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

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 cha...

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Autores principales: Sanz, Ernesto, Saa-Requejo, Antonio, Díaz-Ambrona, Carlos H., Ruiz-Ramos, Margarita, Rodríguez, Alfredo, Iglesias, Eva, Esteve, Paloma, Soriano, Bárbara, Tarquis, Ana M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151831/
https://www.ncbi.nlm.nih.gov/pubmed/34067228
http://dx.doi.org/10.3390/e23050576
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author Sanz, Ernesto
Saa-Requejo, Antonio
Díaz-Ambrona, Carlos H.
Ruiz-Ramos, Margarita
Rodríguez, Alfredo
Iglesias, Eva
Esteve, Paloma
Soriano, Bárbara
Tarquis, Ana M.
author_facet Sanz, Ernesto
Saa-Requejo, Antonio
Díaz-Ambrona, Carlos H.
Ruiz-Ramos, Margarita
Rodríguez, Alfredo
Iglesias, Eva
Esteve, Paloma
Soriano, Bárbara
Tarquis, Ana M.
author_sort Sanz, Ernesto
collection PubMed
description 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.
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spelling pubmed-81518312021-05-27 Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study Sanz, Ernesto Saa-Requejo, Antonio Díaz-Ambrona, Carlos H. Ruiz-Ramos, Margarita Rodríguez, Alfredo Iglesias, Eva Esteve, Paloma Soriano, Bárbara Tarquis, Ana M. Entropy (Basel) Article 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. MDPI 2021-05-07 /pmc/articles/PMC8151831/ /pubmed/34067228 http://dx.doi.org/10.3390/e23050576 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sanz, Ernesto
Saa-Requejo, Antonio
Díaz-Ambrona, Carlos H.
Ruiz-Ramos, Margarita
Rodríguez, Alfredo
Iglesias, Eva
Esteve, Paloma
Soriano, Bárbara
Tarquis, Ana M.
Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study
title Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study
title_full Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study
title_fullStr Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study
title_full_unstemmed Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study
title_short Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study
title_sort generalized structure functions and multifractal detrended fluctuation analysis applied to vegetation index time series: an arid rangeland study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151831/
https://www.ncbi.nlm.nih.gov/pubmed/34067228
http://dx.doi.org/10.3390/e23050576
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