Monitoring vegetation and land cover changes over time in oil-impacted regions is crucial, as it helps determine the levels of ecological degradation and informs remediation options. Satellite-based remote sensing techniques provide cost-effective methods for such analyses. This study aimed to identify the challenges encountered while using Landsat images to detect changes in vegetation health and land cover in Bodo, a hydrocarbon-impacted community in the Niger Delta region of Nigeria, from 2003 to 2023. Landsat 7 ETM+ and Landsat 8 OLI imagery were used to derive Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) over 20 years. Two challenges were encountered. First, Landsat 7 ETM+ was employed from 2003 to 2008 due to the unavailability of Landsat 5. However, the 2008 images were unsuitable due to a malfunction in the Scan Line Corrector (SLC). Secondly, high cloud coverage caused data inconsistencies in 2013 Landsat 8 scenes for the specified Path/Row. Hence, 2008 and 2013 were excluded from the analysis. Results from previous years revealed that NDBI values gradually increased, suggesting minor urban expansion. Stable but low NDWI levels suggest water stress, while changing NDVI values indicate vegetative health. These indices show environmental trends from 2003 to 2023, although the interrupted data sequence makes multi-year comparisons difficult. This study emphasises the importance of correcting sensor malfunctions, mitigating cloud influence, and closing temporal data gaps when utilising satellite imagery for environmental assessment. It recommends the use of cloud-masking methodologies and radar-based datasets, such as Sentinel-1, to enhance data quality and continuity. These adaptive solutions are crucial for more effective GIS-based environmental monitoring.