Detecting the Temporal Scaling Behavior of the Normalized Difference Vegetation Index Time Series in China Using a Detrended Fluctuation Analysis

Abstract

Abstract:Vegetation is an important part of terrestrial ecosystems. Although vegetation dynamics have explicit spatial and temporal dimensions, the study of the temporal process is in its infancy. Evaluation of temporal scaling behavior could provide a unique perspective for exploring the temporal nature of vegetation dynamics. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) was used to reflect vegetation dynamics, and the temporal scaling behavior of the NDVI in China was determined via detrended fluctuation analysis (DFA). Our main objectives were to reveal the temporal scaling behavior of NDVI time series and to understand variation among vegetation types. First, DFA revealed similar exponents, which ranged from 0.6 to 0.9, for all selected pixels, implying that a long-range correlation was generally present in the NDVI time series at the individual pixel scale. We then extended the analysis to all of China and found that 99.30% of the pixel exponents ranged from 0.5 to 1. These results suggest that the NDVI time series displays strong long-range correlation throughout most of China; however, the exponents exhibited regional variability. To explain these differences, OPEN ACCESSRemote Sens. 2015, 7 12943we further analyzed the exponents for 12 vegetation types based on a vegetation map of China. All of the vegetation types exhibited well-defined long-range correlation, with exponents ranging from 0.7189 to 0.8436. For all vegetation types, the maximum and average value and standard deviation of the exponents decreased with increasing annual maximum NDVI values, suggesting that low vegetation density is much more sensitive to external factors. These findings may be useful for understanding vegetation dynamics as a complex, temporally varying phenomenon.

Long Range Correlation in Vegetation Over West Africa From 1982 to 2011

“…The spatial pattern of Hurst exponents in NDVI reveals the spatial heterogeneity in the temporal scaling behavior of the NDVI that rhymes with the individual ecological regions. The SD of the exponent is 0.18 and the range is from 0.0 to 1.3 over the period 1982 -2011, while the range of exponent over China is from 0.4843 to 1.2215 with SD of 0.08 over the period 1982 -2006 [68]. The spatially averaged exponent over West Africa for 30 years is 0.58 while that of China is 0.78 over a period of 25 years, which implies China's vegetation is more persistent than West Africa.…”

“…[67] studied long range correlation in vegetation over the greater Khingan Mountains using DFA method and results indicated that NDVI have self-similar properties. [68] evaluated the temporal scaling behavior of NDVI over China using DFA method and the analysis suggested that the NDVI time series displays strong long-range correlation throughout most of China with regional variability in the Hurst exponent h.…”

A method for alpine wetland delineation and features of border: Zoigê Plateau, China

“…Although the accuracy of wetland delineation needs to be further improved, this kind of research will lay the foundation for exploring methods of wetland monitoring at a low cost on a reserve scale in the future. It is possible to map more distinct wetland boundaries with lesser confusion and greater accuracies using hyperspectral data or high resolution images or time-series data (Feyisa et al, 2014;Guo et al, 2015). The combination of an automated water extraction index and the related digital elevation models could be used to establish models for wetland delineation in the future (Martinez-Lopez et al, 2014).…”

Intra-annual variations of vegetation status in a sub-tropical deciduous forest-dominated area using geospatial approach: a case study of Sali watershed, Bankura, West Bengal, India

“…So, the annual variation of vegetation study is an important part for the understanding and management of the environment. The inter-annual and interseasonal variability of NDVI has been studied by many scholars (Anyamba & Eastman, 1996;Eastman & Fulk, 1993;Guo, Zhang, Yuan, Zhao, & Xue, 2015;Li & Kafatos, 2000;Myneni, Los, & Tucker, 1996;Nemani et al, 2003;Sarkar & Kafatos, 2004). Most of their studies were focused on the factors behind the natural oscillation of natural vegetation.…”

“…Sarkar and Kafatos (2004) studied on the Indian subcontinent for several years to study the variability of vegetation and found the dominance of local climate anomaly in determining the vegetation. Several kinds of datasets, e.g., NOAA-AVHRR, SPOT-VGT, MODIS and GIMMS have been used (NOAA-AVHRR, SPOT-VGT, MODIS and GIMMS) to study the variability of vegetation at the local, regional and global scale (de Jong, de Bruin, de Wit, Schaepman, & Dent, 2011;Detsch, Otte, Appelhans, Hemp, & Nauss, 2016;Dubovyk, Landmann, Erasmus, Tewes, & Schellberg, 2015;Fensholt & Proud, 2012;Guo et al, 2015;Hou, Zhang, & Wang, 2011;Jeong, HO, GIM, & Brown, 2011;Lanorte, Lasaponara, Lovallo, & Telesca, 2014;Lu, Kuenzer, Wang, Guo, & Li, 2015;Martínez & Gilabert, 2009;Schucknecht, Erasmi, Niemeyer, & Matschullat, 2013;Sobrino & Julien, 2011;Teferi, Uhlenbrook, & Bewket, 2015;Zhao et al, 2013).…”