Analisis Tumpahan Minyak di Laut Menggunakan Pemodelan Random Forest Berbasis Data Sentinel-1 SAR
Article Sidebar
Main Article Content
Abstract
Tumpahan minyak di laut berdampak serius terhadap ekosistem pesisir, perikanan, dan keselamatan pelayaran. Penginderaan jauh Synthetic Aperture Radar (SAR) efektif digunakan untuk pemantauan karena tidak terpengaruh awan dan cahaya. Penelitian ini bertujuan mengembangkan metode klasifikasi tumpahan minyak menggunakan citra Sentinel-1 Single Polarization (VV) dengan mengintegrasikan fitur statistik lokal dan algoritma Random Forest (200 pohon keputusan). Fitur yang diekstraksi meliputi nilai backscatter VV, Local Mean, dan Local Standard Deviation (jendela 5×5 piksel). Model dilatih menggunakan 400.000 sampel kelas minyak dan non-minyak serta dievaluasi melalui confusion matrix, precision, recall, F1-score, dan overall accuracy. Hasil menunjukkan integrasi fitur statistik lokal meningkatkan akurasi dari 84,0% menjadi 91,52%. Nilai precision mencapai 0,93 (non-minyak) dan 0,90 (minyak), sedangkan recall masing-masing sebesar 0,89 dan 0,94. Model mengidentifikasi luasan tumpahan minyak sebesar 196,94 km² (1.969.436 piksel). Meskipun masih terdapat kecenderungan over-estimation, metode ini terbukti efektif meningkatkan akurasi klasifikasi dan berpotensi diterapkan untuk pemantauan pencemaran laut secara otomatis dan cepat.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
[1] 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.
[2] A. H. S. Solberg, C. Brekke, and P. O. Husøy, “Oil spill detection in Radarsat and Envisat SAR images,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 3, pp. 746–754, 2007, doi: 10.1109/TGRS.2006.887019.
[3] H. A. Harahsheh, “Oil spill detection and monitoring of Abu Dhabi coastal zone using KOMPSAT-5 SAR imagery,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 41, no. July, pp. 1115–1121, 2016, doi: 10.5194/isprsarchives-XLI-B8-1115-2016.
[4] F. M. Bianchi, M. M. Espeseth, and N. Borch, “Large-scale detection and categorization of oil spills from sar images with deep learning,” Remote Sens., vol. 12, no. 14, pp. 7–11, 2020, doi: 10.3390/rs12142260.
[5] 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.
[6] J. S. Murugan, K. Ramkumar, P. R. Kshirsagar, and T. K. Tak, “Satellite-based oil spill detection using an explainable ViR-SC hybrid deep learning ensemble for improved accuracy and transparency,” Sci. Rep., vol. 16, no. 1, pp. 1–29, 2026, doi: 10.1038/s41598-026-37081-1.
[7] Y. Yang, S. Singha, and R. Goldman, “A near real-time automated oil spill detection and early warning system using Sentinel-1 SAR imagery for the Southeastern Mediterranean Sea,” Int. J. Remote Sens., vol. 45, no. 6, pp. 1997–2027, 2027, doi: 10.1080/01431161.2024.2321468.
[8] M. I. Habibie et al., A comparative study of fully automatic and semi ‑ automatic methods for oil spill detection using Sentinel ‑ 1 data. Springer International Publishing, 2025.
[9] M. Gade, W. Alpers, H. Hühnerfuss, V. R. Wismann, and P. A. Lange, “On the reduction of the radar backscatter by oceanic surface films: Scatterometer measurements and their theoretical interpretation,” Remote Sens. Environ., vol. 66, no. 1, pp. 52–70, 1998, doi: 10.1016/S0034-4257(98)00034-0.
[10] P. Singh and R. Shree, “Speckle noise: Modelling and implementation,” Int. J. Control Theory Appl., vol. 9, no. 17, pp. 8717–8727, 2016.
[11] A. A. Putra, D. Oktaviani, R. R. N. Syifa, S. A. Aliyan, and A. Fadhilah, “Deteksi Tumpahan Minyak Menggunakan Sentinel-1A Synthetic Aperture Radar dan Adaptive Threshold di Perairan Lhokseumawe,” J. Geogr. Sci. Educ., vol. 4, no. 2, pp. 162–172, 2026, doi: 10.69606/geography.v4i2.518.
[12] M. Mansourpour, M. A. Rajabi, and J. A. R. Blais, “Performance of Speckle Noise Reduction Filters on Active Radar and SAR Images,” Int. J. Technol. Eng. Syst., vol. 2, no. 1, pp. 111–114, 2011.
[13] H. Choi and J. Jeong, “Speckle noise reduction technique for sar images using statistical characteristics of speckle noise and discrete wavelet transform,” Remote Sens., vol. 11, no. 10, 2019, doi: 10.3390/rs11101184.
[14] Z. Yu, W. Wang, C. Li, W. Liu, and J. Yang, “Speckle noise suppression in SAR images using a three-step algorithm,” Sensors (Switzerland), vol. 18, no. 11, 2018, doi: 10.3390/s18113643.
[15] F. Nunziata, A. Gambardella, and M. Migliaccio, “On the degree of polarization for SAR sea oil slick observation,” ISPRS J. Photogramm. Remote Sens., vol. 78, pp. 41–49, 2013, doi: 10.1016/j.isprsjprs.2012.12.007.
[16] N. Pinel, C. Bourlier, I. Sergievskaya, N. Longépé, and G. Hajduch, “Asymptotic Modeling of Three-Dimensional Radar Backscattering from Oil Slicks on Sea Surfaces,” Remote Sens., vol. 14, no. 4, pp. 1–27, 2022, doi: 10.3390/rs14040981.
[17] S. A. Ermakov et al., “New Features of Bragg and Non-Polarized Radar Backscattering from Film Slicks on the Sea Surface,” J. Mar. Sci. Eng., vol. 10, no. 9, 2022, doi: 10.3390/jmse10091262.
[18] H. Zheng, J. Zhang, A. Khenchaf, and X. M. Li, “Study on non‐bragg microwave backscattering from sea surface covered with and without oil film at moderate incidence angles,” Remote Sens., vol. 13, no. 13, 2021, doi: 10.3390/rs13132443.
[19] H. Zheng, J. Zhang, Y. Zhang, A. Khenchaf, and Y. Wang, “Theoretical Study on Microwave Scattering Mechanisms of Sea Surfaces Covered with and without Oil Film for Incidence Angle Smaller Than 30°,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 1, pp. 37–46, 2021, doi: 10.1109/TGRS.2020.2993861.
[20] M. R. A. Conceição et al., “Sar oil spill detection system through random forest classifiers,” Remote Sens., vol. 13, no. 11, 2021, doi: 10.3390/rs13112044.