Environmental Monitoring and Assessment

, Volume 155, Issue 1–4, pp 83–90 | Cite as

Determination of chlorophyll-a amount in Golden Horn, Istanbul, Turkey using IKONOS and in situ data



The objective of this research was to explore an accurate and fast way of estimating chlorophyll-a amount, a water quality parameter (WQP), using IKONOS satellite sensor image and in situ measurements. Since the in situ data of WQPs are limited with the number of sampling locations, deriving a correlation between these measurements and remotely sensed image allows synoptic estimates of the related parameter over large areas even if the areas are in remote and inaccessible locations. In this study, simultaneously collected satellite image data and in situ measurements of chlorophyll-a were correlated using multivariate regression model. Different experiments were designed by changing the numbers and distributions of in situ measurements. Regression coefficients of each design and differences between model-derived data and in situ measurements were calculated to find out the optimum design to produce chlorophyll-a map of study region. Results illustrated that both the number and distribution of in situ measurements have impact on regression analysis, therefore should be selected attentively. Also, it is found that IKONOS imagery is an efficient and effective source to derive chlorophyll-a map of the large areas using limited number of ground measurements.


Remote sensing Chlorophyll-a Linear regression IKONOS Accuracy 


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  1. Alp, M., & Cigizoglu, H. K. (2007). Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling & Software, 22, 2–13.CrossRefGoogle Scholar
  2. Cigizoglu, H. K., & Kisi, O. (2005). Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nordic Hydrology, 36, 49–64.Google Scholar
  3. Cracknell, A. P., Newcombe, S. K., Black, A. F., & Kirby, N. E. (2001). The ADMAP (algal bloom detection, monitoring and prediction) concerted action. International Journal of Remote Sensing, 22, 205–247.CrossRefGoogle Scholar
  4. Crist, E. P., & Cicone, R. C. (1984). A physically-based transformation of thematic mapper data – the tasseled cap. IEEE Transactions on Geoscience and Remote Sensing, 22, 256–263.CrossRefGoogle Scholar
  5. Dekker, A. G., Vos, R. J., & Peters, S. W. M. (2001). Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. The Science of the Total Environment, 268, 197–214.CrossRefGoogle Scholar
  6. EPA (2007). U. S. Environmental Protection Agency web page. http://www.epa.gov/.
  7. Goddijn, L. M., & White, M. (2006). Using a digital camera for water quality measurements in Galway Bay Estuarine. Coastal and Shelf Science, 66, 429–436.CrossRefGoogle Scholar
  8. Herut, B., Tibor, G., Yacobi, Y. Z., & Kress, N. (1999). Synoptic measurements of chlorophyll-a and suspended particulate matter in a transitional zone from polluted to clean seawater utilizing airborne remote sensing and ground measurements, Haifa Bay. Marine Pollution Bulletin, 38, 762–772.CrossRefGoogle Scholar
  9. Lathrop, R. G., & Lillesand, T. M. (1989). Monitoring water quality and river plume transport in Green Bay, Lake Michigan with SPOT-1 imagery. Photogrammetric Engineering and Remote Sensing, 55, 349–354.Google Scholar
  10. Lillesand, T. M., Johnson, W. L., Deuell, R. L., Lindstrom, O. M., & Meisner, D. E. (1983). Use of Landsat Data to predict the trophic state of Minnesota Lakes. Photogrammetric Engineering and Remote Sensing, 49, 219–229.Google Scholar
  11. Lindell, T., Pierson, D., Premazzi, G., & Zilioli, E. (1999). Manual for monitoring European lakes using remote sensing techniques EUR report (Vol. 18665). Luxembourg: Office for Official Publications of the European Communities (EN).Google Scholar
  12. Linsley, R. K., Kohler, M. A., & Paulhus, J. L. H. (1975). Hydrology for engineers, 2nd ed. London: McGraw-Hill.Google Scholar
  13. Phinn, S. R., Dekker, A. G., Brando, V. E., & Roelfsema, C. M. (2005). Mapping water quality and substrate cover in optically complex coastal and reef waters: An integrated approach. Marine Pollution Bulletin, 51, 459–469.CrossRefGoogle Scholar
  14. Pozdnyakov, D., Shuchman, R., Korosov, A., & Hatt, C. (2005). Operational algorithm for the retrieval of water quality in the Great Lakes. Remote Sensing of Environment, 97, 352–370.CrossRefGoogle Scholar
  15. Richter, R. (1996). Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Computers & Geosciences, 22, 675–681.CrossRefGoogle Scholar
  16. Sarikaya, O. V. (2006). IKONOS uydu goruntusuyle Halic’te su kalitesi analizi. MSc thesis, Istanbul Technical University, pp. 119–123 (in Turkish).Google Scholar
  17. Sawaya, K. E., Olmanson, V., Heinert, N. J., Brezonik, P. L., & Bauer, M. E. (2003). Extending satellite remote sensing to local scales: Land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88, 144–156.CrossRefGoogle Scholar
  18. Sertel, E., Kutoglu, S. H., & Kaya, S. (2007). Geometric correction accuracy of different satellite sensor images: Application of figure condition. International Journal of Remote Sensing, 28(20), 4685–4692.CrossRefGoogle Scholar
  19. Space Imaging (2004). IKONOS imagery products and product guide, pp. 2–3.Google Scholar
  20. USGS (2007). U.S. Geological Survey web page. http://www.usgs.gov/.
  21. Zhang, Y., Guindon, B., & Cihlar, J. (2002). An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images. Remote Sensing of Environment, 82, 173–187.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Geodesy and Photogrammetry Engineering, Remote Sensing DivisionIstanbul Technical UniversityIstanbulTurkey
  2. 2.GIS and IT DepartmentDe BiltThe Netherlands

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