Enhancing Oil Spill Detection and Response: An Overview of Satellite-based Observation Technologies and Their Impact

Muhammad Iqbal Habibie, Robby Arifandri, Zulfa Qonita, Pronika Kricella, Muhammad Hisyam Khoirudin

Abstract


Oil spills are a major environmental issue that requires prompt detection and effective response strategies. Remote sensing technologies have shown great potential in improving oil spill detection and management. This paper aims to review and compare various remote sensing techniques and models used for oil spill detection and response, with a focus on evaluating their effectiveness in preventing offshore oil spills. The study involves a comprehensive review of recent research on remote sensing methods, such as neural network-based detection, Synthetic Aperture Radar (SAR), and optical sensors, alongside oil spill response techniques. The paper also utilizes the Publish or Perish (PoP) tool to analyze scientific papers related to oil spill detection and response. The PoP tool was employed to examine citation metrics, methodologies, and trends from 187 studies, including 16 focused on remote sensing techniques, 21 on oil spill methods, and 47 on related concerns. Results indicate that neural network-based methods are effective in high-risk areas, while SAR-based detection is recommended for regions with high sea states or cloud cover. The study also finds that a combination of response techniques, such as containment booms and bioremediation, can significantly improve the effectiveness of oil spill management. Moreover, the integration of multi-sensor data and machine learning techniques shows promise in enhancing detection accuracy and reducing false positives. In conclusion, the paper highlights the need for improved sensor technologies and the integration of various detection and response methods to enhance oil spill management efforts. Future research should focus on refining these techniques and developing cost-effective solutions to enable more efficient and timely responses to oil spills.

Keywords


Oil Spill detection; Satellite-based Observation Technologies; Synthetic Aperture Radar (SAR); Geographic Information Systems (GIS); Environmental Impact Assessment; Machine Learning in Oil Spill Response

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References


H. Effendi, M. Mursalin, and S. Hariyadi, “Rapid Water Quality Assessment as a Quick Response of Oil Spill Incident in Coastal Area of Karawang, Indonesia,” Front. Environ. Sci., vol. 10, no. May, pp. 1–7, 2022, doi: 10.3389/fenvs.2022.757412.

M. I. Habibie, T. A. Pianto, and H. I. Akbar, “Classification of Ship type using Sentinel 1 Imagery,” Proc. - 2021 7th Asia-Pacific Conf. Synth. Aperture Radar, APSAR 2021, pp. 7–11, 2021, doi: 10.1109/APSAR52370.2021.9688488.

B. Van Ricardo Zalukhu, A. W. Wijayanto, and M. I. Habibie, “Marine Vessels Detection on Very High-Resolution Remote Sensing Optical Satellites using Object-Based Deep Learning,” Proceeding - IEEE Int. Conf. Commun. Networks Satell. COMNETSAT 2022, pp. 149–154, 2023, doi: 10.1109/COMNETSAT56033.2022.9994340.

M. Fingas and C. Brown, “Review of oil spill remote sensing,” Mar. Pollut. Bull., vol. 83, no. 1, pp. 9–23, 2014, doi: 10.1016/j.marpolbul.2014.03.059.

M. I. Habibie and N. Nurda, “Downscaling of vegetation indices from multi-satellite throughout-season maize,” IOP Conf. Ser. Earth Environ. Sci., vol. 1230, no. 1, 2023, doi: 10.1088/1755-1315/1230/1/012143.

M. I. Habibie and N. Nurda, “Estimation of the Indonesian drought based on phenology vegetation analysis of maize,” IOP Conf. Ser. Earth Environ. Sci., vol. 1230, no. 1, 2023, doi: 10.1088/1755-1315/1230/1/012144.

M. R. Haryayudhanto, M. I. Habibie, D. A. K. Sari, P. A. Aryaguna, and R. Y. Suryandari, “Green Open Space Assessment Using Vegetation Index Analysis (Case study: North Bekasi District),” 2022 IEEE Asia-Pacific Conf. Geosci. Electron. Remote Sens. Technol. Underst. Interact. Land, Ocean. Atmos. Smart City Disaster Mitig. Reg. Resilience, AGERS 2022 - Proceeding, pp. 94–98, 2022, doi: 10.1109/AGERS56232.2022.10093665.

R. Shofiyati, M. I. Habibie, M. Ardha, and B. Susanto, “A Multi-Index Satellite Data Investigation for Corn Growth Patterns Identification,” 2023 IEEE Asia-Pacific Conf. Geosci. Electron. Remote Sens. Technol. Glob. Challenges Geosci. Electron. Remote Sens. Futur. Dir. City, Land, Ocean Sustain. Dev. AGERS 2023, no. April, pp. 156–160, 2023, doi: 10.1109/AGERS61027.2023.10490925.

M. I. Habibie, N. Nurda, H. I. Akbar, O. Bibin Bintoro, R. Arifandri, and N. Ramadhana, “Real time monitoring fire detection Using Remote Sensing,” 2021 IEEE Asia-Pacific Conf. Geosci. Electron. Remote Sens. Technol. AGERS 2021 - Proceeding, pp. 28–32, 2021, doi: 10.1109/AGERS53903.2021.9617260.

I. Leifer et al., “State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill,” Remote Sens. Environ., vol. 124, pp. 185–209, 2012, doi: 10.1016/j.rse.2012.03.024.

V. Burkett, “Global climate change implications for coastal and offshore oil and gas development,” Energy Policy, vol. 39, no. 12, pp. 7719–7725, 2011, doi: 10.1016/j.enpol.2011.09.016.

M. Savonis, V. R. Burkett, and J. R. Potter, “Impacts of Climate Change and Variability on Transportation Systems and Infrastructure: Gulf Coast Study, Phase I A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research,” Systematics, no. March, p. 445pp, 2008.

M. I. Habibie et al., “Assessing Regional Precipitation Patterns Using Multiple Global Satellite-Based Datasets in the Upper Citarum Watershed, Indonesia,” J. Indian Soc. Remote Sens., no. July, 2024, doi: 10.1007/s12524-024-01952-9.

A. Misuri et al., “Technological accidents caused by floods: The case of the Saga prefecture oil spill, Japan 2019,” Int. J. Disaster Risk Reduct., vol. 66, no. December 2020, p. 102634, 2021, doi: 10.1016/j.ijdrr.2021.102634.

D. E. Dismukes and S. Narra, “Sea-Level Rise and Coastal Inundation: A Case Study of the Gulf Coast Energy Infrastructure,” Nat. Resour., vol. 09, no. 04, pp. 150–174, 2018, doi: 10.4236/nr.2018.94010.

C. M. Patricola and M. F. Wehner, “Anthropogenic influences on major tropical cyclone events,” Nature, vol. 563, no. 7731, pp. 339–346, 2018, doi: 10.1038/s41586-018-0673-2.

A. Abimanyu, W. S. Pranowo, I. Faizal, N. K. A. Afandi, and N. P. Purba, “Reconstruction of oil spill trajectory in the Java Sea, Indonesia using sar imagery,” Geogr. Environ. Sustain., vol. 14, no. 1, pp. 177–184, 2021, doi: 10.24057/2071-9388-2020-21.

M. Watts and A. Zalik, “Consistently unreliable: Oil spill data and transparency discourse,” Extr. Ind. Soc., vol. 7, no. 3, pp. 790–795, 2020, doi: 10.1016/j.exis.2020.04.009.

M. O. Soares, C. E. P. Teixeira, L. E. A. Bezerra, E. F. Rabelo, I. B. Castro, and R. M. Cavalcante, “The most extensive oil spill registered in tropical oceans (Brazil): the balance sheet of a disaster,” Environ. Sci. Pollut. Res., vol. 29, no. 13, pp. 19869–19877, 2022, doi: 10.1007/s11356-022-18710-4.

S. Tewari and A. Sirvaiya, “Oil spill remediation and its regulation,” Int. J. Eng. Res. Gen. Sci., vol. 1(6), no. October, pp. 1–7, 2015.

R. Prastyani and A. Basith, “Utilisation of Sentinel-1 SAR Imagery for Oil Spill Mapping: A Case Study of Balikpapan Bay Oil Spill,” JGISE J. Geospatial Inf. Sci. Eng., vol. 1, no. 1, pp. 22–26, 2018, doi: 10.22146/jgise.38533.

Godfried Junio Sebastian Matahelemual, A. B. Harto, and Tri Muji Susantoro, “Oil Spill Detection using Sentinel-1 Multitemporal Data in Offshore Karawang,” vol. 2020, pp. 1–21, 2019.

P. M. DiGiacomo, L. Washburn, B. Holt, and B. H. Jones, “Coastal pollution hazards in southern California observed by SAR imagery: Stormwater plumes, wastewater plumes, and natural hydrocarbon seeps,” Mar. Pollut. Bull., vol. 49, no. 11–12, pp. 1013–1024, 2004, doi: 10.1016/j.marpolbul.2004.07.016.

P. Carrera, J. H. Churnside, G. Boyra, V. Marques, C. Scalabrin, and A. Uriarte, “Comparison of airborne lidar with echosounders: a case study in the coastal Atlantic waters of southern Europe,” ICES J. Mar. Sci., vol. 63, no. 9, pp. 1736–1750, 2006, doi: 10.1016/j.icesjms.2006.07.004.

M. Reed et al., “Oil Spill Modeling an overview of the state of the art,” Spill Sci. Technol. Bull., vol. 5, no. 1, pp. 3–16, 1999.

A. K. Mishra and G. S. Kumar, “Weathering of Oil Spill: Modeling and Analysis,” Aquat. Procedia, vol. 4, no. Icwrcoe, pp. 435–442, 2015, doi: 10.1016/j.aqpro.2015.02.058.

H. A. Espedal and T. Wahl, “Satellite sar oil spill detection using wind history information,” Int. J. Remote Sens., vol. 20, no. 1, pp. 49–65, 1999, doi: 10.1080/014311699213596.

F. Del Frate, A. Petrocchi, J. Lichtenegger, and G. Calabresi, “Neural networks for oil spill detection using ERS-SAR data,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5, pp. 2282–2287, 2000, doi: 10.1109/36.868885.

R. Sunitha, R. S. Kumar, S. Member, A. T. Mathew, and S. Member, “Satellite Oil Spill Detection Using Artificial Neural Networks,” vol. 6, no. 6, pp. 1–8, 2013.

C. Brekke and A. H. S. Solberg, “Oil spill detection by satellite remote sensing,” Remote Sens. Environ., vol. 95, no. 1, pp. 1–13, 2005, doi: 10.1016/j.rse.2004.11.015.

A. H. S. Solberg, “Remote sensing of ocean oil-spill pollution,” Proc. IEEE, vol. 100, no. 10, pp. 2931–2945, 2012, doi: 10.1109/JPROC.2012.2196250.

M. Migliaccio, F. Nunziata, and A. Gambardella, “On the co-polarized phase difference for oil spill observation,” Int. J. Remote Sens., vol. 30, no. 6, pp. 1587–1602, 2009, doi: 10.1080/01431160802520741.

Y. Cheng, X. Li, Q. Xu, O. Garcia-Pineda, O. B. Andersen, and W. G. Pichel, “SAR observation and model tracking of an oil spill event in coastal waters,” Mar. Pollut. Bull., vol. 62, no. 2, pp. 350–363, 2011, doi: 10.1016/j.marpolbul.2010.10.005.

C. Praba Karana, R. S. Rengasamy, and D. Das, “Oil spill cleanup by structured fibre assembly,” Indian J. Fibre Text. Res., vol. 36, no. 2, pp. 190–200, 2011.

G. Alaa El-Din, A. A. Amer, G. Malsh, and M. Hussein, “Study on the use of banana peels for oil spill removal,” Alexandria Eng. J., vol. 57, no. 3, pp. 2061–2068, 2018, doi: 10.1016/j.aej.2017.05.020.

J. M. Bayona, C. Domínguez, and J. Albaigés, “Analytical developments for oil spill fingerprinting,” Trends Environ. Anal. Chem., vol. 5, pp. 26–34, 2015, doi: 10.1016/j.teac.2015.01.004.

Z. Jiao, G. Jia, and Y. Cai, “A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles,” Comput. Ind. Eng., vol. 135, no. December 2017, pp. 1300–1311, 2019, doi: 10.1016/j.cie.2018.11.008.

W. Guo, “Development of a statistical oil spill model for risk assessment,” Environ. Pollut., vol. 230, pp. 945–953, 2017, doi: 10.1016/j.envpol.2017.07.051.

J. R. Nelson and T. H. Grubesic, “Oil spill modeling: Risk, spatial vulnerability, and impact assessment,” Prog. Phys. Geogr., vol. 42, no. 1, pp. 112–127, 2018, doi: 10.1177/0309133317744737.

Z. Yang et al., “Decision support tools for oil spill response (OSR-DSTs): Approaches, challenges, and future research perspectives,” Mar. Pollut. Bull., vol. 167, no. April, p. 112313, 2021, doi: 10.1016/j.marpolbul.2021.112313.

M. Krestenitis, G. Orfanidis, K. Ioannidis, K. Avgerinakis, S. Vrochidis, and I. Kompatsiaris, “Oil spill identification from satellite images using deep neural networks,” Remote Sens., vol. 11, no. 15, pp. 1–22, 2019, doi: 10.3390/rs11151762.

E. Cakir, C. Sevgili, and R. Fiskin, “An analysis of severity of oil spill caused by vessel accidents,” Transp. Res. Part D Transp. Environ., vol. 90, no. December 2020, p. 102662, 2021, doi: 10.1016/j.trd.2020.102662.

P. Tysiąc, T. Strelets, and W. Tuszyńska, “The Application of Satellite Image Analysis in Oil Spill Detection,” Appl. Sci., vol. 12, no. 8, 2022, doi: 10.3390/app12084016.




DOI: http://dx.doi.org/10.12962/j25481479.v10i1.22455

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