KaydedilenlerDerginizi ekleyin

Dergiler / Türkiye Uzaktan Algılama Dergisi / 2026 / Volume: 7 Issue: 1

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

Ahmet Adnan Erdem(IZMIR INSTITUTE OF TECHNOLOGY)0009-0003-0210-134X
Halil Şekerci(IZMIR INSTITUTE OF TECHNOLOGY)0009-0000-2180-3695
Şebnem Elçi(IZMIR INSTITUTE OF TECHNOLOGY)0000-0002-9306-1042
Sayfa
—
Yayın Tarihi
2025-06-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Keywords: NDCI; SABI; UWQV; Chl-a; fish kills

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez: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.
📊
Temel Bulgular & Veri: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.
💡
Sonuç & Katkı:Keywords: NDCI; SABI; UWQV; Chl-a; fish kills
🔬Tür:Research Article
📍Kapsam:2024 Verileri
⚙️Değişkenler:NDCI · SABI · UWQV

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

🔖

Bu Makaleye Atıf Yap

Erdem, A. A., Şekerci, H., Elçi, Ş. (2025). Can fish kills in Izmir bay be explained with satellite image analysis?. Türkiye Uzaktan Algılama Dergisi, Volume: 7 Issue: 1. https://doi.org/10.51489/tuzal.1682454

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (27)

  1. Aktaş, N., Demir, F., & Özkan, Z. (2022). The water quality of Izmir Bay: A Case Study. Environmental Monitoring and Assessment, 194(5), 312–324.
  2. 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
  3. 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.
  4. 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
  5. 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
  6. 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
  7. Braaten, J., Cohen, J., & Yang, X. (2015). Cloud detection algorithm for satellite-based water quality monitoring. Remote Sensing of Environment, 160, 171–182.
  8. 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
  9. 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
  10. Chorus, I., & Welker, M. (2021). Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. Taylor & Francis.
  11. 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.
  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
  13. 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
  14. 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
  15. 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
  16. 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.
  17. 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.
  18. 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
  19. 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
  20. 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.
  21. 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
  22. SSEC. (2012). Space Science and Engineering Center (SSEC) Retrieved January, 31, 2024 from https://www.ssec.wisc.edu
  23. 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
  24. 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
  25. 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
  26. 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.
  27. 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