Research Article
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Can fish kills in Izmir bay be explained with satellite image analysis?

Year 2025, Volume: 7 Issue: 1, 143 - 160, 30.06.2025

Ahmet Adnan Erdem , Halil Şekerci Şebnem Elçi

https://doi.org/10.51489/tuzal.1682454

Abstract

Motivated by a significant environmental crisis that emerged, where large numbers of dead fish washed ashore in İzmir Bay in the summer of 2024, this study aims to analyze the spatial and temporal dynamics of water quality in the inner bay prior to this incidence. By calculating indices such as NDCI, SABI, and UWQV, and correlating them with climatic data (air temperature, wind speed and relative humidity), this research seeks to document the occurrence and drivers of algal blooms in the bay using Landsat 8 and Sentinel-2 satellite data from 2017 to 2025. This is the first comprehensive study conducted for İzmir Bay that investigates the relationships between water quality indices and climatic variables. It also incorporates aerial analysis of the inner bay to provide a broader spatial perspective. A customized code using Python is developed for this study to independently download and analyze raw satellite data with respect to defined corrections/masks. The results of eight years of analysis indicated that critical conditions arise every summer with air temperatures reaching 40 degrees in the study area. Estimated aerial averaged NDCI index and Chl-a concentration values show a strong positive correlation with air temperature, particularly in the Spearman’s rank correlation (rs = 0.67 and 0.62 respectively), indicating a significant relationship between these parameters. Aerial distribution of the indices for the selected critical dates also revealed a significant increase in estimated Chl-a levels during the summer months, specifically in the regions determined from the risk maps produced as a result of this study. The areas with the greatest vulnerability coincide where Poligon, Ilıca streams in the south and Bostanlı and Çiğli streams in the north discharging into the bay. It is recommended that any planned external intervention methods for managing algal blooms should start with these highly vulnerable areas as presented by this study.
Keywords: NDCI; SABI; UWQV; Chl-a; fish kills

Keywords

NDCI SABI UWQV Chl-a fish kills

References

  • Aktaş, N., Demir, F., & Özkan, Z. (2022). The water quality of Izmir Bay: A Case Study. Environmental Monitoring and Assessment, 194(5), 312–324.
  • Anadolu Agency. (2024). İzmir Körfezi'nde balık ölümleri devam ediyor (in Turkish). Retrieved January, 31, 2024 from https://www.denizbulten.com/izmir-korfezinde-balik-olumleri-devam-ediyor-55449h.htm
  • Aegean Sea Water Quality Report. (2022). Water quality bulletin published as a part of the monitoring program conducted by the Turkish Ministry of Environment, Urbanization and Climate.
  • Alharbi, M. (2023). Spectral indices for monitoring algal bloom dynamics in the red sea coast between Jeddah and Rabigh. Marine Pollution Bulletin, 187, 114574. https://doi.org/10.1016/j.rsase.2023.100935
  • Alawadi, F. (2010). Development of the surface algal bloom index (SABI) for assessing submerged aquatic vegetation. Environmental Monitoring and Assessment, 168(1), 511–524. https://doi.org/10.1117/12.862096
  • Beutel, M. W., & Horne, A. J. (2009). A review of the effects of hypolimnetic oxygenation on lake and reservoir water quality. Lake and Reservoir Management, 15(4), 285–297. https://doi.org/10.1080/07438149909354124
  • Braaten, J., Cohen, J., & Yang, X. (2015). Cloud detection algorithm for satellite-based water quality monitoring. Remote Sensing of Environment, 160, 171–182.
  • Carlson, R. E. (1977). A trophic state index for lakes. Limnology and Oceanography, 22(2), 361–369. https://doi.org/10.4319/lo.1977.22.2.0361
  • Chawira, M., Dube, T., & Gumindoga, W. (2013). Remote sensing based water quality monitoring in Chivero and Manyame lakes of Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 66, 38–44. https://doi.org/10.1016/j.pce.2013.09.003
  • Chorus, I., & Welker, M. (2021). Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. Taylor & Francis.
  • Davis, T. W., Berry, M. A., Boyer, G. L., & Gobler, C. J. (2019). The effects of nitrogen and phosphorus on cyanobacterial metabolism and toxin production. Harmful Algae, 81, 1–12.
  • El-Shehawy, R., Gorokhova, E., Fernández-Piñas, F., & del Campo, F. F. (2012). Global warming and harmful cyanobacteria blooms: What can be learned from experiments and field studies? Water Research, 46(5), 1347–1358. https://doi.org/10.1016/j.watres.2011.11.021
  • Gitelson, A. A., Gurlin, D., Moses, W. J., & Barrow, T. (2009). A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case2 waters. Environmental Research Letters, 4(4), 045003. https://doi.org/10.1088/1748-9326/4/4/045003
  • Häder, D. P., & Gao, K. (2015). Interactions of anthropogenic stress factors on marine phytoplankton. Frontiers in Environmental Science, 3, 14. https://doi.org/10.3389/fenvs.2015.00014
  • Han, L., & Rundquist, D. C. (1997). Comparison of NIR/Red Ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: A case study in a turbid reservoir. Remote Sensing of Environment, 62(3), 253–261. https://doi.org/10.1016/S0034-4257(97)00106-5
  • Hansen, J., & Williams, D. (2018). Satellite-based chlorophyll-a monitoring in dynamic coastal waters: Applications and challenges. Journal of Environmental Monitoring, 20(5), 1124–1132.
  • Hollstein, A., Fischer, J., Preusker, R., Finkensieper, S., & Dinter, T. (2016). Neural network cloud detection for Sentinel-2 MSI data. Remote Sensing of Environment, 178, 277–290.
  • McFeeters, S. K. (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
  • Mishra, S., & Mishra, D. R. (2012). Normalized Difference Chlorophyll Index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394–406. https://doi.org/10.1016/j.rse.2011.10.016
  • Ndungu, L. W., Ndiritu, G. G., & Mutua, B. M. (2013). Remote sensing for water quality monitoring in Lake Victoria. African Journal of Science, Technology, Innovation and Development, 5(5), 399–409.
  • Paerl, H. W., & Huisman, J. (2008). Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environmental Microbiology Reports, 1(1), 27–37. https://doi.org/10.1111/j.1758-2229.2008.00004.x
  • SSEC. (2012). Space Science and Engineering Center (SSEC) Retrieved January, 31, 2024 from https://www.ssec.wisc.edu
  • Thiemann, S., & Kaufmann, H. (2002). Lake water quality monitoring using hyperspectral airborne data—a semi-empirical multisensor approach. Remote Sensing of Environment, 81(3), 228–237. https://doi.org/10.1016/S0034-4257(01)00345-5
  • TMMOB. (2024) Chamber of Environmental Engineers İzmir Branch Water Report. Retrieved January, 31, 2024 https://icerik.cmo.org.tr/uploads/ContentFiles/2024-22-3-12-15-16-484547.pdf
  • Tuygun, G. T., Salgut, S., & Elçi, A. (2023). Long-term spatial-temporal monitoring of eutrophication in Lake Burdur using remote sensing data. Water Science & Technology, 87(9), 2184. https://doi.org/10.2166/wst.2023.113
  • Zhang, Y., Qin, B., Zhu, G., Zhang, J., & Gao, G. (2020). Variability and drivers of phytoplankton blooms in a large eutrophic lake observed using remote sensing. Water Research, 170, 115312.
  • Zlinszky, A., & Padányi-Gulyás, G. (2020). Ulyssys water quality viewer: A satellite-based global near real time water quality visualization. Proceedings Book of EGU General Assembly. Online. https://doi.org/10.5194/egusphere-egu2020-18256

Year 2025, Volume: 7 Issue: 1, 143 - 160, 30.06.2025

Ahmet Adnan Erdem , Halil Şekerci Şebnem Elçi

https://doi.org/10.51489/tuzal.1682454

Abstract

References

  • Aktaş, N., Demir, F., & Özkan, Z. (2022). The water quality of Izmir Bay: A Case Study. Environmental Monitoring and Assessment, 194(5), 312–324.
  • Anadolu Agency. (2024). İzmir Körfezi'nde balık ölümleri devam ediyor (in Turkish). Retrieved January, 31, 2024 from https://www.denizbulten.com/izmir-korfezinde-balik-olumleri-devam-ediyor-55449h.htm
  • Aegean Sea Water Quality Report. (2022). Water quality bulletin published as a part of the monitoring program conducted by the Turkish Ministry of Environment, Urbanization and Climate.
  • Alharbi, M. (2023). Spectral indices for monitoring algal bloom dynamics in the red sea coast between Jeddah and Rabigh. Marine Pollution Bulletin, 187, 114574. https://doi.org/10.1016/j.rsase.2023.100935
  • Alawadi, F. (2010). Development of the surface algal bloom index (SABI) for assessing submerged aquatic vegetation. Environmental Monitoring and Assessment, 168(1), 511–524. https://doi.org/10.1117/12.862096
  • Beutel, M. W., & Horne, A. J. (2009). A review of the effects of hypolimnetic oxygenation on lake and reservoir water quality. Lake and Reservoir Management, 15(4), 285–297. https://doi.org/10.1080/07438149909354124
  • Braaten, J., Cohen, J., & Yang, X. (2015). Cloud detection algorithm for satellite-based water quality monitoring. Remote Sensing of Environment, 160, 171–182.
  • Carlson, R. E. (1977). A trophic state index for lakes. Limnology and Oceanography, 22(2), 361–369. https://doi.org/10.4319/lo.1977.22.2.0361
  • Chawira, M., Dube, T., & Gumindoga, W. (2013). Remote sensing based water quality monitoring in Chivero and Manyame lakes of Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 66, 38–44. https://doi.org/10.1016/j.pce.2013.09.003
  • Chorus, I., & Welker, M. (2021). Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. Taylor & Francis.
  • Davis, T. W., Berry, M. A., Boyer, G. L., & Gobler, C. J. (2019). The effects of nitrogen and phosphorus on cyanobacterial metabolism and toxin production. Harmful Algae, 81, 1–12.
  • El-Shehawy, R., Gorokhova, E., Fernández-Piñas, F., & del Campo, F. F. (2012). Global warming and harmful cyanobacteria blooms: What can be learned from experiments and field studies? Water Research, 46(5), 1347–1358. https://doi.org/10.1016/j.watres.2011.11.021
  • Gitelson, A. A., Gurlin, D., Moses, W. J., & Barrow, T. (2009). A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case2 waters. Environmental Research Letters, 4(4), 045003. https://doi.org/10.1088/1748-9326/4/4/045003
  • Häder, D. P., & Gao, K. (2015). Interactions of anthropogenic stress factors on marine phytoplankton. Frontiers in Environmental Science, 3, 14. https://doi.org/10.3389/fenvs.2015.00014
  • Han, L., & Rundquist, D. C. (1997). Comparison of NIR/Red Ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: A case study in a turbid reservoir. Remote Sensing of Environment, 62(3), 253–261. https://doi.org/10.1016/S0034-4257(97)00106-5
  • Hansen, J., & Williams, D. (2018). Satellite-based chlorophyll-a monitoring in dynamic coastal waters: Applications and challenges. Journal of Environmental Monitoring, 20(5), 1124–1132.
  • Hollstein, A., Fischer, J., Preusker, R., Finkensieper, S., & Dinter, T. (2016). Neural network cloud detection for Sentinel-2 MSI data. Remote Sensing of Environment, 178, 277–290.
  • McFeeters, S. K. (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
  • Mishra, S., & Mishra, D. R. (2012). Normalized Difference Chlorophyll Index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394–406. https://doi.org/10.1016/j.rse.2011.10.016
  • Ndungu, L. W., Ndiritu, G. G., & Mutua, B. M. (2013). Remote sensing for water quality monitoring in Lake Victoria. African Journal of Science, Technology, Innovation and Development, 5(5), 399–409.
  • Paerl, H. W., & Huisman, J. (2008). Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environmental Microbiology Reports, 1(1), 27–37. https://doi.org/10.1111/j.1758-2229.2008.00004.x
  • SSEC. (2012). Space Science and Engineering Center (SSEC) Retrieved January, 31, 2024 from https://www.ssec.wisc.edu
  • Thiemann, S., & Kaufmann, H. (2002). Lake water quality monitoring using hyperspectral airborne data—a semi-empirical multisensor approach. Remote Sensing of Environment, 81(3), 228–237. https://doi.org/10.1016/S0034-4257(01)00345-5
  • TMMOB. (2024) Chamber of Environmental Engineers İzmir Branch Water Report. Retrieved January, 31, 2024 https://icerik.cmo.org.tr/uploads/ContentFiles/2024-22-3-12-15-16-484547.pdf
  • Tuygun, G. T., Salgut, S., & Elçi, A. (2023). Long-term spatial-temporal monitoring of eutrophication in Lake Burdur using remote sensing data. Water Science & Technology, 87(9), 2184. https://doi.org/10.2166/wst.2023.113
  • Zhang, Y., Qin, B., Zhu, G., Zhang, J., & Gao, G. (2020). Variability and drivers of phytoplankton blooms in a large eutrophic lake observed using remote sensing. Water Research, 170, 115312.
  • Zlinszky, A., & Padányi-Gulyás, G. (2020). Ulyssys water quality viewer: A satellite-based global near real time water quality visualization. Proceedings Book of EGU General Assembly. Online. https://doi.org/10.5194/egusphere-egu2020-18256
There are 27 citations in total.

Details

Primary Language English
Subjects Remote Sensing
Journal Section Research Articles
Authors

Ahmet Adnan Erdem 0009-0003-0210-134X

Halil Şekerci 0009-0000-2180-3695

Şebnem Elçi 0000-0002-9306-1042

Publication Date June 30, 2025
Submission Date April 24, 2025
Acceptance Date May 20, 2025
Published in Issue Year 2025 Volume: 7 Issue: 1

Cite

IEEE A. . A. Erdem, H. Şekerci, and Ş. Elçi, “Can fish kills in Izmir bay be explained with satellite image analysis?”, TJRS, vol. 7, no. 1, pp. 143–160, 2025, doi: 10.51489/tuzal.1682454.
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