Detecting Long-range Correlations in NDVI over Greater Khingan Mountains

Abstract

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.