Abstract
The Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to negligence, slow response times following oil spills, and difficulties regarding access and safety. This study investigates the pervasive issue of oil spills in the Niger Delta region, by employing a remote sensing approach, leveraging geospatial cloud computing and machine learning to evaluate vegetation health indices (SR, SR2, NDVI, EVI2, GRNDVI, GNDVI) derived from PlanetScope satellite data. These indices were analysed using Slow Moving Average regression, which revealed significant declines in vegetation health following oil spill events. The contaminated landcovers exhibit a Spearman’s correlation coefficient (ρ) ranging from − 0.68 to − 0.82, P < 0.005 and P -values below 0.05 in most landcover categories, suggesting a clear and consistent downward trend in the indices’ values, reflecting a decrease in vegetation health in contaminated areas between 2016 and 2023. A random forest classifier further quantified the extent of contaminated land cover, demonstrating the effectiveness of this method for monitoring environmental damage in this challenging terrain. Contaminated vegetation, wetland, farmland, and grassland cover approximately 4% (1180 ha) of the total Niger Delta area. This integrated approach will enable decision-makers, including government agencies and oil companies, to gain a deeper understanding of the environmental consequences of oil pollution and implement targeted mitigation and remediation strategies.