Issues‎ > ‎vol19n3-n4‎ > ‎


Water Quality Assessment Models for Dokan Lake Using Landsat 8 OLI Satellite Images

Hasti Shwan Abdullah, Mahmoud S. Mahdi & Hekmat M. Ibrahim


It is impractical to monitor water quality more than a small fraction of lakes by conventional field methods because of expense and time requirements. Satellite image is more convenient to be applied to collect the required data for monitoring and assessing water quality in the lakes. Therefore, this study aims to estimate the concentration of some water quality parameters (Temperature, DO, BOD, pH, Turbidity, TSS, TDS, EC, NO3, PO4 and E. coli) by applying developed models based on the remote sensing and GIS techniques on the Landsat 8 OLI satellite image using twenty points in Dokan lake, Kurdistan Region, Iraq at two different seasons. Multiple linear regression is used to obtain mathematical models for estimating the concentration of some water quality parameters depending on spectral reflectance of Landsat 8 OLI. In this study, new band (coastal blue) of Landsat 8 OLI has been
undertaken in developing of models. Moreover, new Independent Component Analysis (ICA) and new 7 band ratios with 16 band combinations have been used. The best models are obtained for TSS, Turbidity and DO with coefficient of determination (R2 ) of 0.98, 0.98, and 0.83 respectively. Generally, for spring season, the performance of all models is reduced due to seasonal change, variance of parameters and other factors. However, high R2 of 0.86 has been shown for Temperature. The results of the developed WQPs models have been mapped to show the water quality parameters concentration distribution within Dokan lake. The conclusions present that correlation of all bands of Landsat 8 OLI is appropriate to water quality parameters.

KeywordsDokan Lake, WQPs, Image Processing, Landsat 8, GIS


[1] S. Y. Ming, J. M. Carolyn and M. S. Robert, "Adaptive Short-Term Water Quality Forecasts Using Remote Sensing and GIS", in AWRA Symposium on GIS and Water Resources, Ft. Lauderdale, FL, (1996).

[2] R. D. Hedger, P. M. Atkinson and T. J. Malthus, "Optimizing Sampling Strategies for Estimating Mean Water Quality in Lakes Using Geostatistical Techniques with Remote Sensing". Lakes & Reservoirs: Research and Management, Vol. 6, pp. 279-288. (2001).

[3] D. Doxaran, R. C. N. Cherkuru and S. J. Lavender, "Use of Reflectance Band Ratios to Estimate Suspended and Dissolved Matter Concentrations in Estuarine Waters". International Journal of Remote Sensing, Vol. 26, No. 8, pp. 1763-1769, (2005).

[4] E. Alparslan, C. Aydöner, V. Tufekci and H. Tüfekci, "Water Quality Assessment At Ömerli Dam Using Remote Sensing Techniques". Environ Monit Assess, Vol. 135, pp. 391-398. (2007).

[5] B. Nas, H. Karabork, S. Ekercin and A. Berktay, "Assessing Water Quality in the Beysehir Lake ( Turkey ) by the Application of GIS, Geostatistics and Remote Sensing", in Taal 2007: The 12th World Lake Conference, Konya, Turkey, (2008).

[6] P. Li, L. Jiang and Z. Feng, "Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors". Remote Sensing, Vol. 6, pp. 310-329, (2014).

[7] F. D. v. d. Meer and S. M. d. Jong, "Image Spectrometry ; Basic Principle and Prospective applications", Third Edition ed., Donrecht, The Netherlands: Springer, (2006).

[8] K. Ararat, N. A. Hassan and S. Abdul Rahman, "Key Biodiversity Survey of Kurdistan, Northern Iraq". Nature Iraq Report, Sulaimani, Kurdistan, Iraq, (2009).

[9] S. S. Ali and S. K. R. Salley, "Numerical Groundwater Flow Modeling for the Intwrgranular Aquifer in Sarsian Sub-Basin, Dokan Lake, Iraqi Kurdistan Region". Journal of Zankoy Sulaimani-Part A, Vol. 15, No. 1, pp. 125-141, (2003).

[10] A. H. A. Bilbas, "Ecosystem Health Assessment of Dukan Lake, Sulaimani, Kurdistan Region of Iraq", Erbil: Salahaddin University, (2014).

[11] K. Navulur, "Multispectral Image Analysis Using the Object-Oriented Paradigm", First Edition, Boca Raton, FL: CRCpress, Taylor & Francis Group. LLC, (2007).

[12] T. M. Lillesand, R. W. Kiefeer and J. W. Chipman, "Remote Sensing and Image Interpretation", Six Edition ed., USA: John Wiley & Sons Inc., (2007).

[13] USGS, "Landsat-A Global Land-Imaging Mission", [Online]. Available: [Accessed 9 6 2015].

[14] UN-ESCWA and B. , "Inventory of Shared Water Resources in Western Asia", Beriut: United Nations Economic and Social Commission for Western, (2013).

[15] C. D. S. S. Baboo and S. Thirunavukkarasu, "Geometric Correction in High Resolution Satellite Imagery Using Mathematical Methods: A Case Study in Kiliyar Sub Basin". Global Journal of Computer Science and Technology: Graphics & Vision, Vol. 14, No. 1, pp. 35-40, (2014).

[16] S. C. Goslee, "Analyzing Remote Sensing Data in R: The Landsat Package". Journal of Statistical Software, Vol. 43, No. 4, pp. 1-25, (2011).

[17] C. C. f. R. S. CCRS, "Tutorial: Fundamentals of Remote Sensing". [Online]. Available: products/educational-resources/9309. [Accessed 03 07 2015].

[18] T. W. Ray, "A FAQ on Vegetation in Remote Sensing". [Online]. Available: [Accessed 25 12 2014].

[19] P. M. Berthouex and L. C. Brown, "Statistics for Enviromental Engineers", 2 ed., Boca Raton, London, New York, Washington, D.C.: Lewis Publishers, (2002).

[20] D. Campbell and S. Campbell, "Introduction to Regression and Data Analysis", StatLab Workshop Series, (2008).

[21] J. O. Rawlings, S. G. Pantula and D. A. Dickey, "Applied Regression Analysis: A Research Tool", Second Edition, Newyork: Springer, (1998).

[22] S. Weisberg, "Applied Linear Regression", Third Edition ed., Hoboken, New Jersey: John Wiley & Sons, Inc., (2005).