Multispectral imagery for detection and monitoring of vegetation affected by oil spills and migration pattern in Niger Delta Region, Nigeria

Abstract

Oil spills in the Niger Delta area can be detected and monitored using this novel technique. Landsat 5 and 8 images were used to assess various vegetation stress, such as Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Atmospheric Resistant Vegetation Index, Green Near Infrared and Green Short-wave Infrared, from the spill site in 2019, non-spill site (1992 pre-oil spill) and 2020 post-oil spill events, respectively. There is a substantial difference (p-value <0.005) in the vegetation conditions at the Spill Site and the non-spill Site in 2020 in terms of NDWI, SAVI, ARVI2 and G-NIR and G-SWIR. NDVI, SAVI, ARVI2, G-NIR, G-SWIR, and G-SWIR. There is a very significant difference in vegetation conditions between the pre-spill event and the post-spill event in 2020 (p-value <0.005). The oil spills' migration patterns and flow directions indicate from north to south along the runoff water using SRTM data. The sentinel 1 data revealed visualization of the flooded areas, including water surfaces that are stable around oil pipelines and the surroundings, using calibration threshold and the RGB band method to distinguish flooded areas from permanent water bodies. This was used to map areas affected by the oil spill on land and water bodies for proper environmental assessment before and during the flood of the oil spill environment. Multi spectral imagery is therefore a veritable tool for detection, response and monitoring of oil spills from pipelines.