# The Application of Satellite Image Analysis in Oil Spill Detection
## 3 Results  
To present test results, Sentinel-2 data in visual analysis were shown in RGB colours composition. The satellite image taken on 10 May 2020 (two weeks before the disaster), was used as a reference image (Figure 5a). The colours of the Ambarnaya River water do not attract objections and the surrounding vegetation is green. The image taken on 31 May 2020 (Figure 5b), i.e., two days after the accident, differs. The earth is barren, and the water colour is not blue, but grey and green. The image clearly shows the red spots of oil that seeped into the river. The image from 8 June 2020 (Figure 5c) shows an area that has undergone initial cleaning. The image still shows the pollution on the river (red oil stains appear in the surrounding troughs and streams). A satellite image from 13 June 2020 (Figure 5d) shows the Ambarnaya River devoid of visible pollution. Unfortunately, the red pixels in the channel and stream near the river can be seen as red spots of residual oil. This means that despite the response of the services, it was impossible to completely stop and clean up toxins from the environment. The exact location of the red leakage point is determined by classification. The results of the SVM classification are shown in Figure 6. Using the Sentinel-2 image to analyse the classification, there is a large number of red pixels representing oil spill-related substances. These are mainly concentrated around water reservoirs, i.e., rivers, beds, and streams. There is a tendency to subside at the edges of the reservoirs, where it can easily enter the soil from shallow waters. The results of surface water change detection in the area studied are alarming. The data (from Sentinel 2) taken for analysis cover the dates: 23 May 2020 (before the leak) and 10 July 2020 (after the leak). Figure 7 shows the changes that took place during this period. Red pixels indicate areas where the amount of water has decreased during this time. The white colour of the pixel means no change in the water level, and the blue pixel, which is not present on the map, would mean an increase in the water level. By analysing the image, it can be seen that the Ambarnaya River lost a lot of water during this time. This could be due to works related to area cleaning, which removes large amounts of contaminated water from the river bed. This significant decrease in water level could be linked to serious oil pollution. Figure 5: Visual Oil Spill Analysis (**a**) 10 May 2020, (**b**) 31 May 2020, (**c**) 8 June 2020, (**d**) 13 June 2020. Figure 6: SVM Classification results. Figure 7: Water change Detection. The results of mapping the oil spill with satellite images from Sentinel-1 missions are interesting (Figure 8a). The radar data have revealed more oil contamination in the area studied than by analysing Sentinel-2 images (Figure 8b). Not only is the Ambarnaya River contaminated, but also small reservoirs, basins, channels, and nearby streams. The leak detection results using Sentinel-2 data (Figure 8b) are slightly less accurate than the above-mentioned SAR methodology. However, they can visually evaluate the thickness of the oil detected. In Figure 8b, blue-colour denotes thick oil spills and green-blue spots represent thinner oil layers. Using Sentinel-1 radar data, the actual contamination state can be observed. The appropriate Sentinel-2 bands tell us about oil density and thickness. The combination of both methods gives more accurate information about the leak and allows us a better understanding of its scale. Figure 8: Oil Detection Results (**a**) from Sentinel 1 imagery (detected oil marked in red), (**b**) from Sentinel 2 imager (detected oil marked in blue and green-blue). The results of the analysis of water quality in the Norilsk region using the JRC Annual Water Classification History v1.3 data set are shown in Figure 9. Solid waters are blue, and the areas in which they do not exist are red. Seasonal water was marked with white pixels. The image analysis shows that water losses have increased significantly since 1984.