Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine

Abstract

In this study, a methodology has been developed for the detection of mucilage with the help of remote sensing (UA) techniques by considering the current mucilage formation in the Sea of Marmara. For this purpose, mucilage formation from10.03.2021 to 06.06.2021 was determined by classification of Sentinel-2 (MSI) satellite images using Random Forest (RF) algorithm on Google Earth Engine (GEE) platform. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index (AWEI) indexes were used for classification. In the classification study, 5 different date ranges were determined by considering the availability of satellite images and cloud ratio. In the first date range (10.03.2021-30.03.2021), the first mucilage image was detected in the Dardanelles Strait. In the following dates, the spread of mucilage towards the Gulf of Izmit and the Gulf of Gemlik in addition to the Dardanelles was determined. Finally, in the images dated between 17.05.2021-06.06.2021, it was seen that the density of mucilage increased in the Dardanelles Strait, Izmit Gulf, Gemlik Gulf, Erdek Kapıdağ Peninsula and the north of the Marmara Island. The area covered by mucilage as of the last date range was calculated as 12,741.94 ha, and this value shows that 1.07% of the Sea of Marmara is covered with mucilage. With this developed methodology, it has been seen that mucilage formation can be detected quickly within minutes and with high accuracy from satellite images anywhere in the world.

Keywords

• Marmara Sea
• Mucilage
• Google Earth Engine
• Remote Sensing

References

1. Artüz, L. M. (2002). Marmara ve Boğazların Ekolojisi ve Değişimler. B.Ü.Deniz Teknolojisi Sempozyumu, February.
2. Artüz, M. L., Okay, I. A., Mater, B., Artüz, O. B., Gürseler, G., & Okay, N. (2007). Bilimsel Açıdan Marmara Denizi. Istanbul: Union of Turkish Bar Associations Publication.
3. Ateş, A. M., Yilmaz, O. S., & Gülgen, F. (2020). Using remote sensing to calculate fl oating photovoltaic technical potential of a dam ’ s surface. Sustainable Energy Technologies and Assessments, 41(July), 100799. https://doi.org/10.1016/j.seta.2020.100799
4. Balkıs-ozdelıce, N., Durmuş, T., & Balcı, M. (2021). A Preliminary Study on the Intense Pelagic and Benthic Mucilage Phenomenon Observed in the Sea of Marmara. International Journal of Environment and Geoinformatics (IJEGEO), 8(4).
5. Bi, L., Fu, B. L., Lou, P. Q., & Tang, T. Y. (2020). Delineation water of pearl river basin using Landsat images from Google Earth Engine. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(3/W10), 5–10. https://doi.org/10.5194/isprs-archives-XLII-3-W10-5-2020
6. Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
7. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
8. Cohen, J. (1960). Kappa: Coefficient of concordance. Educ Psych Measurement, 20(37).

9. Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029
10. 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.
11. Haque, M. I., & Basak, R. (2017). Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. Egyptian Journal of Remote Sensing and Space Science, 20(2), 251–263. https://doi.org/10.1016/j.ejrs.2016.12.003
12. Huang, W., DeVries, B., Huang, C., Lang, M. W., Jones, J. W., Creed, I. F., & Carroll, M. L. (2018). Automated extraction of surface water extent from Sentinel-1 data. Remote Sensing, 10(5), 1–18. https://doi.org/10.3390/rs10050797
13. Jena, R., Pradhan, B., Jung, H., Rai, A. K., & Rizeei, H. M. (2020). Seasonal water change assessment at Mahanadi River, India using multi-temporal data in Google earth engine. Korean Journal of Remote Sensing, 36(1), 1–13.
14. Martinez, E. M. (2003). Remote Sensing Techniques for Land Use Classification of Rio Jauca Watershed Using Ikonos Images. 1–5.
15. McFeeters. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
16. Nguyen, U. N. T., Pham, L. T. H., & Dang, T. D. (2019). An automatic water detection approach using Landsat 8 OLI and Google Earth Engine cloud computing to map lakes and reservoirs in New Zealand. Environmental Monitoring and Assessment, 191(4), 1–12. https://doi.org/10.1007/s10661-019-7355-x
17. Özalp, H. B. (2021). First massive mucilage event observed in deep waters of Çanakkale Strait ( Dardanelles ), Turkey. J. Black Sea/Mediterranean Environment, 27(1), 49–66.
18. Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
19. Qiao, C., Luo, J., Sheng, Y., Shen, Z., Zhu, Z., & Ming, D. (2012). An Adaptive Water Extraction Method from Remote Sensing Image Based on NDWI. Journal of the Indian Society of Remote Sensing, 40(3), 421–433. https://doi.org/10.1007/s12524-011-0162-7
20. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with Erts. NASA Spec, 351, 309.
21. Savun-hekimoğlu, B., & Gazioğlu, C. (2021). Mucilage Problem in the Semi-Enclosed Seas : Recent Outbreak in the Sea of Marmara. International Journal of Environment and Geoinformatics (IJEGEO), 8(4). https://doi.org/10.30897/ijegeo.955739
22. Schiaparelli, S., Castellano, M., Povero, P., Sartoni, G., & Cattaneo‐Vietti, R. (2007). A benthic mucilage event in North‐Western Mediterranean Sea and its possible relationships with the summer 2003 European heatwave: short term effects on littoral rocky assemblages. Marine Ecology, 28(3), 341–353.
23. Tufekçi, V., Balkis, N., Polat Beken, Ç., Ediger, D., & Mantıkçı, M. (2010). Phytoplankton composition and environmental conditions of a mucilage event in the Sea of Marmara. Turkish Journal of Biology, 34(2), 199–210. https://doi.org/10.3906/biy-0812-1
24. Wang, C., Jia, M., Chen, N., & Wang, W. (2018). Long-term surface water dynamics analysis based on landsat imagery and the Google Earth Engine Platform: A case study in the middle Yangtze River Basin. Remote Sensing, 10(10), 1635. https://doi.org/10.3390/rs10101635
25. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
26. Yang, X., Qin, Q., Grussenmeyer, P., & Koehl, M. (2018). Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sensing of Environment, 219, 259–270. https://doi.org/10.1016/j.rse.2018.09.016
27. Yılmaz, O. S., Oruç, M. S., Ateş, A. M., & Gülgen, F. (2021). Orman Yangın Şiddetinin Google Earth Engine ve Coğrafi Bilgi Sistemleri Kullanarak Analizi: Hatay-Belen Örneği. Journal of the Institute of Science and Technology, 11(2), 1519–1532. https://doi.org/10.21597/jist.817900
28. Zibordi, G., & Hooker, S. B. (2000). Marine optical measurements of a mucilage event in the northern Adriatic Sea. 45(2), 322–327.