Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought

Abstract

The variability of temperature and precipitation influenced by El Niño-Southern Oscillation (ENSO) is potentially one of key factors contributing to vegetation product in southern Africa. Thus, understanding large-scale ocean-atmospheric phenomena like the ENSO and Indian Ocean Dipole/Dipole Mode Index (DMI) is important. In this study, 16 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform was used to analyze the vegetation response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP) to climatic variability. The MODIS enhanced vegetation index (EVI), burned area index (BAI), and normalized difference infrared index (NDII) were also analyzed. The study used the Modern Retrospective Analysis for the Research Application (MERRA) model monthly mean soil temperature and precipitations. The Global Land Data Assimilation System (GLDAS) evapotranspiration (ET) data were used to investigate the HiP vegetation water stress. The region in the southern part of the HiP which has land cover dominated by savanna experienced the most impact of the strong El Niño. Both the HiP NDVI inter-annual Mann-Kendal trend test and sequential Mann-Kendall (SQ-MK) test indicated a significant downward trend during the El Niño years of 2003 and 2014-2015. The SQ-MK significant trend turning point which was thought to be associated with the 2014-2015 El Niño periods begun in November 2012. The wavelet coherence and coherence phase indicated a positive teleconnection/correlation between soil temperatures, precipitation, soil moisture (NDII), and ET. This was explained by a dominant in-phase relationship between the NDVI and climatic parameters especially at a period band of 8-16 months. Climate 2018, 6, 95 2 of 24The influence of drought on vegetation varies in the spatial and temporal scales, and these are projected to increase with climate change [6,7]. This behavior affects wildlife, particularly in semi-arid and arid environments where herbivory is strongly restricted by vegetation extent and water availability [8]. In the north-east part of KwaZulu-Natal, South Africa, for example, droughts are becoming a recurrent and prominent feature [9,10], affecting vegetation, water and wildlife resources notably in the Hluhluwe-iMfolozi Park (HiP), the oldest proclaimed game reserve in Africa, as reported in this paper. Furthermore, these impacts have potential consequences that could incapacitate this game reserve's support of its specialist grazers such as rhinos [11].Understanding the association between vegetation productivity and climatic variables such as precipitation and temperature has, therefore, become a high priority. To address this, spatiotemporal tools that can integrate climate data with other information of interest are required....

Leaf water potential of coffee estimated by landsat-8 images

“…The use of NDVI as an indicator of drought vegetation stress and soil moisture was already reported by several authors [2,9,10,12] as NDVI provides a general measurement of vegetation state and health and has been used for accessing drought status since the 1970s when this index was proposed by Rouse [33]. However, there is also a difficulty in monitoring water stress using vegetation indices as this response is observed when notable damage to the culture has already occurred [10].…”

“…Monitoring the water conditions of coffee plantations requires the use of technologies that allow the evaluation of large areas or regions. In this context, the use of remote sensing presents as an opportunity to quantify drought stress when there is no in-situ weather station available (i.e., for time-series creation) [1,2]. In the past years, the new generation of free mediumresolution satellite imagery such as the Landsat-8/OLI and Sentinel-2/MSI presents suitable information for drought monitoring in agricultural lands [3,4].…”

Study on Temporal Variations of Surface Temperature and Rainfall at Conakry Airport, Guinea: 1960–2016

“…This method is called the Sequential Mann-Kendall (SQ-MK), and it is well explained by [33] and other authors [31,34]. This method has been found to perform very well in trend analyses of stream flow and precipitation [35] and also in the field of earth remote sensing [36].…”

Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran)

“…It is an important factor in the surface energy budget and soil moisture variability [1]. Several climate research projects focused on the quantification of vegetation cover to study the emergence of drought [2][3][4][5]. Also, the relation of dust storms with vegetation cover has been the subject of several studies [4,6,7].…”

“…The time series of NDVI can be used for change detection in vegetation health and growth, especially in semi-arid regions [13][14][15][16]. The NDVI time series can be investigated from two perspectives: (1) annual or seasonal parameters and their longtime trends, and (2) abrupt changes between consecutive years.…”