Determination of Sentinel-2 spectral reflectance to detect oil spill on the sea surface
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Published:
Dec 31, 2020
Keywords:
Sentinel-2B, Oil Pollution, Bands Combination, Oil Pattern, Oil Reflectance
Main Article Content
Abstract
Oil spill is one of the most common marine environmental problems. Oil spills can be caused by leakage at oil refineries at sea or disposal of vessel waste. This event has an impact on various sectors, such as fisheries, tourism, and marine ecosystems. This study aims to determine the spectral reflectance of Sentinel-2 response to detecting oil spill on the sea. Oil identification in the sea can be made visually by looking at colored patterns at sea level. Sentinel-2 image reflectance was obtained by processing the image using the Google Earth Engine platform. The results were clipped according to the area of ​​interest and divided to get a value between 0 and 1. Bands combination is possible to identify the oil spill visually. The silvery pattern saw in the red-green-blue combination, but it is arduous to estimate its distribution because of the silvery pattern seen for thick oil. The combination of SWIR-NIR-red bands proved effective in showing the distribution of oil with a deep black pattern. Spectral measurements in the field were undertaken by taking samples in the areas of oil spills and clean water bodies. The oil layer had a lower reflectance than the clean water body. The blue band gave a high response, but the red band gave less response. In the NIR and SWIR bands, the reflectance of oil was lower than the water body. In conclusion, the SWIR - NIR - RED band combination is better used to determine oil spills due to it shows the characteristics of oil generally, either thin or thick oil.
Article Details
How to Cite
Afgatiani, P. M., Putri, F. A., Suhadha, A. G., & Ibrahim, A. (2020). Determination of Sentinel-2 spectral reflectance to detect oil spill on the sea surface. Sustinere: Journal of Environment and Sustainability, 4(3), 144–154. https://doi.org/10.22515/sustinere.jes.v4i3.115
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References
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Prolab. (2020). ASD HandHeld 2: Hand-held VNIR Spectroradiometer.
Salberg, A. B., Rudjord, O., & Solberg, A. H. S. (2014). Oil spill detection in SAR images - A review. IEEE Transactions Oh Geoscience and Remote Sensing, 52(10), 0196–2892.
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Sudibjo, M., Siregar, V. P., & Gaol, J. L. (2013). Algoritma Untuk Deteksi Tumpahan Minyak Di Laut Timor Menggunakan Citra Modis. Jurnal Teknologi Perikanan Dan Kelautan, 4(1), 41–62.
Sulistyono. (2013). Dampak Tumpahan Minyak (Oil Spill) di Perairan Laut Pada Kegiatan Industri Migas dan Metode Penanggulangannya. Forum Teknologi, 3(1), 49–57.
Sulma, S., Insan, K., Rahmi, N., Febrianti, N., & Sitorus, J. (2019). Deteksi Tumpahan Minyak Menggunakan Metode Adaptive Threshold dan Analisis Tekstur pada Data SAR. Majalah Ilmiah Globe, 21(1), 45–52.
Tissot, B. P. & D. H. W. (1978). Petroleum Formation and Occurrence: A New Approach to Oil and Gas Exploration. New York: Springer-Verlag. http://doi.org/10.1017/S0016756800043685
Yolanda, F. (2019). Kerugian Petani Garam Akibat Tumpahan Minyak Rp 700 Juta.
Albert, O. N., Amaratunga, D., & Haigh, R. P. (2018). Evaluation of the Impacts of Oil Spill Disaster on Communities and Its Influence on Restiveness in Niger Delta, Nigeria. In Procedia Engineering (Vol. 212, pp. 1054–1061). Elsevier B.V. http://doi.org/10.1016/j.proeng.2018.01.136
Alpers, W., Holt, B., & Zeng, K. (2017). Oil spill detection by imaging radars: Challenges and pitfalls. Remote Sensing of Environment, 201(August), 133–147. http://doi.org/10.1016/j.rse.2017.09.002
Andreou, C., & Karathanassi, V. (2011). Endmember detection in marine environment with oil spill event. In Image and Signal Processing for Remote Sensing XVII (Vol. 8180, p. 81800P). http://doi.org/10.1117/12.898762
Chaturvedi, S. K., Banerjee, S., & Lele, S. (2019). An assessment of oil spill detection using Sentinel 1 SAR-C images. Journal of Ocean Engineering and Science. http://doi.org/10.1016/j.joes.2019.09.004
Chust, G., & Sagarminaga, Y. (2007). The multi-angle view of MISR detects oil slicks under sun glitter conditions. Remote Sensing of Environment, 107(1–2), 232–239. http://doi.org/10.1016/j.rse.2006.09.024
Dessì, F., Melis, M. T., Naitza, L., & Marini, A. (2008). MODIS data processing for coastal and marine environment monitoring: A study on anomaly detection and evolution in gulf of Cagliari (Sardinia-Italy). In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Congress Beijing 2008, Proceedings of Commission VIII (pp. 695–698). Beijing, China: ISPRS.
Elum, Z. A., Mopipi, K., & Henri-Ukoha, A. (2016). Oil exploitation and its socioeconomic effects on the Niger Delta region of Nigeria. Environmental Science and Pollution Research, 23(13), 12880–12889. http://doi.org/10.1007/s11356-016-6864-1
Fan, K., Zhang, Y., & Lin, H. (2010). Satellite SAR analysis and interpretation of oil spill in the offshore water of Hong Kong. Annals of GIS, 16(4), 269–275. http://doi.org/10.1080/19475683.2010.540259
Fingas, M., & Brown, C. (2014). Review of oil spill remote sensing. Marine Pollution Bulletin, 83(1), 9–23. http://doi.org/10.1016/j.marpolbul.2014.03.059
Fingas, M. F., & Brown, C. E. (1997). Review of oil spill remote sensing. Spill Science and Technology Bulletin, 4(4), 199–208. http://doi.org/10.1016/S1353-2561(98)00023-1
Garcia-Pineda, O., Staples, G., Jones, C. E., Hu, C., Holt, B., Kourafalou, V., … Haces-Garcia, F. (2020). Classification of oil spill by thicknesses using multiple remote sensors. Remote Sensing of Environment, 236(August 2019), 111421. http://doi.org/10.1016/j.rse.2019.111421
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. http://doi.org/10.1016/j.rse.2017.06.031
Iqbal, D. (2019). Rugi Segala Lini Akibat Tumpahan Minyak Pertamina. Retrieved from https://www.mongabay.co.id/2019/09/18/rugi-segala-lini-akibat-tumpahan-minyak-pertamina/
Jha, M. N., Levy, J., & Gao, Y. (2008). Advances in remote sensing for oil spill disaster management: State-of-the-art sensors technology for oil spill surveillance. Sensors, 8(1), 236–255. http://doi.org/10.3390/s8010236
Karlinasari, L., Sabed, M., Wistara, N. J., Purwanto, A., & Wijayanto, H. (2012). Karakteristik Spektra Absorbansi NIR (Near Infra Red) Spektroskopi Kayu Acacia mangium WILLD pada 3 Umur Berbeda. Jurnal Ilmu Kehutanan, 6(1), 45–52. http://doi.org/10.22146/jik.3310
Kolokoussis, P., & Karathanassi, V. (2018). Oil spill detection and mapping using sentinel 2 imagery. Journal of Marine Science and Engineering, 6(1), 4. http://doi.org/10.3390/jmse6010004
Lamrotua Sihombing, V., Nurweda Putra, I. D. N., & Arya Sasmita, G. M. (2018). Aplikasi Deteksi Tumpahan Minyak dengan Interpretasi Citra Satelit Landsat 8. Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), 6(3), 244. http://doi.org/10.24843/jim.2018.v06.i03.p11
Liu, B., Li, Y., Zhang, Q., & Han, L. (2016). Spectral Characteristics of Weathered Oil Films on Water Surface and Selection of Potential Sensitive Bands in Hyper-Spectral Images. Journal of the Indian Society of Remote Sensing, 45(1), 171–177. http://doi.org/10.1007/s12524-016-0568-3
Lu, Y. C., Fu, W. X., Tian, Q. J., Lyu, C. G., & Han, W. C. (2014). Using optical remote sensing model to estimate oil slick thickness based on satellite image. IOP Conference Series: Earth and Environmental Science, 17(1), 12122. http://doi.org/10.1088/1755-1315/17/1/012122
Misra, A., & Balaji, R. (2017). Simple Approaches to Oil Spill Detection Using Sentinel Application Platform (SNAP)-Ocean Application Tools and Texture Analysis: A Comparative Study. Journal of the Indian Society of Remote Sensing, 45(6), 1065–1075. http://doi.org/10.1007/s12524-016-0658-2
Ozigis, M. S., Kaduk, J. D., Jarvis, C. H., da Conceição Bispo, P., & Balzter, H. (2020). Detection of oil pollution impacts on vegetation using multifrequency SAR, multispectral images with fuzzy forest and random forest methods. Environmental Pollution, 256, 1–17. http://doi.org/10.1016/j.envpol.2019.113360
Prolab. (2020). ASD HandHeld 2: Hand-held VNIR Spectroradiometer.
Salberg, A. B., Rudjord, O., & Solberg, A. H. S. (2014). Oil spill detection in SAR images - A review. IEEE Transactions Oh Geoscience and Remote Sensing, 52(10), 0196–2892.
Setiani, P., & Ramdani, F. (2019). Oil spill mapping using multi-sensor Sentinel data in Balikpapan Bay, Indonesia. In 2018 4th International Symposium on Geoinformatics, ISyG 2018 (pp. 1–4). IEEE. http://doi.org/10.1109/ISYG.2018.8612057
Spies, R. B., Mukhtasor, M., & Burns, K. A. (2017). The Montara Oil Spill: A 2009 Well Blowout in the Timor Sea. Archives of Environmental Contamination and Toxicology, 73(1), 55–62. http://doi.org/10.1007/s00244-016-0356-7
Sudibjo, M., Siregar, V. P., & Gaol, J. L. (2013). Algoritma Untuk Deteksi Tumpahan Minyak Di Laut Timor Menggunakan Citra Modis. Jurnal Teknologi Perikanan Dan Kelautan, 4(1), 41–62.
Sulistyono. (2013). Dampak Tumpahan Minyak (Oil Spill) di Perairan Laut Pada Kegiatan Industri Migas dan Metode Penanggulangannya. Forum Teknologi, 3(1), 49–57.
Sulma, S., Insan, K., Rahmi, N., Febrianti, N., & Sitorus, J. (2019). Deteksi Tumpahan Minyak Menggunakan Metode Adaptive Threshold dan Analisis Tekstur pada Data SAR. Majalah Ilmiah Globe, 21(1), 45–52.
Tissot, B. P. & D. H. W. (1978). Petroleum Formation and Occurrence: A New Approach to Oil and Gas Exploration. New York: Springer-Verlag. http://doi.org/10.1017/S0016756800043685
Yolanda, F. (2019). Kerugian Petani Garam Akibat Tumpahan Minyak Rp 700 Juta.