## 3 Materials and Methods
### Developing a Pipeline Impact Indicator  
To analyse the impact region surrounding the pipelines available in the OSM shapefile, a 2.5 km uniform distance buffer was applied per pipeline object. From statistical analysis of the Euclidean distance between each reported spill event epicentre and the nearest pipeline, the mean distance of 852 m with a standard deviation of 1687 m suggests that the majority of spill events occur within this 2.5 km range. Each annual classification was reduced to the area of bare pixels within this buffer. The area within the buffer was calculated using the _pixelArea_ function in GEE, paired with a grouped reducer and the per-class values were then converted to hectares. This provided a time series of deforested land changes over time for each pipeline, reduced to its entire length or per specific pipeline segment. Ordinary Least Squares (OLS) regression was used to analyse degradation rates and assess land-use changes. In this context, the slope (\(m\)) represents the degradation rate within the affected area, quantifying the hectares converted to bare land per year. Furthermore, the \(R^{2}\) value, indicating goodness of fit, provides a means of delineating between 'impulsive' land-use changes and a long-term contaminant-induced increase in bare pixel classification. In Figure 4 we show maps for both \(R^{2}\) and \(\sigma\) (fraction of non-null pixels) across the region of interest. Examining the resulting linear fits indicated that these parameters' extreme values were correlated for boundary condition scenarios sampled from sites throughout the Delta evident in Figure 4. Regions most impacted by persistent spills exhibited a more linear trend in the spread of degraded land, likely due to the compound impact of repeated spills in the mangroves. Though the impact on seedlings and a reduction in leaf density can be observed within days in a heavily contaminated area [36], numerous studies indicate more established mangroves typically experience mortality and dieback on a scale of up to 14 years depending on spill severity and mangrove species [37; 38; 39]. Additionally, the site deterioration, sediment loss, and erosion that follow the event can cause permanent habitat loss within 30 years [37; 38]. Therefore, sites with a stark deviation in trend are more likely to have experienced some human intervention impulse event, such as deforestation for timber production, agricultural land expansion, or settlement expansion. We confirmed this hypothesis following the examination of contemporaneous Sentinel-2 true-colour composite surrounding those pipeline segments exhibiting a low R\({}^{2}\) value. In these composites, we found evidence of deforestation for cropland expansion and small areas of urban sprawl which had not been captured in the built-area mask due to the limitations of using a global land-cover mask from 2021-2022. A 'Pipeline Impact Indicator' (PII) was constructed using the compiled regression statistics to capture the severity of degradation for each location using the best-fit slope (\(m\)), \(R^{2}\) and the fraction of non-null pixels within the delineated buffer area (defined here as \(\sigma\)). A Min-Max Scaler was applied to the slope to scale the data distribution and produce an indicator between 0 and 1 for ease of interpretation within the frame of reference. When applied at the entire pipeline extent scale, the slopes were not normalised to the measured area, as this produced a biased weighting towards the shortest sections of the pipeline compared to the more extensive sections with broader impact. Consequently, short pipeline sections with high impact relative to their small impact area were not well represented in this implementation of the PII. This limitation motivated the implementation of the PII at a more granular scale, where normalising to the measured area would not skew results. This approach yielded two mapped representations of the PII, the entire pipeline length and at segmented pipeline scales.