Climate change is intensifying rainfall variability over West Africa causing extreme events, such as droughts and floods [1
]. This may have consequences on land surface vegetation in this zone since climate plays a key role in vegetation condition. Therefore, it is essential to assess and quantify the response of vegetation to rainfall variability.
Land use/cover (LULC) is of particular interest in assessing the relationship between vegetation and rainfall because their modification is a key factor in global environmental change [2
]. For instance, Nightingale and Phinn [3
] found a strong relationship between precipitation and NDVI (Normalized Difference Vegetation Index) of five land cover types within South Australia and also noted significant positive correlation between precipitation and NDVI for cumulative precipitation over two to four months. Comparing different land covers response to rainfall in the Sahel and East Africa, Nicholson et al.
] concluded that NDVI is best correlated with the rainfall total for the concurrent and two antecedent months and found this correlation to be better in the Sahel than in East Africa. In Central Asia, Gessner et al.
] assessed vegetation sensitivity to precipitation anomalies based on time-series of NDVI and gridded precipitation datasets (GPCC Full Data Reanalysis) during the period 1982–2006 and found that the response of vegetation was strong for precipitation anomalies integrated over periods of two to four months, and there is a temporal lag between precipitation anomalies and vegetation activity for one to three months. Concerning the response timing of NDVI to rainfall, maximum correlation for lag of one month and three months cumulative rainfall have been reported by other researchers (e.g., [6
]). All these studies generally used long time series of NDVI and rainfall against land use/cover data derived for a given year [5
]. These researches assumed no change in land use/covers during the time of observation. Applying this method in an area like West African’s savanna where land use/covers change rapidly could cause bias in the analysis.
Modeling approach has also been applied in the assessment of vegetation response to rainfall (e.g., [8
]). Most often linear correlation is used to measure the relation between vegetation and rainfall (e.g., [3
]), but also non-linearity of this relationship was found (e.g., [11
]). For instance, a polynomial relationship was found to be the best fit between NDVI and rainfall by Boken and Chandra [11
] and Fontes de Sousa et al.
]. Gaughan et al.
] applied geographically weighted regression to investigate the response of the end of the wet season vegetation production, as measured by MODIS NDVI, to the different months of wet season rainfall for different land cover types in a regional catchment of southern Africa; the results showed that intra-annual wet season rainfall accounts for significant amounts of April NDVI variation. Chaoka et al.
] used a Seasonal Model (SM) and a linear Perturbation Model (LPM) to assess the relationship between NDVI and rainfall data in a large tropical catchment and concluded that the LPM performed better than the simple seasonal model. Hashemi [10
] applied a simple linear regression and multivariate regression to model NDVI response to different cumulated monthly rainfall in Azerbaijan province of Northern Iran and noticed that multivariate regression analysis performed better than simple linear regression.
One of the common points of these aforementioned studies (e.g., [3
]) is the choice of NDVI as indicator of vegetation dynamics. Very few studies, like Bobee et al.
], tried other indices like Leaf Area Index (LAI) to study the response of vegetation to rainfall in northwest Senegal.
In savanna regions, NDVI signals were found to be limited to express vegetation response to rainfall [14
]. Huete et al.
] supported that this is due to spectral variability of background material, such as soil albedo, which causes nonlinearity between NDVI and vegetation cover. The Enhance Vegetation Index (EVI) has been developed to improve the quality of NDVI, by solving the problem of distortions in the reflected light due to atmospheric particles and the ground cover below the vegetation. In addition, EVI does not saturate rapidly as NDVI [17
However, in literature, comparison studies carried out between NDVI and EVI showed contrasting results. Son et al.
] noted that EVI-based models were slightly more accurate than those from NDVI-based models in rice crop yields estimation in the Mekong River Delta of Vietnam. On the other hand, Li et al.
] in a study carried out in Northern Hebei Province of China indicate that NDVI has a stronger correlation with field data of vegetation covers than EVI and so has obvious advantages for predicting natural vegetation coverage better than EVI. Wardlow et al.
] in turn found that for crop mapping EVI and NDVI produced equivalent results in Southwest Kansas.
These contrasting conclusions raise the need for more investigation on the performance of NDVI and EVI especially relating to the relationship between vegetation and rainfall variability in West Africa’s savanna where comparative studies are limited, and researches on both indices behavior for different land use/cover types are still rare. The present research, focusing on the relation of NDVI and EVI with rainfall, will fill this gap. The aim of this study is to assess the sensitivity of NDVI and EVI to rainfall variability in the Southwest of Burkina Faso for different land use/covers.
A study to assess the relationship between vegetation dynamics and rainfall variability has been carried out in the savanna biome of the Southwest of Burkina Faso. Unlike other studies, this study focuses on comparative analysis between NDVI and EVI in assessing the response of land use/covers to different rainfall indicators.
The main conclusion of the research is that NDVI and EVI are well correlated with rainfall variability according to different land use/cover types. This finding is supported by the results of previous studies. Some similarities were noticed in the response of NDVI and EVI to rainfall, but discrepancies were also found and mainly seen on the response magnitude and the influence of the rainfall indicators. Between both indices, and for all land use/covers, NDVI was generally more sensitive to rainfall variability compared to EVI. However, the difference between the Pearson’s coefficient values of both vegetation indices was slight in the four stations.
As this study focused on only a selected area on a local scale, there is the need for further analysis on regional scale in order to reaffirm the results obtained. Nevertheless, the study reveals important findings that will trigger further investigations on EVI and NDVI with respect to their accuracy in estimating vegetation temporal dynamics. For example further study to understand the difference in response magnitude between both indices by determining the contributing factors has to be explored.
The similar performance of the vegetation indices may suggest combined investigations on biophysical parameters like Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Optimal correlations of a certain vegetation index may be misleading in case that this index is underperforming in its relation to that quantifiable biophysical variables used in environmental modeling. Those investigations could be relevant for improving the understanding of the environmental dynamics that is occurring in the Southwest of Burkina Faso.