009,30(13):3495-3513.
[14] 戴声佩,张勃,王海军.中国西北地区植被NDVI的时空变化及其影响因子分析[J].地球信息科学学报,2010,12(3):315-321.
[15] 郜建华,薛慧文.对云量的长程相关性研究[J].北京大学学报(自然科学版),2011,47(4): 613-618.
[16] Talkner P, Weber R O. Power spectrum and detrended fluctuation analysis: Application to daily temperatures[J]. Physical Review E,2000,62(1):150-160.
[17] 史凯,张斌,艾南山,等.元谋干热河谷近50a降水量时间序列的DFA分析[J].山地学报, 2008,26(5):553-559.
[18] 宋闰柳,于静洁,刘昌明.基于去趋势波动分析方法的土壤水分长程相关性研究[J].水利学报,2011,42(3):315-322.
[19] 谢先红,崔远来,周玉桃.参考作物蒸发量时间序列的长程相关性和多重分形[J].水利学报,2008,39(12):1327-1333.
[20] Lin G, Chen X, Fu Z. Temporal-spatial diversities of long-range correlation for relative humidity over China[J]. Physica A, 2007,383(2):585-594.
[21] Yuan N, Fu Z, Mao J. Different scaling behaviors in daily temperature records over China[J]. Physica A, 2010,398(19):4087-4095.
[22] 孙红雨,王常耀,牛铮,等.中国植被覆盖变化及其与气候因子关系——基于NOAA时间序列数据[J].遥感学报,1998,2(3):204-210.
[23] Ji L, Peters A J. A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains[J]. International Journal of Remote Sensing, 2004,25(2):297-311.
[24] 江田汉,邓莲堂.近140年中国、北半球和全球气温的标度律[J].高原气象, 2005, 24(3):410-414.
/
〈 | 〉 |