, Volume 661, Issue 1, pp 97–111 | Cite as

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

  • Jörg HeblinskiEmail author
  • Klaus Schmieder
  • Thomas Heege
  • Thomas Kwaku Agyemang
  • Hovik Sayadyan
  • Lilit Vardanyan


Physics-based remote sensing in littoral environments for ecological monitoring and assessment is a challenging task that depends on adequate atmospheric conditions during data acquisition, sensor capabilities and correction of signal disturbances associated with water surface and water column. Airborne hyper-spectral scanners offer higher potential than satellite sensors for wetland monitoring and assessment. However, application in remote areas is often limited by national restrictions, time and high costs compared to satellite data. In this study, we tested the potential of the commercial, high-resolution multi-spectral satellite QuickBird for monitoring littoral zones of Lake Sevan (Armenia). We present a classification procedure that uses a physics-based image processing system (MIP) and GIS tools for calculating spatial metrics. We focused on classification of littoral sediment coverage over three consecutive years (2006–2008) to document changes in vegetation structure associated with a rise in water levels. We describe a spectral unmixing algorithm for basic classification and a supervised algorithm for mapping vegetation types. Atmospheric aerosol retrieval, lake-specific parameterisation and validation of classifications were supported by underwater spectral measurements in the respective seasons. Results revealed accurate classification of submersed aquatic vegetation and sediment structures in the littoral zone, documenting spatial vegetation dynamics induced by water level fluctuations and inter-annual variations in phytoplankton blooms. The data prove the cost-effective applicability of satellite remote-sensing approaches for high-resolution mapping in space and time of lake littoral zones playing a major role in lake ecosystem functioning. Such approaches could be used for monitoring wetlands anywhere in the world.


Remote sensing Littoral vegetation Water level fluctuations Spectral unmixing Inversion QuickBird 



We thank the SEMIS team and our other Armenian partners for supporting the project activities. A special thanks goes to the EOMAP company for support in processing and parameterisation tasks. Finally, we gratefully thank the VW Foundation for financial support of this study.


  1. Agyemang, T. K., J. Heblinski, K. Schmieder, H. Sayadyan & L. Vardanyan, 2010. Accuracy assessment of supervised classification of submersed macrophytes: the case of the Gavaraget region of Lake Sevan, Armenia. Hydrobiologia. doi: 10.1007/s10750-010-0465-7.
  2. Albert, A. & C. D. Mobley, 2003. An analytical model for subsurface irradiance and remote sensing reflectance in deep and shallow case-2 waters. Optics Express 11: 2873–2890 [available on internet at http://opticsexpress./org/abstract.cfm?URI=OPEX-11-22-2873].Google Scholar
  3. Babayan, A., S. Hakobyan, K. Jenderedjian, S. Muradyan & M. Voscanov, 2006. Lake Sevan—Experience and Lessons Learned Brief: 347–362 [available on internet at].
  4. Becker, B. L., D. P. Lusch & J. Qi, 2005. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis. Remote Sensing of Environment 97: 238–248.CrossRefGoogle Scholar
  5. Becker, B. L., D. P. Lusch & J. Qi, 2007. A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetlands imagery. Remote Sensing of Environment 108: 111–120.CrossRefGoogle Scholar
  6. Blaschke, T., 2000. Landscape metrics: Konzepte eines jungen Ansatzes der Landschaftsökologie im Naturschutz. Archiv für Naturschutz & Landschaftsforschung 9: 267–299.Google Scholar
  7. Bugarelli, B., V. Kisselev & L. Roberti, 1999. Radiative transfer in the atmosphere ocean system: the finite-element method. Applied Optics 38: 1530–1542.CrossRefGoogle Scholar
  8. Chick, J. H. & C. C. McIvor, 1994. Patterns in the abundance and composition of fishes among beds of different macrophytes: viewing a littoral zone as a landscape. Canadian Journal of Fisheries and Aquatic Sciences 51: 2873–2882.CrossRefGoogle Scholar
  9. Chilingaryan, A. L., B. P. Mnatsakanyan, K. A. Aghababyan & H. V. Toqmagyan, 2002. Hydrology of Rivers and Lakes of Armenia. Yerevan, Armenia.Google Scholar
  10. Dekker, A., V. Brando, J. Anstee, N. Pinnel, T. Kutser, H. Hoogenboom, R. Pasterkamp, S. Peters, R. Vos, C. Olbert & T. Malthus, 2001. Applications of imaging spectrometry in inland, estuarine, coastal and ocean waters. In van der Meer, F. D. & S. M. de Jong (eds), Imaging Spectrometry: Basic Principles and Prospective Applications, Volume IV of Remote Sensing and Digital Image Processing. Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
  11. Diehl, S., 1993. Effects of habitat structure on resource availability, diet and growth of benthivorous perch, Perca fluviatilis. OIKOS 67: 403–414.CrossRefGoogle Scholar
  12. Dienst, M., K. Schmieder & W. Ostendorp, 2004. Effects of water level variations on the dynamics of the reed belts of Lake Constance. Limnologica 34: 29–36.Google Scholar
  13. Digital Globe, 2009 (1). QuickBird Product Description [available on internet at, accessed 24 February 2009].
  14. Digital Globe, 2009 (2). QuickBird Imagery Products, Product Guide, Revision 5.0 [available on internet at, accessed 03 March 2009].
  15. Govender, M., K. Chetty & H. Bulcock, 2007. A review of hyperspectral remote sensing and its application in vegetation and water resources studies. Water SA 33: 145–151.Google Scholar
  16. Haberäcker, P., 1991. Digitale Bildverarbeitung: Grundlagen und Anwendungen. Carl Hanser Verlag, München, Wien.Google Scholar
  17. Heege, T., A. Bogner & N. Pinnel, 2003. Mapping of submerged aquatic vegetation with a physically based process chain. Proceedings of Remote Sensing, SPIE—The International Society for Optical Engineering, Vol. 5233. CD-ROM Proceedings.Google Scholar
  18. Heege, T., C. Häse, A. Bogner & N. Pinnel, 2004. Physikalisch basierte Prozessierung multispektraler Fernerkundungsdaten von Binnengewässern. Laufener Seminarbeiträge, Bayer. Akad. f. Naturschutz u. Landschaftspflege 03: 67–71.Google Scholar
  19. Heege, T., P. Hausknecht & H. Kobryn, 2006. Hyperspectral seafloor mapping and direct bathymetry calculation using HyMap data from the Ningaloo reef and Rottnest Island areas in Western Australia, CD-ROM. 13th ARSP Conference, 20–24 November 2006, Canberra, Australia: 1–7.Google Scholar
  20. Heege, T. & J. Fischer, 2004. Mapping of water constituents in Lake Constance using multispectral airborne scanner data and a physically based processing scheme. Canadian Journal Remote Sensing 30: 77–86.Google Scholar
  21. Im, J., J. R. Jensen & J. A. Tullis, 2007. Object-based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing 29(2): 399–423.CrossRefGoogle Scholar
  22. Jenderedjian, K., A. Babayan, S. Hakobyan, S. Muradyan & M. Voskanov, 2005. Managing Lakes and their Basins for Sustainable Use: A Report for Lake Basin Managers and Shareholders. ILEC Foundation, Kusatsu, Japan.Google Scholar
  23. Karibyan, M., 2007. Head of Meteorological Station, Sevan, Hydro-Meteorological Agency, Ministry of Nature Protection, Armenia. Personal Communication 4 August 2007.Google Scholar
  24. Kisselev, V. B., L. Roberti & G. Perona, 1995. Finite-element algorithm for radiative transfer in vertically inhomogeneous media: numerical scheme and applications. Applied Optics 34: 8460–8471.CrossRefPubMedGoogle Scholar
  25. Lu, D. & Q. Weng, 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5): 823–870.CrossRefGoogle Scholar
  26. McGarigal, K., S. A. Cushman, M. C. Neel & E. Ene, 2002. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps [available on internet at, accessed March 2010].
  27. MES-Armenia (Ministy of Emergency Situations), 2008. Hydro-Meteorological Agency. Communication November 2008.Google Scholar
  28. Miksa, S., T. Heege, V. Kisselev & P. Gege, 2005. Mapping water constituents in Lake Constance using Chris/Proba. Proceedings of the 3rd ESA Chris/Proba Workshop, Volume ESA SP-593, June 2005, Frascati, Italy. ESRIN.Google Scholar
  29. Ostendorp, W., M. Dienst & K. Schmieder, 2003. Disturbance and rehabilitation of lakeside Phragmites reeds following an extreme flood in Lake Constance (Germany). Hydrobiologia 506–509: 687–695.CrossRefGoogle Scholar
  30. Petr, T., 2000. Interactions between fish and aquatic macrophytes in inland waters. A review. FAO Fisheries Technical Paper 396. FAO, Rome: 185 pp.Google Scholar
  31. Pinnel, N., 2007. A Method for Mapping Submerged Macrophytes in Lakes using Hyperspectral Remote Sensing: 164 pp [available on internet at].
  32. Sawaya, K. E., L. G. Olmanson, N. J. Heinert, P. L. Brezonik & M. E. Bauer, 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
  33. Schmieder, K., M. Dienst & W. Ostendorp, 2002. Effects of the extreme flood in 1999 on the spatial dynamics and stand structure of the reed belts in Lake Constance. Limnologica 32: 131–146.Google Scholar
  34. Schmieder, K., A. Woithon, T. Heege & N. Pinnel, 2010. Remote sensing techniques and GIS modeling approaches for monitoring and assessment of littoral vegetation at Lake Constance, Germany. Verhandlungen International Verein Limnologie 30(10): 1–3.Google Scholar
  35. Schneider, S., 2007. Macrophyte trophic indicator values from a European perspective. Limnologica 37(4): 281–289.Google Scholar
  36. Strang, I. & M. Dienst, 2004. Effects of water level at Lake Constance on the Deschampsietum rhenanae from 1989 to 2003. Limnologica 34: 22–28.Google Scholar
  37. Weaver, M. J., J. J. Magnuson & M. K. Clayton, 1997. Distribution of littoral fishes in structurally complex macrophytes. Canadian Journal of Fisheries and Aquatic Sciences 54: 2277–2289.CrossRefGoogle Scholar
  38. Wilcox, D. A., 1992. Implications for faunal habitat related to altered macrophyte structure in regulated lakes in Northern Minnesota. Wetlands 12: 192–203.CrossRefGoogle Scholar
  39. Wilcox, D. A. & S. J. Nichols, 2008. The effects of water-level fluctuations on vegetation in a Lake Huron wetland. Wetlands 28(2): 487–501.CrossRefGoogle Scholar
  40. Wilcox, D. A. & Y. Xie, 2007. Predicting wetland plant community responses to proposed water-level-regulation plans for Lake Ontario: GIS-based modeling. Journal of Great Lakes Research 33(4): 751–773.CrossRefGoogle Scholar
  41. Woithon, A. & K. Schmieder, 2004. Bruthabitatmodellierung für den Drosselrohrsänger (Acrocephalus arundinaceus L.) als Bestandteil eines integrativen Managementsystems für Seeufer. Limnologica 34: 132–139.Google Scholar
  42. Wolter, P. T., C. A. Johnston & G. J. Niemi, 2005. Mapping submerged aquatic vegetation in the US Great Lakes using QuickBird satellite data. International Journal of Remote Sensing 26(23): 5255–5274.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Jörg Heblinski
    • 1
    Email author
  • Klaus Schmieder
    • 3
  • Thomas Heege
    • 2
  • Thomas Kwaku Agyemang
    • 3
  • Hovik Sayadyan
    • 4
  • Lilit Vardanyan
    • 5
  1. 1.GilchingGermany
  2. 2.EOMAP GmbH & Co. KGGilchingGermany
  3. 3.Institute for Landscape and Plant EcologyUniversity of HohenheimStuttgartGermany
  4. 4.Department of Forestry and Agro-EcologyState Agrarian UniversityYerevanArmenia
  5. 5.Vanevan UniversityYerevanArmenia

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