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Environmental Monitoring and Assessment

, Volume 186, Issue 8, pp 5009–5026 | Cite as

Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications

  • V. MarkogianniEmail author
  • E. Dimitriou
  • I. Karaouzas
Article

Abstract

Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophyll-a concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic–eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R 2) among point sampling results and the remotely sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available.

Keywords

Water quality Chlorophyll-a Trophic status Landsat Lake 

Notes

Acknowledgments

This study has been conducted under the project entitled “Monitoring of the ecological quality of the Koumoundourou Lake and designing of management, restoration and developmental actions” and has been funded by HELLENIC PETROLEUM SA. We would like to express our gratitude to the two anonymous reviewers for their valuable comments toward the improvement of this manuscript.

References

  1. Allan, M. G., Hamilton, D. P., Hicks, B. J., & Brabyn, L. (2011). Landsat remote sensing of chlorophyll-a concentrations in central North Island lakes of New Zealand. International Journal of Remote Sensing, 32(7), 2037–2055.CrossRefGoogle Scholar
  2. Arthington, A. H., & Welcomme, R. L. (1995). The condition of large river systems of the world. In N. B. Armantrout & R. J. Jr Wolotira (Eds.), Condition of the world’s aquatic habitats (pp. 44–75). Lebanon: World Fisheries Congress Science Publishers.Google Scholar
  3. Beauchamp, E. G., Trevors, J. T., & Paul, J. W. (1989). Carbon sources for bacterial denitrification. Advances in Soil Science, 10, 113–142.CrossRefGoogle Scholar
  4. Bergmann, T., Fahnenstiel, G., Lohrenz, S., Millie, D., & Schofield, O. (2004). Impacts of a recurrent resuspension event and variable phytoplankton community composition on remote sensing reflectance. Journal of Geophysical Research, 109.Google Scholar
  5. Bremner, J. M., & Shaw, K. (1958). Denitrification in soil. II Factors affecting denitrification. The Journal of Agricultural Science, 51, 40–52.CrossRefGoogle Scholar
  6. Budd, J., & Warrington, D. (2004). Satellite-based sediment and chlorophyll a estimates for Lake Superior. Journal of Great Lakes Research—Special Issue on Lake Superior, 30, 459–466. doi: 10.1016/S0380-1330(04)70406-2. ISSN: 0380-1330.CrossRefGoogle Scholar
  7. Carlson, R. E., & Simpson, J. (1996). A coordinator’s guide to volunteer lake monitoring methods. North American Lake Management Society, 96.Google Scholar
  8. Clarke, K. R., & Gorley, R. N., PRIMER v6. (2006). User manual/tutorial. Plymouth.Google Scholar
  9. Conides, A., & Parpoura, A. R. (1997). A study of oil pollution effects on the ecology of a coastal lake ecosystem. The Environmentalist, 17, 297–306.CrossRefGoogle Scholar
  10. Danen-Louwerse, H. J., Lijklema, L., & Coenraats, M. (1995). Coprecipitation of phosphate with calcium carbonate in Lake Veluwe. Water Research, 29, 1781–1785.CrossRefGoogle Scholar
  11. Dekker, A. G., & Peters, S. W. M. (1993). The use of the Thematic Mapper for the analysis of eutrophic lakes: a case study in the Netherlands. International Journal of Remote Sensing, 14, 799–821.CrossRefGoogle Scholar
  12. Dekker, A. G., Malthus, T. J., Wijnen, M. M., & Seyhan, E. (1992). Remote sensing as a tool for assessing water quality in Loosdrecht Lakes. Hydrobiologia, 233, 137–159.CrossRefGoogle Scholar
  13. Dimitriou, E., Mentzafou, A., Zoggaris, S., Gritzalis, K., Karaouzas, I., Skoulikidis, N., et al. (2011). Monitoring of the ecological quality of Koumoundourou Lake and designing of management, restoration and developmental actions. 1st Technical Report, IIW-HCMR.Google Scholar
  14. Dimitriou, E., Karaouzas, I., Sarantakos, K., Zacharias, I., Bogdanos, K., & Diapoulis, A. (2008). Groundwater risk assessment at a heavily industrialized catchment and the associated impacts on a peri-urban wetland. Journal of Environmental Management, 88, 526–538.CrossRefGoogle Scholar
  15. EPA—US Environmental Protection Agency. (2000). Nutrient criteria, technical guidance manual, lakes and reservoirs, First Edition, EPA-822-B00-001.Google Scholar
  16. EPA. (2007). Ambient water quality criteria for dissolved oxygen, water clarity and chlorophyll-a for the Chesapeake Bay and its tidal tributaries chlorophyll criteria addendum. In coordination with the Office of Water/Office of Science and Technology, Washington, D.C. and the states of Delaware, Maryland, New York, Pennsylvania, Virginia and West Virginia and the District of Columbia.Google Scholar
  17. Eppley, R. W., & Weiler, C. S. (1979). The dominance of nanoplankton as an indicator of marine pollution: a critique. Oceanologica Acta, 2, 241–245.Google Scholar
  18. Everard, M., & Powell, A. (2002). Rivers as living systems. Aquatic Conservation: Marine and Freshwater Ecosystems, 12(4), 329–337.CrossRefGoogle Scholar
  19. Gikas, G. D., Yiannakopoulou, T., & Tsihrintzis, V. A. (2006). Water quality trends in a coastal lagoon impacted by non-point source pollution after implementation of protective measures. Hydrobiologia, 563, 385–406. doi: 10.1007/s10750-006-0034-2.CrossRefGoogle Scholar
  20. Gitelson, A., Mayo, M., Yacobi, Y. Z., Parparov, A., & Berman, T. (1994). The use of high-spectral-resolution radiometer data for detection of low chlorophyll concentrations in Lake Kinneret. Journal of Plankton Research, 16, 993–1002.CrossRefGoogle Scholar
  21. Gitelson, A., Garbuzov, G., Szilagyi, F., Mittenzwey, K. H., Karnieli, A., & Kaiser, A. (1993). Quantitative remote sensing methods for real-time monitoring of inland water quality. International Journal of Remote Sensing, 14, 1269–1295.CrossRefGoogle Scholar
  22. Gitelson, A. A., Nikanorov, A. M., Sabo, G., & Szilagyi, F. (1986). Etude de la qualite des esex de surface par teledetection. IAHS Publications, 157, 111–121.Google Scholar
  23. Goodin, D. G., Han, L., Fraser, R. N., Rundquist, D. C., Stebbins, W. A., & Schalles, J. F. (1993). Analysis of suspended solids in water using remotely sensed high resolution derivative spectra. Photogrammetric Engineering and Remote Sensing, 59, 505–510.Google Scholar
  24. Hamilton, M. K., Davis, C. O., Rhea, W. J., Pilorz, S. H., & Carder, K. L. (1993). Estimating chlorophyll content and bathymetry of Lake Tahoe using AVIRIS data. Remote Sensing of Environment, 44, 217–230.CrossRefGoogle Scholar
  25. Han, L., & Jordan, J. K. (2005). Estimating and mapping chlorophyll-a concentration in Pensacola Bay, Florida using Landsat ETM+ data. International Journal of Remote Sensing, 23, 5245–5254.CrossRefGoogle Scholar
  26. Han, L., Rundquist, D. C., Liu, L. L., Fraser, R. N., & Schalles, J. F. (1994). The spectral responses of algal chlorophyll in water with varying levels of suspended sediment. International Journal of Remote Sensing, 15, 3707–3718.CrossRefGoogle Scholar
  27. Herrera-Silveira, J. A., & Morales-Ojeda, S. M. (2009). Evaluation of the health status of a coastal ecosystem in southeast Mexico: assessment of water quality, phytoplankton and submerged aquatic vegetation. Marine Pollution Bulletin, 59, 72–86.CrossRefGoogle Scholar
  28. Holm-Hansen, O., Lorenzen, C. J., Hormes, R. N., & Strickland, J. D. H. (1965). Fluorometric determination of chlorophyll. Journal du Conseil/Conseil Permanent International pour l’Exploration de la Mer, 30, 3–15. doi: 10.1093/icesjms/30.1.3
  29. Isla Molleda M. (2007). Water quality in recirculating aquaculture systems for arctic charr (Salvelinus alpinus L.) culture. División de Cultivos Marinos, Centro de Investigaciones Pesqueras (CIP). Final Project.Google Scholar
  30. Jacquet, J.M., & Zang, B., (1989). Colour analysis of inland waters using Landsat TM data, European coordinated effort for monitoring the Earth’s environment, ESA SP-II 02, (Noordwijk, The Netherlands: ESTEC) (pp. 57–70).Google Scholar
  31. Keiner, L. E., & Yan, X. (1998). A neural network model for estimating sea surface chlorophyll and sediments form Thematic Mapper imagery. Remote Sensing of Environment, 66, 153–165.CrossRefGoogle Scholar
  32. Koponen, S., Pulliainen, J., Kallio, K., & Hallikainen, M. (2002). Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment, 79(1), 51–59.CrossRefGoogle Scholar
  33. Kruskal, J. B. (1964). Nonmetric multidimensional scaling: a numerical method. Psychometrika, 29, 115–129.CrossRefGoogle Scholar
  34. Lathrop, R. G., Lillesand, T. M., & Yandell, B. S. (1991). Testing the utility of simple multi-date thematic mapper calibration algorithms for monitoring turbid inland waters. International Journal of Remote Sensing, 10, 2045–2063.CrossRefGoogle Scholar
  35. Lathrop, R. G., & Lillesand, T. M. (1986). Use of thematic mapper data to assess water quality in Green Bay and central Lake Michigan. Photogrammetric Engineering & Remote Sensing, 52, 671–680.Google Scholar
  36. Lesht, B. M., Barbiero, R. P., & Warren, G. J. (2013). A band-ratio algorithm for retrieving open-lake chlorophyll values from satellite observations of the Great Lakes. Journal of Great Lakes Research, 39, 138–152.CrossRefGoogle Scholar
  37. Li, H., Budd, J. W., & Green, S. A. (2004). Evaluation and regionalization optimization of bio-optical algorithms for central Lake Superior. Journal of Great Lakes Research—Special Issue on Lake Superior, 30, 443–458. doi: 10.1016/S0380-1330(04)70405-0. ISSN: 0380-1330.CrossRefGoogle Scholar
  38. Matthews, M. W., Stewart, B., & Robertson, L. (2012). An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sensing of Environment, 124, 637–652.CrossRefGoogle Scholar
  39. Metcalfe-Smith, J. L. (2009). Biological water-quality assessment of rivers: use of macroinvertebrate communities. In P. Calow & G. E. Petts (Eds.), The rivers handbook: hydrological and ecological principles, Volume Two. Oxford: Blackwell Science Ltd. doi: 10.1002/9781444313871.ch8.Google Scholar
  40. Millie, D. F., Baker, M. C., Tucker, C. S., Vinyard, B. T., & Dionigi, C. P. (1992). High resolution airborne remote sensing of bloom-forming phytoplankton. Journal of Phycology, 28, 281–290.CrossRefGoogle Scholar
  41. Moss, B., Stephens, D., Alvarez, C., Becares, E., Van De Bund, W., Collings, S., et al. (2003). The determination of ecological status in shallow lakes—a tested system (ECOFRAME) for implementation of the European Water Framework Directive. Aquatic Conservation: Marine and Freshwater Ecosystems, 13, 507–549.CrossRefGoogle Scholar
  42. Moustaka-Gouni, M., Vardaka, E., Michaloudi, E., Kormas, K. A., Tryfon, E., Mihalatou, H., Gkelis, S., & Lanaras, T. (2006). Plankton food web structure in a eutrophic polymictic lake with a history of toxic cyanobacterial blooms. Limnology and Oceanography, 51, 715–727.CrossRefGoogle Scholar
  43. Niemi, G. J., Devore, P., Detenbeck, N., Taylor, D., & Lima, A. (1990). Overview of case studies on recovery of aquatic systems from disturbance. Environmental Management, 14, 571–587.CrossRefGoogle Scholar
  44. OECD, Vollenweider, R. A., & Kerekes, J. J. (1982). Eutrophication of waters. Monitoring, assessment, and control (p. 192). Paris: OECD.Google Scholar
  45. Pillay, T. V. R., & Kutty, M. N. (2005). Aquaculture, principles and practices (2nd ed.). Oxford: Blackwell Publishing Ltd. 630.Google Scholar
  46. Reardon, B. C., & McGarrigle, M. L. (1989). Utility of Thematic Mapper data for national survey of fresh lakes, European Coordinated Effort for Monitoring the Earth’s Environment, ESA SP-II02, (Noordwijk, The Netherlands: ESTEC) (pp. 25–31).Google Scholar
  47. Ritchie, J. C., Cooper, C. M., & Schieoe, F. R. (1990). The relationship of MSS and TM digital data with suspended sediments, chlorophyll, and temperature in Moon Lake, Mississippi. Remote Sensing of Environment, 33, 137–148.CrossRefGoogle Scholar
  48. Rosenberg, D. M., & Resh, V. H. (1993). Freshwater biomonitoring and benthic macroinvertebrates. New York: Chapman Hall.Google Scholar
  49. Scheffer, M., Hosper, S., Meijer, M., Moss, B., & Jeppesen, E. (1993). Alternative equilibria in shallow lakes. Trends in Ecology and Evolution, 8, 275–279.CrossRefGoogle Scholar
  50. Schindler, D. (1978). Factors regulating phytoplankton production and standing crop in the world’s freshwaters. Limnology and Oceanography, 23, 478–486.CrossRefGoogle Scholar
  51. Shuchman, R., Korosov, A., Hatt, C., Pozdnyakov, D., Means, J., & Meadows, G. (2006). Verification and application of a bio-optical algorithm for Lake Michigan using Sea-WiFS: a 7-year inter-annual analysis. Journal of Great Lakes Research, 32, 258–279.CrossRefGoogle Scholar
  52. Spatharis, S., & Tsirtsisa, G. (2010). Ecological quality scales based on phytoplankton for the implementation of Water Framework Directive in the Eastern Mediterranean. Ecological Indicators, 10(4), 840–847.CrossRefGoogle Scholar
  53. Specchiulli, A., Focardi, S., Renzi, M., Scirocco, T., Lucrezia, C., Paolo, B., & Simone, B. (2008). Environmental heterogeneity patterns and assessment of trophic levels in two Mediterranean lagoons: Orbetello and Varano, Italy. Science of the Total Environment, 402, 285–298.CrossRefGoogle Scholar
  54. Stanner, D., & Bourdeau, P. (1995). Europe’s environment. Copenhagen: European Environmental Agency.Google Scholar
  55. Starbuck, S.M. Jr. (1998). The toxicity of ammonia to the summer flounder (Paralychtus dentatus), Atlantic Silverside (Menidia menidia), and Quahog Clam (Mercenaria mercenaria). Civil and Environmental Engineering, Virginia Tech, Master thesis.Google Scholar
  56. Tassan, S. (1997). A numerical model for the detection of sediment concentration in stratified river plumes using Thematic Mapper data. International Journal of Remote Sensing, 18, 2699–2705.CrossRefGoogle Scholar
  57. Tiedje, B., Moll, A., & Kaleschke, L. (2010). Comparison of temporal and spatial structures of chlorophyll derived from MODIS satellite data and ECOHAM3 model data in the North Sea. Journal of Sea Research, 64, 250–259.CrossRefGoogle Scholar
  58. Vertucci, F. A., & Likens, G. E. (1989). Spectral reflectance and water quality of Adirondack mountain region lakes. Limnology and Oceanography, 34, 1656–1672.CrossRefGoogle Scholar
  59. Walker, I. R., (2001). Midges: Chironomidae and related Diptera (pp. 43–66). In: J. P. Smol, H. J. B.Google Scholar
  60. Wetzel, R. (2001). Limnology. Lake and river ecosystems. New York: Academic Press.Google Scholar
  61. Witter, D. L., Ortiz, J. D., Palm, S., Heath, R. T., & Budd, J. W. (2009). Assessing the application of SeaWiFS ocean color algorithm to Lake Erie. Journal of Great Lakes Research, 35, 361–370.CrossRefGoogle Scholar
  62. YCEO—Yale Center for Earth Observation, Yale Institute of Biospheric Studies. (2010). Index of CEO Documentation (http://www.yale.edu/ceo/Documentation/).
  63. Zacharias, I., Bertachas, I., Skoulikidis, N., & Koussouris, T. (2002). Greek Lakes: limnological overview. Lakes & Reservoirs: Research & Management, 7, 55–62. doi: 10.1046/j.1440-1770.2002.00171.x.CrossRefGoogle Scholar
  64. Zilioli, E., & Brivio, P. A. (1997). The satellite derived optical information for the comparative assessment of lacustrine water quality. The Science of the Total Environment, 196, 29–245.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Marine Biological Resources and Inland WatersHellenic Centre for Marine ResearchAnavyssosGreece

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