Water quality retrievals from high resolution ikonos multispectral imagery: A case study in Istanbul, Turkey

Water, Air, and Soil Pollution

Volumn 183, Issue 1-4, 2007, Pages 239-251

  Ekercin, Semih ^a  

^a AKSARAY UNIVERSITY   (Turkey)

Author keywords

IKONOS multispectral data; In situ; Multiple regression; Satellite remote sensing; Water quality

Indexed keywords

ALGORITHMS; CONCENTRATION (PROCESS); IMAGE SENSORS; RADIOMETRY; REGRESSION ANALYSIS; REMOTE SENSING;

IKONOS MULTISPECTRAL DATA; MULTIPLE REGRESSION; SATELLITE REMOTE SENSING;

WATER QUALITY;

CHLOROPHYLL;

ALGORITHMS; CONCENTRATION (PROCESS); IMAGE SENSORS; RADIOMETRY; REGRESSION ANALYSIS; REMOTE SENSING; WATER QUALITY;

APPLIED SCIENCE; CHLOROPHYLL A; IKONOS; IMAGE ANALYSIS; IN SITU MEASUREMENT; MAPPING; MAPPING METHOD; METROPOLITAN AREA; MULTIPLE REGRESSION; MULTISPECTRAL IMAGE; RADIOMETRIC METHOD; REMOTE SENSING; SATELLITE DATA; SUSPENDED SEDIMENT; WATER QUALITY;

ARTICLE; CORRELATION ANALYSIS; IMAGE PROCESSING; MULTIPLE LINEAR REGRESSION ANALYSIS; PARTICLE RESUSPENSION; REMOTE SENSING; SEDIMENT; TURKEY (REPUBLIC); WATER ANALYSIS; WATER QUALITY;

EURASIA; GOLDEN HORN; ISTANBUL [TURKEY]; TURKEY;

EID: 34249817605     PISSN: 00496979     EISSN: 15732932     Source Type: Journal    
DOI: 10.1007/s11270-007-9373-5     Document Type: Article

References (25)

1. Bilge, F., Yazici, B., Dogeroglu, T., & Ayday, C. (2003). Statistical evaluation of remotely sensed data for water quality monitoring. International Journal of Remote Sensing, 24, 5317-5326.
2. Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62, 1025-1036.
3. Dekker, A. G., Vos, R. J., & Peters, S. W. M. (2002). Analytical algorithms for lake water TSM estimation for retrospective analysis of TM and SPOT sensor data. International Journal of Remote Sensing, 23, 15-35.
4. D'sa, E. J., Zaitzeff, J. B., & Steward, R. G. (2000). Monitoring water quality in Florida Bay with remotely sensed salinity and in situ bio-optical observations. International Journal of Remote Sensing, 21, 811-816.
5. Ellis, J. B. (1999). Impacts of urban growth on surface water and groundwater quality. Birmingham: IAHS.
6. Engman, E. T., & Gurney, R. J. (1991). Remote sensing in hydrology. London: Chapman and Hall.
7. Gan, T. Y., Kalinga, O. A., Ohgushi, K., & Araki, H. (2004). Retrieving seawater turbidity from Landsat TM data by regressions and an artificial neural network. International Journal of Remote Sensing, 25, 4593-4615.
8. Harrington, J. A., & Schiebe, F. R. (1992). Remote sensing of Lake Chicot, Arkansas: Monitoring suspended sediments, turbidity and Secchi depth with Landsat MSS data. Remote Sensing of Environment, 39, 15-27.
9. Hellweger, F. L., Schlosser, P., Lall, U., & Weissel, J. K. (2004). Use of satellite imagery for water quality studies in New York Harbor. Estuarine, Coastal and Shelf Science, 61, 437-448.
10. Iluz, D., Yacobi, Y. Z., & Gitelson, A. (2003). Adaptation of an algorithm for chlorophyll-a estimation by optical data in the oligotrophic Gulf of Eilat. International Journal of Remote Sensing, 24, 1157-1163.
11. Jensen, J. R. (2000). Remote sensing of the environment: An earth resource perspective. Upper Saddle River, NJ: Prentice-Hall.
12. Karpouzli, E., & Malthus, T. (2003). The empirical line method for the atmospheric correction of IKONOS imagery. International Journal of Remote Sensing, 24, 1143-1150.
13. Kirk, J. T. O. (1994). Light and photosynthesis in aquatic ecosystems. Cambridge: Cambridge University Press.
14. Kondratyev, K. Y., Filatov, N. N., Johannessen, O. M., Melentyev, V. V., Pozdnyakov, D. V., Ryanzhin, et al. (1999). Chapter 3, Modern passive and active optical and microwave remote sensing: Advanced feasibilities for application in contemporary limnological stidies. In K. Ya. Kondratyev, & N. N. Filatov (Eds.), Limnology and remote sensing: A contemporary approach (pp. 169-305). Chichester: Springer-Praxis.
15. Li, R., & Li, J. (2004). Satellite remote sensing technology for lake water clarity monitoring: An overview. Environmental Informatics Archives, 2, 893-901.
16. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote sensing and image interpretation. New York: Wiley.
17. Mather, P. (1999). Computer processing of remotely sensed images. Chichester, UK.: Wiley.
18. Pozdnyakov, D., Korosov, A., Grassl, H., & Pettersson, L. (2005). An advanced algorithm for operational retrieval of water quality from satellite data in the visible. International Journal of Remote Sensing, 26, 2669-2687.
19. Pozdnyakov, D., Pettersson, L., Johannessen, O. M., Liaskovski, A., Filatov, N., & Bobylev, L. (2003). SeaWiFS maps water quality parameters of the White Sea. International Journal of Remote Sensing, 24, 4065-4071.
20. Ritchie, J. C., & Cooper, C. M. (2001). Remote sensing techniques for determining water quality: Applications to TMDLs. In TMDL Science Issues Conference, Water Environment Federation, Alexandria, VA, pp. 367-374.
21. Space Imaging Eurasia, IKONOS Relative Spectral Response and Radiometric Cal Coefficients. Retrieved August 14, 2006, from http://www.spaceimaging.com/ products/ikonos/spectral.htm.
22. Sváb, E., Tyler, A. N., Preston, T., Presing, M., & Balogh, K. V. (2005). Characterizing the spectral reflectance of algae in lake waters with high suspended sediment concentrations. International Journal of Remote Sensing, 26, 919-928.
23. Thenkabail, P. S., Enclona, E. A., Ashton, M. S., Legg, C., & De Dieu, M. J. (2000). Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sensing of Environment, 90, 23-43.
24. Turkey Travel Planner. Retrieved August 14, 2006, from http://www.turkeytravelplanner.com/WhereToGo/Istanbul/Sights/GoldenHorn/index. html.
25. Zhang, Y., Pulliainen, J. T., Koponen, S. S., & Hallikainen, M. T. (2003). Water quality retrievals from combined Landsat TM Data and ERS-2 SAR Data in the Gulf of Finland. IEEE Transactions on Geoscience and Remote Sensing, 41, 622-629.