Assessing the spatial distribution and impacts of recent oil spill along the Western Coast of Karachi, Pakistan. Issue 17 (2nd September 2022)

• Record Type: Journal Article
• Authors: Amin, Gomal; Shahzad, Muhammad Imran; Nazeer, Majid; Zia, Ibrahim; Jaweria, Sundas; Mehmood, Irfan
• Is Part Of: Geocarto international. Volume 37:Issue 17(2022)
• Page Start: 4911
• Page End: 4931
• Publication Date: 2022-09-02
• Subjects: Oil spill -- Satellite remote sensing -- Sentinel-1 -- Karachi coast; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1899307
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Oil spill is an environmental challenthat influences various aspects of coastal zones including humans, health, economy, and coastal ecosystems. The source of the oil spill can be both natural and anthropogenic. Oil spill response is dependent on the prompt detection of spillain open waters and at the coast. This study uses Sentinel-1 (S-1) C-band Synthetic Aperture Radar (SAR) data to detect oil spill that transpired at Karachi's coastline from 23 to 26 October 2018. The oil spill detection was conducted using the K-means clustering algorithm of S-1 SAR data that involves reduction of speckle. Meteorological data consisting of wind speed and direction from S-1 Level-2 Ocean product (L2 OCN) based on the European Centre for Medium-RanWeather Forecasts (ECMWF) was also utilized. Initially, two long streaks of bunker oil covering an approximate area of 9.45 km 2 was detected on 23 October 2018. Later oil spill spread over an area of ∼188.30 km 2 (∼20 times more) until 26 October 2018 along the Karachi shoreline. Spilled oil covered ∼20 times more of its first spread area at an alarming rate of ∼37.63 km 2 per day due to wind and wave action. Oil spill damaged the local ecology along the western coast of Karachi. Such damages can be avoided by installing an oil spill monitoring system using SAR and ground data which will support both environment and economy.