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

Tertsea Igbawua,Shahzad Ali,Jiahua Zhang,Fengmei Yao

doi:10.1109/access.2019.2933235

Abstract

Satellite-derived Normalized Difference Vegetation index (NDVI) data records offer important sources for long term correlation modelling over West Africa. In this study, we assessed long range correlations in half monthly NDVI records over West Africa from 1982 to 2011 using GIMMS NDVI. In our analysis, we assessed (a) the annual and seasonal trends obtained using Ordinary Linear Regression, (b) the detrended lag-1-autocorrelation C(1), (c) the Detrended Fluctuation Analysis (DFA) scaling Hurst exponent h and (d) the Multifractal (MF) characteristics of NDVI. Results show that there exist some patterns or trends in the records that persist over time. The value of C(1) for NDVI was obtained as 0.989 is significant at 95% confidence interval. Consequently, the scaling h values of the Hurst DFA showed that about 37.4, 20.5, 41.7 and 0.5% of the vegetated areas are anti-correlated (h <; 0.5), un-correlated (h = 0.5), correlated (0.5 <; h <; 1) and uncorrelated random walk (h = 1), respectively. The trend analysis from Ordinary Least square Regression (OLR) shows that about 54.3, 0.1 and 45.6% of the vegetated areas are positively, uncorrelated and negatively correlated, respectively. Our findings revealed that the DFA method performed better than OLR and the findings could be useful in identifying areas with improved and degraded vegetation, which cannot be properly captured by the OLR method. Accordingly, the comparison of the MF-DFA results of original data to those of shuffled and surrogate series indicated that the multifractal nature of considered time-series is both from PDF and long-range correlations but arguably, MF due to long range correlation dominates over West Africa. The research is therefore helpful in the formulating crop and environmental management policies that may be used to improve ecosystem management using a long term plan (inter-annual) or short term (inter-seasonal) planning.

Highlights

The vegetation of West Africa fluctuates rapidly both at spatial and temporal scales and the rate of fluctuation depends on the level of vegetation degradation and/or replacement [1], [2]
The decomposition of the original Normalized Difference Vegetation index (NDVI) series was done according to the equation Yt = three components: Trend (Tt) + St + Rt [85], and our assumption here is that, NDVI temporal and spatial series are composed of some pattern which is concealed by random noise
The spatially averaged Hurst exponent over West Africa for 30 years is 0.58 while that of China is 0.78 over a period of 25yrs, which implies China’s vegetation is more persistent than West Africa. This is because China has adopted sound ecological development and maintenance programs compared to West Africa

Summary

Introduction

The vegetation of West Africa fluctuates rapidly both at spatial and temporal scales and the rate of fluctuation depends on the level of vegetation degradation and/or replacement [1], [2]. A lot of environmental factors are responsible for long-term vegetation degradation over the region, including naturally occurring processes [3] and human activities [4]–[11]. The naturally occurring processes could be erosion, drought, floods, population pressure, increased. Vegetation has a good coupling with climate over the region [12], [13] and researches have shown that the depleted vegetation surface and rapid evaporation of moisture from the surface may have provided a positive feedback [14] that raises and sustains the droughts conditions [15].

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