Deteksi Tumpahan Minyak Menggunakan Sentinel-1A Synthetic Aperture Radar dan Adaptive Threshold di Perairan Lhokseumawe

Alvin Anugrah Putra; Dela Oktaviani; Rizqia Rahmah Nurul Syifa; Silmi Afina Aliyan; Achmad Fadhilah

doi:10.69606/geography.v4i2.518

Authors

Alvin Anugrah Putra Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia, Kota Bandung, Jawa Barat, Indonesia
Dela Oktaviani Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia, Kota Bandung, Jawa Barat, Indonesia
Rizqia Rahmah Nurul Syifa Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia, Kota Bandung, Jawa Barat, Indonesia https://orcid.org/0009-0001-7004-3773
Silmi Afina Aliyan Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia, Kota Bandung, Jawa Barat, Indonesia https://orcid.org/0000-0003-2580-9837
Achmad Fadhilah Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia, Kota Bandung, Jawa Barat, Indonesia https://orcid.org/0009-0007-4434-4525

DOI:

https://doi.org/10.69606/geography.v4i2.518

Keywords:

oil spill, Sentinel-1A SAR, coastal waters

Abstract

Oil spills represent a major form of marine pollution that poses serious threats to coastal ecosystems and human activities. This study aims to detect and analyse the spatial distribution of the oil spill in Lhokseumawe waters using Sentinel-1A Synthetic Aperture Radar (SAR) imagery with software SNAP. The dataset used consists of Sentinel-1A SAR imagery acquired on 24 April 2022 with Interferometric Wide Swath mode and VV polarisation. Oil spill detection was performed using an adaptive thresholding approach through the Oil Spill Detection Tool. The oil spill was identified as a dark area with low backscatter values forming elongated and fragmented patterns influenced by ocean currents and wind conditions. Variations in the threshold shift values produced different estimated extents of the oil spill, namely 36.86 km² at the 1.0 dB threshold, 12.82 km² at the 2.0 dB threshold, and 4.61 km² at the 3.0 dB threshold. Although potential false detections may occur due to look-alike phenomena, this study demonstrates that Sentinel-1A SAR imagery is effective for monitoring oil spills in coastal waters.

References

Annisa, N. A., Vionica, P., & Kamal, U. (2024). Analisis Dampak Lingkungan Wilayah Pesisir Akibat Tumpahan Oil Spill di Karawang. Media Hukum Indonesia (MHI), 2(2), 192-196. https://doi.org/10.5281/zenodo.11261952

Asmunda, A. (2022, April 24). Laut Lhokseumawe Terancam Tercemar Akibat Kapal Angkut BBM Bocor. MASAKINI.CO. Diakses dari https://masakini.co/2022/04/25/laut-lhokseumawe-terancam-tercemar-akibat-kapal-angkut-bbm-bocor/

Astuti, A. D., & Titah, H. S. (2021). Studi Fitoremediasi Polutan Minyak Bumi di Wilayah Pesisir Tercemar Menggunakan Tumbuhan Mangrove (Studi Kasus: Tumpahan Minyak Mentah Sumur YYA-1 Pesisir Karawang Jawa Barat). Jurnal Teknik ITS, 9(2), F111-F116.

BBC News. (2018, April 4). Polisi: Tumpahan Minyak di Teluk Balikpapan Berasal dari Pipa Pertamina. Diakses dari https://www.bbc.com/indonesia/indonesia-43640595

Beyer, J., Trannum, H. C., Bakke, T., Hodson, P. V., & Collier, T. K. (2016). Environmental Effects of the Deepwater Horizon Oil Spill: A Review. Marine pollution bulletin, 110(1), 28-51. https://doi.org/10.1016/j.marpolbul.2016.06.027

Chen, R., Li, B., Jia, B., Xu, J., Ma, L., Yang, H., & Wang, H. (2022). Oil Spill Identification in X-band Marine Radar Image Using K-Means and Texture Feature. PeerJ Computer Science, 8, e1133. https://doi.org/10.7717/peerj-cs.1133

Damayanti, F. N., Putra, I. D. N. N., Nuarsa, I. W., & Hartuti, M. (2022). Deteksi Pola Sebaran Tumpahan Minyak (Oil Spill) Menggunakan Citra Sentinel-1A di Perairan Karawang. Journal of Marine and Aquatic Sciences, 8(2), 210-220. https://doi.org/10.24843/jmas.2022.v08.i02.p06

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., & Thépaut, J.-N. (2023). ERA5 Hourly Data on Single Levels from 1940 to Present. Copernicus Climate Change Service Climate Data Store.

Jafarzadeh, H., Mahdianpari, M., Homayouni, S., Mohammadimanesh, F., & Dabboor, M. (2021). Oil Spill Detection from Earth Observation Synthetic Aperture Radar: A Comprehensive Review and Meta-Analysis. GIScience & Remote Sensing, 58(7), 1022–1051. https://doi.org/10.1080/15481603.2021.1952542

Lentini, C. A. D., Mendonça, L. F. F. D., Conceicao, M. R. A., Lima, A. T., Vasconcelos, R. N. D., & Porsani, M. J. (2022). Comparison Between Oil Spill Images and Look-Alikes: An Evaluation of SAR-derived Observations of the 2019 oil Spill Incident Along Brazilian Waters. Anais da Academia Brasileira de Ciências, 94(suppl 2), e20211207. https://doi.org/10.1590/0001-3765202220211207

Marghany, M. (2014). Utilization of a Genetic Algorithm for the Automatic Detection of Oil Spill from RADARSAT-2 SAR Satellite Data. Marine Pollution Bulletin, 89(1-2), 20-29. https://doi.org/10.1016/j.marpolbul.2014.10.041

Meyer, F. J. (2019). Spaceborne Synthetic Aperture Radar: Principles, Data Access, and Basic Processing Techniques. In M. T. Banik, M. Santoro, & L. T. Dinh (Eds.), Synthetic Aperture Radar (SAR) Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation (pp. 21–64). National Aeronautics and Space Administration.

Prastyani, R., & Basith, A. (2019). Deteksi Tumpahan Minyak di Selat Makassar dengan Penginderaan Jauh Sensor Aktif dan Pasif. Elipsoida: Jurnal Geodesi dan Geomatika, 2(01), 88-94. https://doi.org/10.14710/elipsoida.2019.4864

Saifudin, D., Subardi, A., & J, S. R. A. (2020). Penyebab dan Upaya Penanganan Tumpahan Minyak pada Kegiatan Bunker di atas Kapal LPG/C Decora. Jurnal Sains dan Teknologi Maritim 21(1), 40-46. https://doi.org/10.33556/jstm.v21i1.258.

Suhendi, B. A. P., & Marsisno, W. (2025). Integrasi Citra Satelit Radar dan Data AIS untuk Monitoring Tumpahan Minyak dengan Pendekatan Machine Learning. Seminar Nasional Official Statistics, 2025(1), 193–202.

Topouzelis, K. N. (2008). Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms. Sensors, 8(10), 6642-6659. https://doi.org/10.3390/s8106642

Xiong, X., & Butler, J. (2018). 1.01 – Volume 1 Overview. In Comprehensive Remote Sensing (Vol. 1, pp. 1–6). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.10311-2

Xu, J., Song, B., Yang, X., & Nan, X. (2020). An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation. Remote Sensing, 12(23), 3857. https://doi.org/10.3390/rs12233857