Abstract
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.
Highlights
Many rangelands are suffering severe degradation processes
We applied a combination of methods to study Normalized Difference Vegetation Index (NDVI) series that allows an in-depth understanding of the NDVI performance: the NDVI temporal trend, where we studied the presence of changes and trends in the dynamics of our time series; the Hurst index (HI), a monofractal technique to analyze the persistence of our time series; as well as the generalized structure function (GSF) and multifractal detrended fluctuation analysis (MF-detrended fluctuation analysis (DFA)), multifractal techniques that use different mathematical approaches to study a multitude of parameters of time series to see how they scale differently
The Pettitt test found a shift in the trend in the four areas: Area 1 (A1) and Area 2 (A2) in the autumn of 2006; Area 3 (A3) in the autumn of 2007; and Area 4 (A4) in the autumn of 2008
Summary
Many rangelands are suffering severe degradation processes. The tailored monitoring of vegetation to inform the sustainable management of these areas will prevent their degradation [1,2,3]. New tools and metrics use complexity to understand and predict natural systems’ behavior and improve monitoring and management programs. Advances suggest that complex-systems science can develop prediction frameworks with metrics that explain the underlying causes of spatiotemporal dynamics [4]. Remote sensing and satellite monitoring methods are commonly used to study ecology and agriculture [5,6,7,8].
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