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İskenderun Körfezi kıyı alanlarında sıcaklık ve klorofil-a için uydu ve model temelli veri setlerinin temsil yeteneği üzerine bir değerlendirme

Year 2024, Volume: 41 Issue: 3, 220 - 225, 15.09.2024

Fethi Bengil , Sinan Mavruk , Sevim Polat , Gürkan Akbulut

https://doi.org/10.12714/egejfas.41.3.07

Abstract

Bu çalışma, İskenderun Körfezi'nde yüzey suyu sıcaklığı (SST) ve klorofil-a (Chl-a) düzeylerinin uydu ve modelleme verileriyle izlenmesini ve bu veri setlerinin deniz ekosistemlerinin izlenmesinde kullanılabilirliğini araştırmaktadır. Araştırmada yerinde ölçüm veri seti, MODIS-Aqua uydu görüntülerinden elde edilen veri seti ve Copernicus MyOcean veri setinden alınan modelleme verileri kullanılmıştır. Körfezdeki SST ve chl-a dağılımı için eşleştirilmiş veri setleri üzerine yapılan analizlerin sonuçlarına göre, SST için uydu ve model veri setlerinin, klorofil-a için ise uydu veri setinin ölçüm verileri ile istatistiksel olarak anlamlı korelasyona sahip olduğunu belirlenmiştir. Veri setlerinin belirsizliği üzerine yapılan değerlendirmeler, SST için uydu veri setinin daha dar bir yayılıma ve daha az aykırı değer dağılımına sahip olduğunu ortaya koymuştur. Klorofil-a için her iki veri setinin de yüksek belirsizlik aralıklarına sahip olduğu ve daha fazla geliştirmeye ihtiyaç duyduğu görülmüştür. Bu çalışma, İskenderun Körfezi'nde SST ve chl-a değişkenlerinin izlenmesi için uydu ve model veri setlerinin kullanılabilirliğini göstermektedir.

Keywords

Uzaktan algılama sayısal modelleme temsiliyet İskenderun Körfezi

Ethical Statement

Bu çalışma için etik onay gerekli değildir.

Supporting Institution

Bu çalışma TUBİTAK 2218 Programı tarafından (121C439) desteklenmiştir.

Project Number

121C439

Thanks

Bu çalışma TUBİTAK 2218 Programı tarafından (121C439) desteklenmiştir.

References

  • Abbas, M.M., Melesse, A. M., Scinto, L.J., & Rehage, J. (2019). Satellite estimation of chlorophyll-a using moderate resolution imaging spectroradiometer (MODIS) sensor in shallow coastal water bodies: Validation and improvement. Water, 11(8), 1621. https://doi.org/10.3390/w11081621
  • Acker, J.G., & Leptoukh, G. (2007). Online analysis enhances use of NASA Earth science data. Eos, Transactions American Geophysical Union, 88(2), 14-17. https://doi.org/10.1029/2007EO020003
  • Agate, J., Ballinger, R., & Ward, R.D. (2024). Satellite remote sensing can provide semi-automated monitoring to aid coastal decision-making. Estuarine, Coastal and Shelf Science, 298, 108639. https://doi.org/10.1016/j.ecss.2024.108639
  • Ali, K.A., Ortiz, J., Bonini, N., Shuman, M., & Sydow, C. (2016). Application of Aqua MODIS sensor data for estimating chlorophyll a in the turbid case 2 waters of Lake Erie using bio-optical models. GIScience & Remote Sensing, 53(4), 483 505. https://doi.org/10.1080/15481603.2016.1177248
  • Avşar, D. (1999). Yeni bir skifomedüz (Rhopilema nomadica)’ün dağılımı ile ilgili olarak Doğu Akdeniz’in fiziko-kimyasal özellikleri. Turkish Journal of Zoology, 23(2), 605–616.
  • Baklanov, A.A., Grisogono, B., Bornstein, R., Mahrt, L., Zilitinkevich, S.S., Taylor, P., Larsen, S.E., Rotach, M.W., & Fernando, H.J.S. (2011). The nature, theory, and modeling of atmospheric planetary boundary layers. Bulletin of the American Meteorological Society, 92(2), 123–128. https://doi.org/10.1175/2010BAMS2797.1
  • Bengil, F., & Bizsel, K. (2014). Assessing the impact of aquaculture farms using remote sensing: An empirical neural network algorithm for Ildırı Bay, Turkey. Aquaculture Environment Interactions, 6(1), 67 79. https://doi.org/10.3354/aei00115
  • Bengil, F., Mavruk, S., Kızılkaya, Z., Bengil, E.G.T., Alan, V., & Kızılkaya, I.T. (2021). Descriptive capability of datasets as proxy of sea water temperature in coastal systems: An evaluation from the Aegean Sea. Turkish Journal of Fisheries and Aquatic Sciences, 21, 627-635. http://doi.org/10.4194/1303-2712-v21_12_05
  • Choi, Y., Park, Y., Hwang, J., Jeong, K., & Kim, E. (2022). Improving ocean forecasting using deep learning and numerical model integration. Journal of Marine Science and Engineering, 10(4), 450. https://doi.org/10.3390/jmse10040450
  • Clementi, E., Pistoia, J., Escudier, R., Delrosso, D., Drudi, M., Grandi, A., Lecci, R., Cretí, S., Ciliberti, S., Coppini, G., Masina, S., & Pinardi, N. (2019). Mediterranean Sea analysis and forecast (CMEMS MED Currents 2016 2019) [Data set].
  • Fettweis, M.P., & Nechad, B. (2011). Evaluation of in situ and remote sensing sampling methods for SPM concentrations, Belgian continental shelf (southern North Sea). Ocean Dynamics, 61, 157 171. https://doi.org/10.1007/s10236-010-0310-6
  • Fox-Kemper, B., Adcroft, A., Böning, C.W., Chassignet, E.P., Curchitser, E., Danabasoglu, G., Eden, C., England, M.H., Gerdes, R., Greatbatch, R.J., Griffies, S.M., Hallberg, R.W., Hanert, E., Heimbach, P., Hewitt, H.T., Hill, C.N., Komuro, Y., Legg, S., Le Sommer, J., ... & Yeager, S.G. (2019). Challenges and prospects in ocean circulation models. Frontiers in Marine Science, 6, 65. https://doi.org/10.3389/fmars.2019.00065
  • Green, K., & Tashman, L. (2009). Percentage error: What denominator? Foresight: The International Journal of Applied Forecasting, 12, 36-40.
  • IOCCG (2000). Remote sensing of ocean colour in coastal, and other optically-complex, waters. In S. Sathyendranath, (Ed.), Reports of the International Ocean-Colour Coordinating Group, No. 3, 140p., IOCCG (International Ocean-Colour Coordinating Group), Dartmouth, Canada.
  • Kim, C.S., Park, Y.-J., Park, K.S., Shim, J.S., & Lim, H.-S. (2013). Application of GOCI satellite data to ocean modeling. Journal of Coastal Research, 65(sp2), 1409-1414. https://doi.org/10.2112/SI65-238.1
  • Le Sommer, J., Chassignet, E.P., & Wallcraft, A.J. (2018). Ocean circulation modeling for operational oceanography: current status and future challenges. In New Frontiers in Operational Oceanography (pp. 289–306). GODAE OceanView. https://doi.org/10.17125/gov2018.ch12
  • Li, Z., England, M.H., & Groeskamp, S. (2023). Recent acceleration in global ocean heat accumulation by mode and intermediate waters. Nature Communications, 14, 6888. https://doi.org/10.1038/s41467-023-42468-z
  • Matarrese, R., De Pasquale, V., Guerriero, L., Morea, A., Pasquariello, G., Umgiesser, G., Scroccaro, I., & Alabiso, G. (2004). Comparison between remote-sensed data and in situ measurements in coastal waters: The Taranto Sea case. Chemistry and Ecology, 20(3), 225–237. https://doi.org/10.1080/02757540410001689795
  • Mavruk, S., Bengil, F., Yeldan, H., Manasirli, M., & Avsar, D. (2017). The trend of lessepsian fish populations with an emphasis on temperature variations in Iskenderun Bay, the Northeastern Mediterranean. Fisheries Oceanography, 26(5), 542–554. https://doi.org/10.1111/fog.12215
  • Parsons, T.R., Maita, Y., & Lalli, C.M. (1984). A manual of chemical and biological methods for seawater analysis. Pergamon Press, Oxford, New York.
  • Roland Pitcher, C., Lawton, P., Ellis, N., Smith, S.J., Incze, L.S., Wei, C.-L., Greenlaw, M.E., Wolff, N.H., Sameoto, J.A., & Snelgrove, P.V.R. (2012).
  • Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional-scale ecosystems. Journal of Applied Ecology, 49, 670 679. https://doi.org/10.1111/j.1365 2664.2012.02148.x
  • Schalles, J.F. (2006). Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal. In L. Richardson, E. LeDrew, (Eds.), Remote Sensing of Aquatic Coastal Ecosystem Processes. Remote Sensing and Digital Image Processing, 9, 27-79, Springer, Dordrecht. https://doi.org/10.1007/1-4020-3968-9_3
  • Smale, D., & Wernberg, T. (2009). Satellite-derived SST data as a proxy for water temperature in nearshore benthic ecology. Marine Ecology Progress Series, 387, 27–37. https://doi.org/10.3354/meps08132
  • Smit, A.J., Roberts, M., Anderson, R.J., Dufois, F., Dudley, S.F.J., Bornman, T.G., Olbers, J., & Bolton, J.J. (2013). A coastal seawater temperature dataset for biogeographical studies: Large biases between in situ and remotely-sensed data sets around the coast of South Africa. PLoS ONE, 8(12), e81944. https://doi.org/10.1371/journal.pone.0081944
  • Sokal, R.R., & Rohlf, F.J. (2012). Biometry: The principles and practice of statistics in biological research. 2nd edition. Journal of the Royal Statistical Society, Series A.
  • Thakur, K.K., Vanderstichel, R., Barrell, J., Stryhn, H., Patanasatienkul, T., & Revie, C.W. (2018). Comparison of sea lice (Lepeophtheirus salmonis) abundance levels on Atlantic salmon farms in eastern Canada using standard sea lice monitoring and the Atlantic zone monitoring program. Preventive Veterinary Medicine, 149, 90 99. https://doi.org/10.1016/j.prevetmed.2017.11.008
  • Wang L, Yang C, Liu Y, Shan B, Ma S, & Sun D. (2023) Effects of biotic and abiotic factors on the spatiotemporal distribution of round scad (Decapterus maruadsi) in the Hainan Island offshore area. Diversity; 15(5):659. https://doi.org/10.3390/d15050659
  • Wernberg, T., Smale, D.A., & Thomsen, M.S. (2012). A decade of climate change experiments on marine organisms: Procedures, patterns and problems. Global Change Biology, 18, 1491 1498. https://doi.org/10.1111/j.1365-2486.2012.02656.x
  • Zennaro, F., Furlan, E., Canu, D., Aveytua Alcazar, L., Rosati, G., Solidoro, C., Aslan, S., & Critto, A. (2023). Venice lagoon chlorophyll-a evaluation under climate change conditions: A hybrid water quality machine learning and biogeochemical-based framework. Ecological Indicators. 157, 111245. https://doi.org/https://doi.org/10.1016/j.ecolind.2023.111245

An evaluation on the proximity of satellite- and model-based datasets of temperature and chlorophyll-a in coastal areas of İskenderun Bay

Year 2024, Volume: 41 Issue: 3, 220 - 225, 15.09.2024

Fethi Bengil , Sinan Mavruk , Sevim Polat , Gürkan Akbulut

https://doi.org/10.12714/egejfas.41.3.07

Abstract

This study investigates the monitoring of sea surface temperature (SST) and chlorophyll-a (Chl-a) levels in Iskenderun Bay using satellite and modeling data and evaluates the possible use of these datasets for monitoring marine ecosystems. Datasets derived from MODIS-Aqua satellite imagery and modeling data obtained from the Copernicus MyOcean and in-situ measurements were used in the study. According to the analysis on paried data sets of the distribution of SST and chl-a, sattelite and model datasets showed statistically significant correlations with in-situ measurements for SST. However, only satellite dataset showed significant correlations for chl-a. Evaluations on uncertainty of the data sets revealed that the satellite dataset had a narrower range and less outlier distribution for SST. For chlorophyll-a, both datasets had wide uncertainty ranges and required further improvement. This study highlights the potential of satellite and model datasets for monitoring SST and chl-a variations in Iskenderun Bay.

Keywords

Remote sensing numerical modeling proximity İskenderun Bay

Ethical Statement

There is no need ethical approval for this study.

Supporting Institution

This study was supported by the TUBITAK 2218 Program (121C439).

Project Number

121C439

Thanks

This study was supported by the TUBITAK 2218 Program (121C439).

References

  • Abbas, M.M., Melesse, A. M., Scinto, L.J., & Rehage, J. (2019). Satellite estimation of chlorophyll-a using moderate resolution imaging spectroradiometer (MODIS) sensor in shallow coastal water bodies: Validation and improvement. Water, 11(8), 1621. https://doi.org/10.3390/w11081621
  • Acker, J.G., & Leptoukh, G. (2007). Online analysis enhances use of NASA Earth science data. Eos, Transactions American Geophysical Union, 88(2), 14-17. https://doi.org/10.1029/2007EO020003
  • Agate, J., Ballinger, R., & Ward, R.D. (2024). Satellite remote sensing can provide semi-automated monitoring to aid coastal decision-making. Estuarine, Coastal and Shelf Science, 298, 108639. https://doi.org/10.1016/j.ecss.2024.108639
  • Ali, K.A., Ortiz, J., Bonini, N., Shuman, M., & Sydow, C. (2016). Application of Aqua MODIS sensor data for estimating chlorophyll a in the turbid case 2 waters of Lake Erie using bio-optical models. GIScience & Remote Sensing, 53(4), 483 505. https://doi.org/10.1080/15481603.2016.1177248
  • Avşar, D. (1999). Yeni bir skifomedüz (Rhopilema nomadica)’ün dağılımı ile ilgili olarak Doğu Akdeniz’in fiziko-kimyasal özellikleri. Turkish Journal of Zoology, 23(2), 605–616.
  • Baklanov, A.A., Grisogono, B., Bornstein, R., Mahrt, L., Zilitinkevich, S.S., Taylor, P., Larsen, S.E., Rotach, M.W., & Fernando, H.J.S. (2011). The nature, theory, and modeling of atmospheric planetary boundary layers. Bulletin of the American Meteorological Society, 92(2), 123–128. https://doi.org/10.1175/2010BAMS2797.1
  • Bengil, F., & Bizsel, K. (2014). Assessing the impact of aquaculture farms using remote sensing: An empirical neural network algorithm for Ildırı Bay, Turkey. Aquaculture Environment Interactions, 6(1), 67 79. https://doi.org/10.3354/aei00115
  • Bengil, F., Mavruk, S., Kızılkaya, Z., Bengil, E.G.T., Alan, V., & Kızılkaya, I.T. (2021). Descriptive capability of datasets as proxy of sea water temperature in coastal systems: An evaluation from the Aegean Sea. Turkish Journal of Fisheries and Aquatic Sciences, 21, 627-635. http://doi.org/10.4194/1303-2712-v21_12_05
  • Choi, Y., Park, Y., Hwang, J., Jeong, K., & Kim, E. (2022). Improving ocean forecasting using deep learning and numerical model integration. Journal of Marine Science and Engineering, 10(4), 450. https://doi.org/10.3390/jmse10040450
  • Clementi, E., Pistoia, J., Escudier, R., Delrosso, D., Drudi, M., Grandi, A., Lecci, R., Cretí, S., Ciliberti, S., Coppini, G., Masina, S., & Pinardi, N. (2019). Mediterranean Sea analysis and forecast (CMEMS MED Currents 2016 2019) [Data set].
  • Fettweis, M.P., & Nechad, B. (2011). Evaluation of in situ and remote sensing sampling methods for SPM concentrations, Belgian continental shelf (southern North Sea). Ocean Dynamics, 61, 157 171. https://doi.org/10.1007/s10236-010-0310-6
  • Fox-Kemper, B., Adcroft, A., Böning, C.W., Chassignet, E.P., Curchitser, E., Danabasoglu, G., Eden, C., England, M.H., Gerdes, R., Greatbatch, R.J., Griffies, S.M., Hallberg, R.W., Hanert, E., Heimbach, P., Hewitt, H.T., Hill, C.N., Komuro, Y., Legg, S., Le Sommer, J., ... & Yeager, S.G. (2019). Challenges and prospects in ocean circulation models. Frontiers in Marine Science, 6, 65. https://doi.org/10.3389/fmars.2019.00065
  • Green, K., & Tashman, L. (2009). Percentage error: What denominator? Foresight: The International Journal of Applied Forecasting, 12, 36-40.
  • IOCCG (2000). Remote sensing of ocean colour in coastal, and other optically-complex, waters. In S. Sathyendranath, (Ed.), Reports of the International Ocean-Colour Coordinating Group, No. 3, 140p., IOCCG (International Ocean-Colour Coordinating Group), Dartmouth, Canada.
  • Kim, C.S., Park, Y.-J., Park, K.S., Shim, J.S., & Lim, H.-S. (2013). Application of GOCI satellite data to ocean modeling. Journal of Coastal Research, 65(sp2), 1409-1414. https://doi.org/10.2112/SI65-238.1
  • Le Sommer, J., Chassignet, E.P., & Wallcraft, A.J. (2018). Ocean circulation modeling for operational oceanography: current status and future challenges. In New Frontiers in Operational Oceanography (pp. 289–306). GODAE OceanView. https://doi.org/10.17125/gov2018.ch12
  • Li, Z., England, M.H., & Groeskamp, S. (2023). Recent acceleration in global ocean heat accumulation by mode and intermediate waters. Nature Communications, 14, 6888. https://doi.org/10.1038/s41467-023-42468-z
  • Matarrese, R., De Pasquale, V., Guerriero, L., Morea, A., Pasquariello, G., Umgiesser, G., Scroccaro, I., & Alabiso, G. (2004). Comparison between remote-sensed data and in situ measurements in coastal waters: The Taranto Sea case. Chemistry and Ecology, 20(3), 225–237. https://doi.org/10.1080/02757540410001689795
  • Mavruk, S., Bengil, F., Yeldan, H., Manasirli, M., & Avsar, D. (2017). The trend of lessepsian fish populations with an emphasis on temperature variations in Iskenderun Bay, the Northeastern Mediterranean. Fisheries Oceanography, 26(5), 542–554. https://doi.org/10.1111/fog.12215
  • Parsons, T.R., Maita, Y., & Lalli, C.M. (1984). A manual of chemical and biological methods for seawater analysis. Pergamon Press, Oxford, New York.
  • Roland Pitcher, C., Lawton, P., Ellis, N., Smith, S.J., Incze, L.S., Wei, C.-L., Greenlaw, M.E., Wolff, N.H., Sameoto, J.A., & Snelgrove, P.V.R. (2012).
  • Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional-scale ecosystems. Journal of Applied Ecology, 49, 670 679. https://doi.org/10.1111/j.1365 2664.2012.02148.x
  • Schalles, J.F. (2006). Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal. In L. Richardson, E. LeDrew, (Eds.), Remote Sensing of Aquatic Coastal Ecosystem Processes. Remote Sensing and Digital Image Processing, 9, 27-79, Springer, Dordrecht. https://doi.org/10.1007/1-4020-3968-9_3
  • Smale, D., & Wernberg, T. (2009). Satellite-derived SST data as a proxy for water temperature in nearshore benthic ecology. Marine Ecology Progress Series, 387, 27–37. https://doi.org/10.3354/meps08132
  • Smit, A.J., Roberts, M., Anderson, R.J., Dufois, F., Dudley, S.F.J., Bornman, T.G., Olbers, J., & Bolton, J.J. (2013). A coastal seawater temperature dataset for biogeographical studies: Large biases between in situ and remotely-sensed data sets around the coast of South Africa. PLoS ONE, 8(12), e81944. https://doi.org/10.1371/journal.pone.0081944
  • Sokal, R.R., & Rohlf, F.J. (2012). Biometry: The principles and practice of statistics in biological research. 2nd edition. Journal of the Royal Statistical Society, Series A.
  • Thakur, K.K., Vanderstichel, R., Barrell, J., Stryhn, H., Patanasatienkul, T., & Revie, C.W. (2018). Comparison of sea lice (Lepeophtheirus salmonis) abundance levels on Atlantic salmon farms in eastern Canada using standard sea lice monitoring and the Atlantic zone monitoring program. Preventive Veterinary Medicine, 149, 90 99. https://doi.org/10.1016/j.prevetmed.2017.11.008
  • Wang L, Yang C, Liu Y, Shan B, Ma S, & Sun D. (2023) Effects of biotic and abiotic factors on the spatiotemporal distribution of round scad (Decapterus maruadsi) in the Hainan Island offshore area. Diversity; 15(5):659. https://doi.org/10.3390/d15050659
  • Wernberg, T., Smale, D.A., & Thomsen, M.S. (2012). A decade of climate change experiments on marine organisms: Procedures, patterns and problems. Global Change Biology, 18, 1491 1498. https://doi.org/10.1111/j.1365-2486.2012.02656.x
  • Zennaro, F., Furlan, E., Canu, D., Aveytua Alcazar, L., Rosati, G., Solidoro, C., Aslan, S., & Critto, A. (2023). Venice lagoon chlorophyll-a evaluation under climate change conditions: A hybrid water quality machine learning and biogeochemical-based framework. Ecological Indicators. 157, 111245. https://doi.org/https://doi.org/10.1016/j.ecolind.2023.111245
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Maritime Engineering (Other)
Journal Section Articles
Authors

Fethi Bengil 0000-0003-0989-2829

Sinan Mavruk 0000-0003-1958-0634

Sevim Polat 0000-0002-4756-1177

Gürkan Akbulut 0000-0002-9593-154X

Project Number 121C439
Early Pub Date September 2, 2024
Publication Date September 15, 2024
Submission Date June 20, 2024
Acceptance Date July 31, 2024
Published in Issue Year 2024Volume: 41 Issue: 3

Cite

APA Bengil, F., Mavruk, S., Polat, S., Akbulut, G. (2024). İskenderun Körfezi kıyı alanlarında sıcaklık ve klorofil-a için uydu ve model temelli veri setlerinin temsil yeteneği üzerine bir değerlendirme. Ege Journal of Fisheries and Aquatic Sciences, 41(3), 220-225. https://doi.org/10.12714/egejfas.41.3.07