## 4 Discussion of Results
### Pipeline Scale Analysis  
Pipeline Impact Indicator  
The speed and direction of water flow within the Delta impact the distribution of oil following a spill incident. If clean-up efforts are delayed, the flow dynamics of the Delta are likely to cause a more prolonged and widespread impact. As the mangroves are predominantly found in the coastal vegetation region, the surrounding water bodies have a complex repetitive tidal cycle rather than a unidirectional flow. Over time, this estuarine environment becomes a persistent sink for the petroleum hydrocarbons in crude oil, constantly redistributing through sediments and on the water surface rather than flushing the contaminants out to sea [41]. However, the immediate vicinity of the spill is likely to experience the most severe damage as the concentration of oil will be higher in that region initially, particularly given the hydrophobicity of many hydrocarbons [42]. Figure 10: Classification results for the year 2023 overlaid with key features including pipeline infrastructure, built-up areas, oil refineries, and terminals. Spill locations from [14] are also illustrated with the size of each circular spill marker, indicating the estimated quantity and the colour denoting the reported date of the spill. This figure illustrates the spatial and temporal distribution of the reported oil spills during the study period. Oil spill data alone are unreliable as a metric to evaluate the environmental impact of pipeline infrastructure due to the need for thorough and timely reporting. It is well-documented that there is great uncertainty regarding data veracity and complete spill reports in the Niger Delta [43]. The self-reporting aspect demanded by regulatory compliance brings into question a conflict of interest and leads to reports that are incomplete, inconsistent, or incoherent. It is also clear that the resources required to cross-examine reports are a point of contention amongst analysts who are working towards transparency in the industry. Though the availability of reports through a public resource such as NOS-DRA [14] is certainly to be commended, discrepancies of the order of 15-20% between publicly available data and international oil company published statistics [43] have been reported. Research built entirely upon such large uncertainty calls for more independent and objective methods of assessing the impact of spills in the region. To address this, we developed the Pipeline Impact Indicator (PII) to quantify the impact based on the rate of bare land increase within a 2.5 km buffer of each pipeline, a buffer size supported by spill locations from [14] and the Euclidean distance analysis detailed in Section 3.4. The PII maps, shown in Figure 11, provide a more objective metric for identifying pipeline segments with accelerated environmental damage. This section will discuss four instances of each implementation of the PII, chosen specifically to characterise the minimum and maximum values, as well as two additional examples that are of interest given the regional context. These results are detailed in Table 3.  
The PII revealed varying levels of impact across different pipelines and pipeline buffer regions, as we show in Figures 12 and 13. For instance, sample pipeline 1 (southwest of Bille) marked in Figure 11 (top panel), has a low PII of 0.18, corresponding to the relatively small number of spills and the limited extent of bare land per unit of affected area. In contrast, pipeline segment 2, which runs through Bille, exhibited a much higher PII of 0.78, indicating substantial damage which we originally identified in our regional classification map as being associated with the largest quantity of oil spills within a 2.5 km radius of its associated pipeline infrastructure (Figure 7). This area has been exposed to a large accumulated quantity of crude oil spillages relative to the length of the infrastructure component.