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

2015 ◽  
Vol 7 (10) ◽  
pp. 12942-12960 ◽  
Author(s):  
Xiaoyi Guo ◽  
Hongyan Zhang ◽  
Tao Yuan ◽  
Jianjun Zhao ◽  
Zhenshan Xue
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Scaling Behavior ◽  
Fluctuation Analysis ◽  
Temporal Scaling ◽  
Detrended Fluctuation ◽  
Index Time Series

scholarly journals Analysis of Multifractal and Organization/Order Structure in Suomi-NPP VIIRS Normalized Difference Vegetation Index Series of Wildfire Affected and Unaffected Sites by Using the Multifractal Detrended Fluctuation Analysis and the Fisher–Shannon Analysis

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 415 ◽  
Author(s):  
Rui Ba ◽  
Weiguo Song ◽  
Michele Lovallo ◽  
Siuming Lo ◽  
Luciano Telesca
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Temporal Dynamics ◽  
Order Structure ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

The analysis of vegetation dynamics affected by wildfires contributes to the understanding of ecological changes under disturbances. The use of the Normalized Difference Vegetation Index (NDVI) of satellite time series can effectively contribute to this investigation. In this paper, we employed the methods of multifractal detrended fluctuation analysis (MFDFA) and Fisher–Shannon (FS) analysis to investigate the NDVI series acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar-Orbiting Partnership (Suomi-NPP). Four study sites that were covered by two different types of vegetation were analyzed, among them two sites were affected by a wildfire (the Camp Fire, 2018). Our findings reveal that the wildfire increases the heterogeneity of the NDVI time series along with their organization structure. Furthermore, the fire-affected and fire-unaffected pixels are quite well separated through the range of the generalized Hurst exponents and the FS information plane. The analysis could provide deeper insights on the temporal dynamics of vegetation that are induced by wildfire.

FLUCTUATION ANALYSIS OF MONTHLY RAINFALL TIME SERIES

2010 ◽  
Vol 09 (02) ◽  
pp. 219-228 ◽  
Author(s):  
JORGE O. PIERINI ◽  
LUCIANO TELESCA
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Scaling Behavior ◽  
Monthly Rainfall ◽  
Fluctuation Analysis ◽  
Rainfall Time Series ◽  
Scaling Exponents ◽  
Times Series ◽  
Detrended Fluctuation

The monthly rainfall time series, spanning more than a century, recorded in several sites in the middle Argentina were analyzed. The power spetral density (PSD) method reveals the presence of annual and semi-annual cyclic fluctuations. The detrended fluctuation analysis (DFA) performed on the residual times series (after removing the periodicities) shows a scaling behavior, characterized by DFA scaling exponents ranging between 0.54 and 0.58. These findings could contribute to a better understanding of rainfall dynamics.

scholarly journals Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 576
Author(s):  
Ernesto Sanz ◽  
Antonio Saa-Requejo ◽  
Carlos H. Díaz-Ambrona ◽  
Margarita Ruiz-Ramos ◽  
Alfredo Rodríguez ◽  
...  
Keyword(s):  
Time Series ◽  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Vegetation Index ◽  
Spatial Diversity ◽  
Fluctuation Analysis ◽  
Term Memory ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Fractal Analyses ◽  
Detrended Fluctuation

Estimates suggest that more than 70% of the world’s rangelands are degraded. The Normalized Difference Vegetation Index (NDVI) is commonly used by ecologists and agriculturalists to monitor vegetation and contribute to more sustainable rangeland management. This paper aims to explore the scaling character of NDVI and NDVI anomaly (NDVIa) time series by applying three fractal analyses: generalized structure function (GSF), multifractal detrended fluctuation analysis (MF-DFA), and Hurst index (HI). The study was conducted in four study areas in Southeastern Spain. Results suggest a multifractal character influenced by different land uses and spatial diversity. MF-DFA indicated an antipersistent character in study areas, while GSF and HI results indicated a persistent character. Different behaviors of generalized Hurst and scaling exponents were found between herbaceous and tree dominated areas. MF-DFA and surrogate and shuffle series allow us to study multifractal sources, reflecting the importance of long-range correlations in these areas. Two types of long-range correlation appear to be in place due to short-term memory reflecting seasonality and longer-term memory based on a time scale of a year or longer. The comparison of these series also provides us with a differentiating profile to distinguish among our four study areas that can improve land use and risk management in arid rangelands.

Investigation scaling behavior of earthquake-related signals in magnetotelluric measurements, Northern Algeria

2021 ◽  
Author(s):  
Ahmed Seddik Kasdi ◽  
Abderrezak Bouzid ◽  
Mohamed Hamoudi ◽  
Abdslem Abtout
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Seismic Event ◽  
Scaling Behavior ◽  
Stationary Time Series ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Dynamics ◽  
Electromagnetic Signals ◽  
Detrended Fluctuation

<p>The north-central region of Algeria has been characterized by a swarm-type seismicity, after the strong Mw6.8 Boumerdès earthquake of May 21, 2003, culminating with the earthquake that occurred on July 17, 2013 of magnitude Mw=5. A magnetotelluric station was installed on December 2014 in the Medea region, 60 km south of the capital Algiers. We measured the five components of the telluric and magnetic field with a sampling frequency of 15 Hz. The seismic activity in the region provided the opportunity to observe and study the earthquake’s related electromagnetic signal. The scaling properties of the recorded electric and magnetic time series were investigated. On the basis of multifractal detrended fluctuation analysis, which is a powerful method for detecting scaling in non-stationary time series, deviations from the uniform scale of the power law were identified and quantified. We investigated the time dynamics of the earthquake related electromagnetic time series measured at the magnetotelluric station. The multifractal detrended fluctuation analysis showed the different multifractality properties of electromagnetic signals before, during and after the seismic event. The results of this work show an unstable scaling behavior in electromagnetic data during the occurrence of the seismic event. These first results could be useful in the framework of seismo-electromagnetic signals studies.</p>

scholarly journals On the Validity of Detrended Fluctuation Analysis at Short Scales

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 61
Author(s):  
Pedro Carpena ◽  
Manuel Gómez-Extremera ◽  
Pedro A. Bernaola-Galván
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Time Scales ◽  
Real World ◽  
Detrended Fluctuation Analysis ◽  
Scaling Behavior ◽  
Fluctuation Analysis ◽  
Scaling Properties ◽  
Short Scale ◽  
Detrended Fluctuation

Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function F(ℓ), which quantifies the fluctuations of the time series around the local trend, which is substracted (detrended). If the time series exhibits scaling properties, then F(ℓ)∼ℓα asymptotically, and the scaling exponent α is typically estimated as the slope of a linear fitting in the logF(ℓ) vs. log(ℓ) plot. In this way, α measures the strength of the correlations and characterizes the underlying dynamical system. However, in many cases, and especially in a physiological time series, the scaling behavior is different at short and long scales, resulting in logF(ℓ) vs. log(ℓ) plots with two different slopes, α1 at short scales and α2 at large scales of observation. These two exponents are usually associated with the existence of different mechanisms that work at distinct time scales acting on the underlying dynamical system. Here, however, and since the power-law behavior of F(ℓ) is asymptotic, we question the use of α1 to characterize the correlations at short scales. To this end, we show first that, even for artificial time series with perfect scaling, i.e., with a single exponent α valid for all scales, DFA provides an α1 value that systematically overestimates the true exponent α. In addition, second, when artificial time series with two different scaling exponents at short and large scales are considered, the α1 value provided by DFA not only can severely underestimate or overestimate the true short-scale exponent, but also depends on the value of the large scale exponent. This behavior should prevent the use of α1 to describe the scaling properties at short scales: if DFA is used in two time series with the same scaling behavior at short scales but very different scaling properties at large scales, very different values of α1 will be obtained, although the short scale properties are identical. These artifacts may lead to wrong interpretations when analyzing real-world time series: on the one hand, for time series with truly perfect scaling, the spurious value of α1 could lead to wrongly thinking that there exists some specific mechanism acting only at short time scales in the dynamical system. On the other hand, for time series with true different scaling at short and large scales, the incorrect α1 value would not characterize properly the short scale behavior of the dynamical system.

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