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.

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.

scholarly journals Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1157
Author(s):  
Faheem Aslam ◽  
Saima Latif ◽  
Paulo Ferreira
Keyword(s):  
Time Series ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Financial Time Series ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Detrended Fluctuation

The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

Multifractal detrended fluctuation analysis of SENSEX fluctuation in the Indian stock market

2010 ◽  
Vol 88 (8) ◽  
pp. 545-551 ◽  
Author(s):  
Srimonti Dutta
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Detrended Fluctuation Analysis ◽  
Nonstationary Time Series ◽  
Anomalous Behaviour ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Indian Stock Market ◽  
Detrended Fluctuation

The fluctuation of SENSEX in the Indian stock market for the period Jan 2003–Dec 2009 is studied using the multifractal detrended fluctuation analysis (MFDFA) approach. The effect of the fall in the stock market in 2008 is also investigated. The data exhibits that the nonstationary time series of SENSEX fluctuations are multifractal in nature. An increase in the degree of multifractality prior to the anomalous behaviour in the SENSEX values is also observed. The increase in the degree of correlation for the period 2007–2009 is also responsible for the meteoric rise and the catastrophic fall in the values of SENSEX.

MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS OF ELECTRIC LOAD SERIES

Fractals ◽  
2015 ◽  
Vol 23 (02) ◽  
pp. 1550010 ◽  
Author(s):  
XIAOHUI YUAN ◽  
BIN JI ◽  
YANBIN YUAN ◽  
YUEHUA HUANG ◽  
XIANSHAN LI ◽  
...  
Keyword(s):  
Time Series ◽  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Quadratic Function ◽  
Fluctuation Analysis ◽  
Electric Load ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Range Correlation ◽  
Detrended Fluctuation ◽  
Multifractal Nature

Multifractal detrended fluctuation analysis (MF-DFA) method is applied to analyze the daily electric load time series. The results of the MF-DFA show that there are three crossover timescales at seven days, 15 days and 365 days approximately in the fluctuation function. Also we find that these fluctuations have multifractal nature with long range correlation behavior. The multifractal singularity spectrum of the daily electric load series has been fitted by the quadratic function model. Comparing the MF-DFA results of the original load series with those of shuffled and surrogate series, it concludes that the multifractal characteristics of the daily electric load time series is due to both broadness of the probability density function and long-range correlation, and the long-range correlation is dominant.

scholarly journals A probe into the Multifractal Behaviour of Total Ozone Time Series through Detrended Fluctuation Analysis

2021 ◽  
Author(s):  
Sombit Chakraborty ◽  
Surajit Chattopadhyay
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Total Ozone ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Order Polynomial ◽  
Local Trend ◽  
Detrended Fluctuation ◽  
Daily Total

Abstract The present study reports a multifractal detrended fluctuation analysis of total ozone time series. Considering daily total ozone concentration (TOC) data ranging from 2015 to 2019, we have created a new profile by subtracting the trend. Subsequently we have divided the profile \({X}_{i}\) into non intersecting segments of equal time scale varying from 25 to 30. Fitting a second order polynomial, we have eliminated the local trend from each segment and thereafter we have computed the detrended variance. Finally the multifractal behaviour has been identified and the singularity spectra has helped us in obtaining the generalised Hurst exponent which in this case has come out to be greater than 0.5.

Multifractal detrended fluctuation analysis of soil radon (222Rn) and thoron (220Rn) time series

2021 ◽  
Vol 328 (1) ◽  
pp. 425-434
Author(s):  
Muhammad Rafique ◽  
Javid Iqbal ◽  
Kashif Javed Lone ◽  
Kimberlee Jane Kearfott ◽  
Saeed Ur Rahman ◽  
...  
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Soil Radon ◽  
Detrended Fluctuation
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