Optical Remote Sensing of Intertidal Flats

  • C. Brockmann
  • K. Stelzer

Intertidal areas are characterised by very high biodiversity and high productivity. Many of the intertidal flats worldwide are protected by the Ramsar Convention and other international monitoring programmes including also the Trilateral Monitoring and Assessment Programme for the Wadden Sea along the coasts of Denmark, Germany and The Netherlands. Remote sensing is a valuable tool for providing the cost efficient monitoring required by these directives. This includes mapping from airplanes, interpretation of aerial colour and infrared images, as well as classification of multispectral and hyperspectral data from airborne or spaceborne instruments. Information concerning different components of intertidal flats, namely the sediment type, macro- and microphytes and mussel beds, can be obtained by applying classification methods to these optical remotely sensed data. Comparison of the remote sensing data with in-situ measurements is an important step for improving the tuning parameters of the classification method and for validating the results. Examples are shown for a classification of the sediment type, macrophytes and mussel beds in the German Wadden Sea area. The methods presented here are currently being transferred into operational monitoring programs.

Keywords

Clay Chlorophyll Depression Europe Radar 

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References

  1. Bartholdy J, Folving S (1986) Sediment classification and surface type mapping in the Danish Wadden Sea by remote sensing. Neth. J. Sea Res. 20(4): 337-345CrossRefGoogle Scholar
  2. Deppe F (1999) Intertidal Mudflats Worldwide. Practical course at the Common Wadden Sea Secretariat (CWSS) in Wilhelmshaven 1st June - 30th September 1999. cwss.www.de/news/documents/others/Mudflats-Worldwide-2000.pdf
  3. Deronde B, Kempeneers P, Forster RM (2006) Imaging spectroscopy as a tool to study sediment characteristics on a tidal sandbank in the Westerschelde. Estuarine Coastal and Shelf Science. Vol. 69, Iss. 3-4, pp 580-590CrossRefGoogle Scholar
  4. Doerffer R, Murphy D (1989) Factor analysis and classification of remotely sensed data for monitoring tidal flats. Helgoland Marine Research Vol. 43 No. 3-4, 275-293Google Scholar
  5. Donoghue DN, Mironnet N (2002) Development of an integrated geographical information system prototype for coastal habitat monitoring. Comput. Geosci. 28, 1 (Feb. 2002), 129-141CrossRefGoogle Scholar
  6. Hakvoort JHM, Heineke M, Heymann K, Kuhl H, Riethmuller R, Witte G (1998) A basis for mapping the erodibility of tidal flats by optical remote sensing. Mar Fresh Res 49:867-873CrossRefGoogle Scholar
  7. Kleeberg U (1990) Kartierung der Sedimentverteilung im Wattenmeer durch integrierte Auswertung von Satellitendaten und Daten aus der Wattenmeer-datenbank der GKSS Diplomarbeit Universität TrierGoogle Scholar
  8. Mackinney G (1941) Absorption of Light by Chlorophyll Solutions. Journal of Biological Chemistry, 1941Google Scholar
  9. Rainey MP, Tyler AN, Bryant RG, Gilvear DJ, McDonald P (2000) The influence of surface and interstitial moisture on the spectral characteristics of intertidal sediments. Int. J. Remote Sensing, Vol. 21, No. 16, pp 3025-3038CrossRefGoogle Scholar
  10. Rainey MP, Tyler AN, Gilvear DJ, Bryant RG, McDonald P (2003) Mapping intertidal estuarine sediment grain size distributions through airborne remote sensing. Remote Sensing of Environment, 86, 480-490CrossRefGoogle Scholar
  11. Reise K, de Jong F (1999) Biology - The Tidal Area. In: CWSS (Eds.). Wadden Sea QSR 1999, Wilhelmshaven, Germany, pp 69-70Google Scholar
  12. Reise K (2004) Vorkommen von Grünalgen und Seegras im Nationalpark Schleswig Holsteinisches Wattenmeer 2003,- ForschungsberichtGoogle Scholar
  13. Richards JA (1999) Remote Sensing Digital Image Analysis: An Introduction. Springer, Berlin. 3rd Edition.Google Scholar
  14. Ryu JH, Na YH, Won JS, Doerffer R (2002) A critical grain size for Landsat ETM+ investigations into intertidal sediments: A case study of the Gomso intertidal flats, KoreaGoogle Scholar
  15. Schmidt KS (2003) Hyperspectral Remote Sensing of Vegetation Species Distribution in a Saltmarsh. Dissertation. International Institute for Geo-Information Science and Earth Observation EnschedeGoogle Scholar
  16. Smith GM, Milton EJ (1999) The use of the empirical line method to calibrate remotely sensed data to reflectances. IJRS Vol. 20, No. 13, pp 2653-2662CrossRefGoogle Scholar
  17. Smith GM, Thomson AG, Möller I, Kromkamp JC (2004) Using hyperspectral imaging for the assessment of mudflat surface stability. Journal of Coastal Res., Vol. 20, No. 4, pp 1165-1175CrossRefGoogle Scholar
  18. Stelzer K (1998) Erfassung der Sedimentverteilung des Schleswig-Holsteinischen Wattenmeeres mit Hilfe multispektraler Fernerkundungsdaten. Diplomarbeit Universität TrierGoogle Scholar
  19. Stelzer K (2004) Potenzial der Fernerkundung im Küstenraum für die Umsetzung der EG-Wasserrahmenrichtlinie, TMAP und FFH - Abschlussbericht; http://www.brockmann-consult.de/english/flyers/pdf/PotenzialFEKueste_Abschlussbericht.pdf
  20. Stelzer K, Brockmann C (2006) Optische Fernerkundung für die Küstenzone. In: Traub KP, Kohlus J (Eds.): GIS im Küstenzonen Management. - Grundlagen und Anwendungen. - HeidelbergGoogle Scholar
  21. Thomson AG, Fuller RM, Sparks TH, Yates MG, Eastwood JA (1998) Ground and airborne radiometry over intertidal surfaces: waveband selection for cover classification. Int. J. Remote Sensing, Vol. 19, No. 6, pp 1189 -1205CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V 2008

Authors and Affiliations

  • C. Brockmann
    • 1
  • K. Stelzer
    • 1
  1. 1.Brockmann ConsultGermany

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