# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 2 Materials and Methods
### Data  
#### 2.2.1 Satellite-Derived Vegetation Index (NDVI)  
Remote sensing Vegetation Indices (VIs) can be used to quantitative and qualitative evaluate vegetation cover, vigor, and growth dynamics [19]. The choice of vegetation index, the NDVI, was based on the result from [PERSON] [30], which shows that five spectral indices, namely, the normalised difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), adjusted resistant vegetation index (ARVI2), green near-infrared (G/NIR), and green shortwave infrared (G/SWIR), out of the twenty (20) VI tested, were consistently sensitive to the effects of oil pollution on vegetation in the Niger Delta. Similarly, the study by [PERSON] et al. [10] shows that three of the indices--EVI, NDVI, and SAVI--were very sensitive to the effects of oil spills on the different vegetation covers in the Niger Delta among five (5) vegetation indices. The NDVI has been demonstrated to be a robust vegetation index in monitoring the impact of oil spills on the health of vegetation [1; 12; 30; 31; 44] and is the most suitable index to detect the effects of petroleum pollution on vegetation [45]. The NDVI is an index of plant greenness as well as an indicator of the density of plants [46], which is based on the reflectance properties of the areas covered by the vegetation [47].  
The launch of the Landsat (ESTS-1) mission in 1972 sparked investigations surrounding its capability for vegetation monitoring and categorisation [8]. The health of vegetation in the study area was determined using the Landsat collection 1 NDVI product, computed using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery for the period 2006-2018 via the EROS Science Processing Architecture (ESPA) on-demand interface. The Landsat 1 data collection comprises derived vegetation indices that are atmospherically corrected and orthorectified to provide an archive of consistent data for time-series analysis [48]. The ready-processed Landsat NDVI data were downloaded from [[https://espa.cr.usgs.gov/](https://espa.cr.usgs.gov/)]([https://espa.cr.usgs.gov/](https://espa.cr.usgs.gov/)) (accessed on 22 December 2024). geometrically corrected, and projected to WGS84 Universal Traverse Mercator (UTM) projection Zones 31 and 32 (Table 1). For the period 2006-2018, a total of 22 NDVI images acquired around the timing of oil spill incidences were used in this study (Table 2). These images were selected to coincide with the most consistent weather conditions during the dry season from December to February to minimise cloud cover and temperature variability. Accordingly, the effect of meteorological conditions on the observed changes in NDVI is mitigated.  
The NDVI in the ready-processed data is calculated from each Landsat 7 ETM+ according to Equation (1):  
\[\mathrm{NDVI}=\frac{\mathrm{NIR}-\mathrm{RED}}{\mathrm{NIR}+\mathrm{RED}} \tag{1}\]  
where RED and NIR represent the spectral reflectance bands measurements acquired in the red (visible) and near-infrared regions with a wavelength range of 0.63-0.69 \(\upmu\)m and 0.77-0.90 \(\upmu\)m, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Sensor** & **Path/Row** & **UTM Zone** \\ \hline L7 & ETM+ & 188/56 & 32 \\ L7 & ETM+ & 188/57 & 32 \\ L7 & ETM+ & 189/56 & 32 \\ L7 & ETM+ & 189/57 & 31 \\ L7 & ETM+ & 190/56 & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor and orbit path/row of the Landsat 7 ETM+ imagery.