Using multiple satellite observations to quantitatively assess and model oil pollution and predict risks and consequences to shoreline from oil platforms in the Caspian Sea. Issue 6 (18th August 2018)

• Record Type: Journal Article
• Authors: Bayramov, Emil; Knee, Karen; Kada, Martin; Buchroithner, Manfred
• Is Part Of: Human & ecological risk assessment. Volume 24:Issue 6(2018)
• Page Start: 1501
• Page End: 1514
• Publication Date: 2018-08-18
• Subjects: GIS -- remote sensing -- oil and gas -- oil pollution; Health risk assessment -- Periodicals; Ecological risk assessment -- Periodicals; Ecology -- Periodicals; Risk Assessment -- Periodicals; 363.102
• DOI: 10.1080/10807039.2017.1416454
• Languages: English
• ISSNs: 1080-7039

Abstract

ABSTRACT: This research focused on the following objectives: (1) using satellite data to characterize the spatiotemporal distribution of anthropogenic oil spills from Oil Rocks Settlement, Chilov and Pirallahi Islands (2) stochastic modeling of the oil spill risk pose to water quality and shoreline ecosystems, and (3) validating model predictions using satellite images. 165 satellite images acquired by SENTINEL-1A, LANDSAT-8, RADARSAT, ENVISAT and ERS sensors between 1996 and 2015 were used for the detection of oil spills using object-based classification and visual interpretation. Anthropogenic hotspots were observed at three oldest oil production sites with estimated oil spilling up to 1264 m 3 per day and different degrees of temporal repetition of oil spills. The largest area (5639 km 2 ) experienced 1–10 detected oil spills, while 993 km 2 experienced 11–20 oil spills, 775 km 2 experienced 21–50 oil spills, 208 km 2 experienced 51–100 oil spills, and 36 km 2 experienced 101–150 oil spills. The majority (83% or 6157 km 2 ) of sea surface area within the combined boundary of detected oil spills (7422 km 2 ) had a 50% or greater chance of oil spill contamination. Approximately, 6% (44 km of 751 km) of shoreline had a 50% or greater probability of contamination with land use classes sensitive to pollution.