SST Correlation Between Chlorophyll and Turbidity by Landsat MS Image Analysis for the Coast of Izmir Province

Yıl 2022, Cilt: 9 Sayı: 4, 35 - 45, 25.12.2022

Mert Kayalık Özşen Çorumluoğlu

https://doi.org/10.30897/ijegeo.1065482

Öz

Temporal Sea Surface Temperature (SST) analyses by satellite images are quite vital in terms of understanding the sea water quality. Specific water quality criteria include dissolved oxygen, chlorophyll, temperature, depth, pH, salinity, and turbidity, and these criteria are used to determine water quality in seas. In the current study, three criteria which are chlorophyll, temperature (SST) and turbidity were examined through their correlation with SST derived from Landsat sensors. This let to know about the examined criteria at minimum and maximum temperature dates, the relation with respect to temperature change rates, and to understand the events that occur in certain dates. The regular or irregular increases of the detected SST are evidence of sea water quality or pollution resulted from the criteria in the study area. Therefore, first turbid water which contains a high amount of suspended sediment was studied. After the turbidity index was completed, the Chlorophyll study was carried out to detect the algae like substances. The aims of the study are to evaluate the temporal change of water quality in coastal region of Izmir province, using spectral indices, and to contribute to the development of more sensitive qualitative index algorithms in the future. At the end of the study, high correlation coefficients revealed the relationship between SST and indexes.

Anahtar Kelimeler

Landsat Satellite, Sea Surface Temperature, Turbidity, Chlorophyll

Kaynakça

• Acharya, T. D., Subedi, A., Yang, I. T., & Lee, D. H. (2017). Combining Water Indices for Water and Background Threshold in Landsat Image. Proceedings, 2(3), 143. https://doi.org/10.3390/ecsa-4-04902
• Alshaikh, A. Y. (2016). Detection of Sea Surface Temperature and Thermal Pollution of Agricultural Coastal Areas using Thermal Infrared , Jeddah City , West KSA. 5(1), 51–60.
• Baughman, C. A., Jones, B. M., Bartz, K. K., Young, D. B., & Zimmerman, C. E. (2015). Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska. Remote Sensing, 7(10), 13692–13710. https://doi.org/10.3390/rs71013692
• Boucher, J., Weathers, K. C., Norouzi, H., & Steele, B. (2018). Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring. Ecological Applications, 28(4), 1044–1054. https://doi.org/10.1002/eap.1708
• Candra, D. S., Phinn, S., & Scarth, P. (2016). Cloud and cloud shadow masking using multi-temporal cloud masking algorithm in tropical environmental. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41(July), 95–100. https://doi.org/10.5194/isprsarchives-XLI-B2-95-2016
• Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3), 459–479. https://doi.org/10.1016/0034-4257(88)90019-3
• Cole, C. J., Friesen, B. A., Wilson, E. M., Wilds, S. R., & Noble, S. M. (2015). Use of Satellite Images to Determine Surface-Water Cover During the Flood Event of September 13 , 2013 , in Lyons and Western Longmont , Colorado. 1258, 20151042.
• Corumluoglu, O., & Asri, I. (2015). The effect of urban heat island on Izmir’s city ecosystem and climate. Environmental Science and Pollution Research, 22(5), 3202–3211. https://doi.org/10.1007/s11356-014-2874-z
• CORUMLUOGLU, O., & Çorumluoğlu, Ö. (2021). SSI Analysis of Long-Term UHI Development Due To Urbanization Affecting Urban Ecosystem and Local Climate Change Through City of Izmir Case. 0–38. https://www.researchsquare.com/article/rs-1048102/latest.pdf
• Gardelle, J., Hiernaux, P., Kergoat, L., & Grippa, M. (2010). Less rain, more water in ponds: A remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali). Hydrology and Earth System Sciences, 14(2), 309–324. https://doi.org/10.5194/hess-14-309-2010
• Patra, P. P., Dubey, S. K., Trivedi, R. K., Suhu, S. K., & Rout, S. K. (2016). Estimation of Chlorophyll-a Concentration and Trophic States for an Inland Lake from Landsat-8 OLI Data: A Case Nalban Lake of East Kalkota Wetland, India. Preprints, August, 18. https://doi.org/10.20944/preprints201608.0149.v1
• Rahman, I. M. M., Islam, M. M., Hossain, M. M., Hossain, M. S., Begum, Z. A., Chowdhury, D. A., Chakraborty, M. K., Rahman, M. A., Nazimuddin, M., & Hasegawa, H. (2011). Stagnant surface water bodies (SSWBs) as an alternative water resource for the Chittagong metropolitan area of Bangladesh: Physicochemical characterization in terms of water quality indices. In Environmental Monitoring and Assessment (Vol. 173, Issues 1–4). https://doi.org/10.1007/s10661-010-1414-7
• Subramaniam, S.; Saxena, M. (2011). AUTOMATED ALGORITHM FOR EXTRACTION OF WETLANDS FROM IRS RESOURCESAT LISS III DATA S.Subramaniam and Manoj Saxena RS&GIS Applications Area, National Remote Sensing Centre(NRSC), ISRO, Hyderabad, AP, 500 625, INDIA KEY WORDS: Wetland, Remote Sensing, automa. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8/W20,2011, XXXVIII(November).
• Tan, W., Liu, P., Liu, Y., Yang, S., & Feng, S. (2017). A 30-year assessment of phytoplankton blooms in erhai lake using landsat imagery: 1987 to 2016. Remote Sensing, 9(12). https://doi.org/10.3390/rs9121265
• Tarantino, E. (2012). Monitoring spatial and temporal distribution of Sea Surface Temperature with TIR sensor data. European Journal of Remote Sensing, 44(1), 97–107. https://doi.org/10.5721/ItJRS20124418
• Tepanosayn, G., Muradyan, V., Hovsepyan, A., Minasyan, L., & Asmaryan, S. (2017). A Landsat 8 OLI Satellite Data-Based Assessment of Spatio-Temporal Variations of Lake Sevan Phytoplankton Biomass. In Annals of Valahia University of Targoviste, Geographical Series (Vol. 17, Issue 1, pp. 83–89). https://doi.org/10.1515/avutgs-2017-0008
• Thomas, A., Byrne, D., & Weatherbee, R. (2002). Coastal sea surface temperature variability from Landsat infrared data. Remote Sensing of Environment, 81(2–3), 262–272. https://doi.org/10.1016/S0034-4257(02)00004-4
• Wang, J., Rossow, W. B., Uttal, T., & Rozendaal, M. (1999). Variability of cloud vertical structure during ASTEX observed from a combination of rawinsonde, radar, ceilometer, and satellite. Monthly Weather Review, 127(10), 2484–2502. https://doi.org/10.1175/1520-0493(1999)127<2484:VOCVSD>2.0.CO;2
• Xing, Q., Braga, F., Tosi, L., Lou, M., Wu, L., Tang, C., Zaggia, L., & Teatini, P. (2014). Towards Detecting Fresh Submarine Groundwater Discharge At the Laizhou Bay (Southern Bohai Sea, China) By Remote Sensing Methods. 2014(November). http://atmcorr.gsfc.nasa.gov
• Xing, Q., Chen, C. Q., & Shi, P. (2006). Method of integrating Landsat-5 and Landsat-7 data to retrieve sea surface temperature in coastal waters on the basis of local empirical algorithm. Ocean Science Journal, 41(2), 97–104. https://doi.org/10.1007/BF03022414
• Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/j.rse.2011.10.028