Mapping the extent of Oil Spill in Mauritius using Remote Sensing
Updated: Jun 13
MV Wakashio, a bulk carrier owned by Japanese shipping company Nagashiki, was on its way to Brazil from China where it had delivered its consignment of iron-ore. It was on standby mode, waiting for orders, when it collided with a 'reef' off the south east coast of Mauritius on 25th July 2020. It was carrying 4000 tonnes of fuel and the crash resulted in leakage of around 1000 tonnes - 7000 barrels equivalent.
The image below depicts the extent of oil spill, as extracted from Sentinel SAR satellite imagery as on 10th August 2020.
Much thanks to RUS Copernicus for the training data. Sentinel-1B SAR imagery has a 5 m by 20 m spatial resolution. The results are indicative in nature and for informational purposes only.
Mauritius, by virtue of being a small tourism-centric nation, didn't have adequate resources to contain the oil spill. Its pristine beaches, coral reefs and turquoise lagoons were under severe threat of irreversible damage and, as a result, the government declared a 'state of environmental emergency' and sought help from France, in particular, (its neighboring island Réunion is an overseas French territory) to deal with this unprecedented crisis.
The good news is that 3000 tonnes (out of 4000) of fuel was pumped out of the ship reservoir before it could have leaked into the ocean. (Note: The ship broke into two a few days later and a major crisis was averted). However, the devastation caused by the previous oil spill was still significant. Marine creatures were dying in numbers, oil vapors were causing discomfort to nearby residents and the Mauritian economy would suffer considerably due to the adverse impact this incident would have on tourism in this region.
Video 1: Extent of Mauritius Oil Spill. Source: The National, 8th August 2020
It is important to note that satellite imagery analysis is 'indicative' in nature i.e. it is influenced by imagery source and imagery analysis parameters.
For example, Finnish micro-satellite ICEYE shared this imagery analysis output (below) on 11th August - it shows a greater area affected by oil spill than my finding. This can be due to the fact that ICEYE imagery is more suited - specially calibrated - for oil spill detection than Sentinel SAR imagery is, which I have used as my raw imagery product. Moreover, it is safe to assume that their scientific researchers would be using more finely-tuned analysis parameters than what I have used.
Compared to the Gulf Oil spill (Deepwater Horizon) where 3.19 million oil barrels were leaked, this incident was much smaller in magnitude. However, there has been an increase in oil pollution related incidents in ecologically sensitive regions recently - in Mauritius as well as at other locations (notably in the Arctic Ocean) and this calls for much contemplation about the shipping routes used to transport fuel, the need to use alternate fuels, early warning and mitigation systems, and other such measures to deal with such an eventuality. Can't help but feel that the year 2020 is Nature's way to send us a strong reminder to mend our relationship with the environment before it is too late.
You can read my other satellite imagery processing work from these hyperlinks- 1) Forest Fires in Uttarakhand, 2) Flooding Aftermath of Amphan Cyclone, 3) Land Subsidence in Kolkata, 4) Urban Heat Index in Mumbai, 5) Ship Detection in Gulf of Kutch & in Suez Canal, 6) Air Pollution in India, 7) Estimating Evotranspiration in Punjab, 8) Drought Monitoring at Indirasagar Reservoir, 9) Deformation Monitoring at 3 Volcanoes, 10) Landcover Mapping in Berlin using Hyperspectral Imaging, 11) Snow Cover Mapping in Himachal Pradesh, 12) Sargassum Invasion in Caribbean Sea, 13) Chamoli Disaster Source Identification, 14) Mapping Water Bodies, 15) Determining Damage caused by the Explosion in Beirut, 16) Measuring Urbanization Growth of 2 Indian cities, 17) Random Forest Algorithm to Classify Deforestation, 18) Mapping Rice Fields 19) Landslide Deformation Monitoring in Iceland & 20) Extracting Infrastructure Objects using Deep Learning.
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