The Use of Object-Based Image Analysis for Monitoring 2021 Marine Mucilage Bloom in the Sea of Marmara

Year 2021, Volume: 8 Issue: 4, 529 - 536, 15.12.2021

Taşkın Kavzoğlu , Hasan Tonbul , İsmail Çölkesen , Umut Gunes Sefercik

https://doi.org/10.30897/ijegeo.990875

Cited By: 12

Abstract

Global warming threatens ecosystems through rising temperatures, increasing sea levels, drought, and extreme weather conditions. The natural balance of seas and oceans is also at stake with recent outbreaks of mucilage events all over the world. The mucilage phenomenon, which has been frequently observed in the Adriatic and Tyrrhenian seas, has taken place the second time in the Sea of Marmara in Spring 2021. The Sea of Marmara dividing the Asian and European parts of Turkey is an important inland sea with heavy maritime traffic, hosting many industrial zones and surrounded by highly populated cities. This study aims to determine the mucilage formations that were observed intensely all around the Sea of Marmara, focusing on the coasts of Istanbul, Kocaeli, Yalova, and Bursa through classifying Sentinel-2A images dated 19 and 24 May 2021, when the peak period of mucilage bloom, using a new paradigm of object-based image analysis (OBIA) approach. To create representative and homogenous image objects, multi-resolution segmentation was applied, and its result was inputted into a classification process using Random Forest (RF) classifier to generate thematic maps. The produced results were compared with pixel-based classification and a high correlation was estimated. Object-based classification was found effective for the determination of mucilage-covered areas (> 90% overall accuracy) for both considered dates. More specifically, areas covered with mucilage aggregates were computed as 56.15 km² and 67.51 km² for 19 May and 24 May 2021, respectively, indicating rapid growth in only 5-day period. The resulting thematic maps revealed that mucilage was heavily distributed in the gulfs of Gemlik and Izmit and along the coasts of Darica, Tuzla and Pendik.

Keywords

Mucilage, OBIA, Random Forest, Segmentation, Sentinel-2A

References

• Addink, E.A., de Jong, S.M., Pebesma, E.J. (2007). The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogrammetric Engineering and Remote Sensing, 72(8), 905-912.
• Aktan, Y., Dede, A., Ciftci, P.S. (2008). Mucilage event associated with diatoms and dinoflagellates in Sea of Marmara, Turkey. Harmful Algae News, 36, 1-3.
• Azam, F., Fonda-Umani, S., Funari, E. (1999). Significance of bacteria in the mucilage phenomenon in the northern Adriatic Sea. Ann Ist Super Sanita, 35(3), 411-9. PMID: 10721207.
• Baatz, M., Schape, A. (2000). Multiresolution segmentation – An optimization approach for high quality multi-scale image segmentation. In: Strobl J. et al. (Eds.), Angewandte Geographische Informationsverarbeitung (pp. 12-23), Herbert Wichmann Verlag.
• Belgiu, M., Drǎguţ, L., Strobl, J. (2014). Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using Worldview-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 205-215.
• Berthon, J.F., Zibordi, G. (2000). Marine optical measurements of a mucilage event in the northern Adriatic Sea. Limnology and Oceanography, 45(2), 322-327.
• Bianchi, G. (1746). Notizie sulla vasta fioritura algale del 1729. Raccolta d’opuscoli scientifici e filologici, 34, 256-257.
• Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16.
• Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R.Q., vander Meer, F., van der Werff, H., van Coillie, F., Tiede, D. (2014). Geographic object-based image analysis towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191.
• Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
• Buzzelli, E., Gianna, R., Marchori, E., Bruno, M. (1997). Influence of nutrient factors on production of mucilage by Amphora coffeaeformis var. perpusilla. Continental Shelf Research, 17, 1171-1180.
• Castilla, G., Hay, G.J. (2008). Image objects and geographic objects. In: Blaschke T., Lang S., Hay G.J. (Eds.), Object-based image analysis – spatial concepts for knowledge-driven remote sensing applications (pp. 91-110), Springer, Heidelberg, Berlin, New York.
• Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J. (2001). Color image segmentation: advances and prospects. Pattern Recognition, 34(12), 2259-2281.
• Colkesen, I., Kavzoglu, T. (2017). Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using Sentinel-2 and Landsat OLI imagery. Remote Sensing Letters, 8, 1082-1091.
• Cozzi, S., Ivancic, I., Catalano, G., Djakovac, T., Degobbis, D. (2004). Dynamics of the oceanography properties during mucilage appearance in the Northern Adriatic Sea: Analysis of the 1977 event in comparison to earlier events. Journal of Marine Systems, 50, 223-241.
• Danovaro, R., Fonda-Umani, S., Pusceddu, A. (2009). Climate change and the potential spreading of marine mucilage and microbial pathogens in the Mediterranean Sea. PloS One. 4(9), e7006.
• Deserti, M., Cacciamani, C., Chiggiato, J., Rinaldi, A., Ferrari, C.R. (2005). Relationships between northern Adriatic Sea mucilage events and climate variability. Science of the Total Environment, 353, 82-88.
• Drăgut L, Tiede, D., Levick, S. (2010). ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24, 859-871.
• Drăgut, L., Csillik, O., Eisank, C., Tiede, D. (2014). Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 119-127.
• Dronova, I., Gong, P., Clinton, N.E., Wang, L., Fu, W., Qi, S., Liu, Y. (2012). Landscape analysis of wetland plant functional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sensing of Environment, 127, 357-369.
• Espindola, G., Câmara, G., Reis, I., Bins, L., Monteiro, A. (2006). Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27, 3035-3040.
• Funari, E., Ade, P. (1999). Human health implications associated with mucilage in the northern Adriatic Sea. Ann Ist Super Sanita, 35(3), 421-5, PMID:10721208.
• Giani, M., Savelli, F., Berto, D., Zangrando, V., Cosović, B., Vojvodić, V. (2005). Temporal dynamics of dissolved and particulate organic carbon in the northern Adriatic Sea in relation to the mucilage events. Science of the Total Environment, 353(1–3), 126-38. PMID: 16289251.
• Gigliotti, A. (2013). Extracting temporal and spatial distributions information about marine mucilage phenomenon based on MODIS satellite images; a case study of the Tyrrhenian and the Adriatic Sea, 2010-2012 (MsC thesis). Universidade Nova. Lisboa, Portugal.
• Gotsis-Skretas, O. (1995). Mucilage appearances in Greek waters during 1982-1994. Science of the Total Environment, 165, 229-230.
• Hay, G.J., Blaschke, T., Marceau, D.J., Bouchard, A. (2003). A comparison of three image object methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry and Remote Sensing, 57(5-6), 327-345.
• Jensen, J.R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd Edition, Upper Saddle River: Prentice-Hall.
• Johnson, B., Xie, Z. (2011). Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 473-483.
• Kavzoglu, T. (2017). Object-oriented random forest for high resolution land cover mapping using Quickbird- 2 imagery. In: Samui P., Roy, S.S., Balas, V.E. (Eds.), Handbook of Neural Computation (pp. 607-619), Elsevier.
• Kavzoğlu, T., Çölkesen, İ., Sefercik, U.G., Öztürk, M.Y. (2021). Marmara Denizi’ndeki müsilaj oluşumlarının çok zamanlı optik ve termal uydu görüntülerinden makine öğrenme algoritması ile tespiti ve analizi. Harita Dergisi, 166, 1-9. (in Turkish).
• Kavzoglu, T., Erdemir, M.Y., Tonbul, H. (2016). A region-based multi-scale approach for object-based image analysis. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLI-B7, 241-247.
• Kavzoglu, T., Tonbul, H. (2018). An experimental comparison of multi-resolution segmentation, SLIC and K-means clustering for object-based classification of VHR Imagery. International Journal of Remote Sensing, 39(18), 6020-6036.
• Kavzoglu, T., Tonbul, H., Yildiz Erdemir, M., Colkesen, I. (2018), Dimensionality reduction and classification of hyperspectral ımages using object-based image analysis. Journal of the Indian Society of Remote Sensing, 46(8), 1297-1306.
• Kavzoglu, T., Yildiz Erdemir, M., Tonbul, H. (2017). Classification of semiurban landscapes from very high-resolution satellite images using a regionalized multiscale segmentation approach. Journal of Applied Remote Sensing, 11(3), 035016.
• Kraus, R., Supić, N. (2015). Sea Dynamics impacts on the macroaggregates: A case study of the 1997 mucilage event in the northern Adriatic. Progress in Ocenaography, 138, 249-267.
• Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality – dealing with complexity. In: Blaschke, T., Lang, S., Hay, G.J. (Eds.), Object-based image analysis – spatial concepts for knowledge-driven remote sensing applications (pp. 3-27), Springer, Heidelberg, Berlin, New York.
• Luo, H., Li, D., Liu, C. (2017). Parameter evaluation and optimization for multi-resolution segmentation in object-based shadow detection using very high resolution imagery. Geocarto International, 32(12), 1307-1332.
• Mecozzi, M., Pietrantonio, E., Noto, V., Papai, Z. (2005). The humin structure of mucilage aggregates in the Adriatic and Tyrrhenian seas: hypothesis about the reasonable causes of mucilage formation. Marine Chemistry, 95, 255-269.
• Özalp, H.B. (2021). First massive mucilage event observed in deep waters of Çanakkale Strait (Dardanelles), Turkey. Journal of the Black Sea/Mediterranean Environment, 27(1), 49-66.
• Rinaldi, A., Vollenweider, R.A., Montanari, G., Ferrari, C.R., Ghetti, A. (1995). Mucilages in Italian seas: the Adriatic and Tyrrhenian Seas. 1988-1991, Science of the Total Environment, 165(1-3), 165-183.
• Ryherd, S., Woodcock, C. (1996). Combining spectral and texture data in the segmentation of remotely sensed images. Photogrammetric Engineering and Remote Sensing, 62, 181-194.
• Savun-Hekimoğlu, B., Gazioğlu, C. (2021). Mucilage problem in the semi-enclosed seas: recent outburst in the Sea of Marmara. International Journal of Environment and Geoinformatics (IJEGEO), 8(4), 402-413.
• Tas, S., Kus, D., Yılmaz, I.N. (2020). Temporal variations in phytoplankton composition in the north-eastern Sea of Marmara: potentially toxic species and mucilage event. Mediterranean Marine Science, 21(3), 668-683.
• Tassan, S. (1993). An algorithm for the detection of the white-tide (“mucilage”) phenomenon in the Adriatic Sea using AVHRR data. Remote Sensing of Environment, 45(1), 29-42.
• Tonbul, H., Colkesen, I., Kavzoglu, T. (2020). Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery. Journal of Geodetic Science, 10(1), 14-22.
• Tonbul, H., Kavzoglu, T. (2020). Semi-automatic building extraction from Worldview-2 imagery using Taguchi optimization. Photogrammetric Engineering and Remote Sensing, 86(9), 547-555.
• Tüfekçi, V., Balkıs, N., Beken Polat, 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, 199-210.
• Urbani, R., Magaletti, E., Sist, P., Cicero, A.M. (2005). Extracellular carbohydrates released by the marine diatoms Cylindrotheca closterium, Thalassiosira pseudonana and Skeletonema costatum: Effect of Pdepletion and growth status. Science of The Total Environment, 353 (1-3), 300-306.
• Vollenwider, R.A., Rinaldi, A. (1995). Editorial. The Science of the Total Environment, 165(1995), 5-7.
• Yentur, R.E., Buyuates, Y., Ozen, O., Altin, A. (2013). The environmental and socio-economical effects of a biologic problem: Mucilage. Marine Science and Technology Bulletin, 2(2), 13-15.
• Zambianchi, E., Calvitti, C., Cecamore, P., D'Amico, F., Ferulano, E., Lanciano, P. (1992). The mucilage phenomenon in the Northern Adriatic Sea, summer 1989: a study carried out with remote sensing techniques. Marine Coastal Eutrophication, 126, 581-598.