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A Principal Component Analysis Methodology of Oil Spill Detection and Monitoring Using Satellite Remote Sensing Sensors
Department of Mining Engineering, Cukurova University, Adana, Turkey.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, Rome, Italy.
Department of Planning, Design, Technology of Architecture, Sapienza University of Rome, Design, Italy.
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2023 (English)In: Remote Sensing, E-ISSN 2072-4292, Vol. 15, no 5, article id 1460Article in journal (Refereed) Published
Abstract [en]

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results.

Place, publisher, year, edition, pages
MDPI , 2023. Vol. 15, no 5, article id 1460
Keywords [en]
image processing, Landsat-8, oil spill, Sentinel-1, Sentinel-2, synthetic aperture radar (SAR), Geometrical optics, High pass filters, Image analysis, Image enhancement, Marine pollution, Oil spills, Optical data processing, Optical remote sensing, Principal component analysis, Radar imaging, Seawater, Space-based radar, Surface waters, Synthetic aperture radar, Wind, Images processing, LANDSAT, Oil spill detection, Principal-component analysis, Remote sensing images, Satellite remote sensing
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:mdh:diva-62114DOI: 10.3390/rs15051460ISI: 000947909600001Scopus ID: 2-s2.0-85149967457OAI: oai:DiVA.org:mdh-62114DiVA, id: diva2:1745185
Available from: 2023-03-22 Created: 2023-03-22 Last updated: 2023-08-28Bibliographically approved

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Majidi Nezhad, Meysam

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