地球信息科学学报

地球信息科学学报 ›› 2013, Vol. 15 ›› Issue (1): 152-158.doi: 10.3724/SP.J.1047.2013.00152

• 遥感技术与应用 • 上一篇    

大兴安岭NDVI时间序列的长程相关性特征分析

郭笑怡, 刘德赢, 张洪岩   

  1. 东北师范大学 城市与环境科学学院, 长春 130024
  • 收稿日期:2012-10-09 修回日期:2012-12-13 出版日期:2013-02-25 发布日期:2013-02-25
  • 作者简介:郭笑怡(1985-),男,博士研究生,研究方向为GIS应用与环境遥感。E-mail:guoxy914@nenu.edu.cn
  • 基金资助:

    中央高校基本科研业务费专项资金资助(11SSXT134)。

Detecting Long-range Correlations in NDVI over Greater Khingan Mountains

GUO Xiaoyi, LIU Deying, ZHANG Hongyan   

  1. School of Urban and Environmental Sciences, Northeast Normal University, Changchun 130024, China
  • Received:2012-10-09 Revised:2012-12-13 Online:2013-02-25 Published:2013-02-25

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被引次数

8 | 4

摘要：

运用消除趋势波动分析方法和GIMMS数据集,研究大兴安岭1982-2006年NDVI长程相关性,揭示了大兴安岭NDVI动态的时间尺度特征,为气候-植被综合模型中时间尺度特性分析提供依据。主要结论有:(1)NDVI是一个复杂的动力系统,其动态过程受到多个因素和过程影响,NDVI可能存在多种不同的时空尺度。(2)一阶消除趋势波动分析可以避免忽略较小时间尺度特征,适合应用于15d分辨率的时间序列;大兴安岭NDVI在2-15个月内有较强的长程相关性;表明NDVI动态过程不是随机序列,是一个由内在自相似机制决定的长程相关过程;(3)随着时间的推移,NDVI动态时间特征发生变化,大兴安岭各生态地理区标度指数都存在"拐点",其出现时间在6.5-8.5个月之间,北段西侧与南段"拐点"出现时间比北段和中段早1-2个月。在较小时间尺度内,NDVI呈现出接近1/f噪声特点,在较大时间尺度上,NDVI具有长程幂律相关性,不同生态地理区NDVI持续性强度有差异。

关键词: DFA, 长程相关性, NDVI, 大兴安岭

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.

Key words: NDVI, DFA, long-range correlations, Greater Khingan Mountains