Journal of Coastal Conservation

, Volume 2, Issue 1, pp 23–32 | Cite as

Assessing wetland changes in the venice lagoon by means of satellite remote sensing data

  • Brivio P. A. Email author
  • Zilioli E. 


Not only does lagoon ecology represent a transitional zone between the sea and the continent but it also expresses the equilibrium belt between erosion and sedimentation processes. Within the framework of a coastal management scheme, a precise and timely mapping of morphological changes in this environment is important. This paper illustrates the possible contribution of multi-temporal satellite observations in the monitoring of the erosion/sedimentation processes of coastal zones, where landscape features are subjected to highly morphodynamical modifications. In particular, an improved mapping accuracy was obtained by the successive application of the Maximum Likelihood (MLH) classifier and the Linear Mixture Model (LMM) techniques to the satellite image classification procedure. In fact, by estimating the amount of shallow water and wetland within each satellite pixel, the LMM technique allows for an accurate mapping of the transitional zones in the lagoon environment, thus permitting an optimal separation between land and water. The study concerns the Venice lagoon (Italy) which has been sinking slowly since the beginning of this century. This has led to widespread loss of wetlands. In order to monitor the development of the land cover, four Landsat Thematic Mapper scenes were examined, during the period 1984 to 1993. The results obtained proved that the digital analysis method of multitemporal satellite imagery, applied over a selected test area, enables the evolution of an estuarine environment landscape, with its different sequences of erosion and periods of accretion, to be monitored. The significant influence of tidal stages is discussed in the data analysis.


Image classification Landsat Linear Mixture Model Maximum likelihood Morphodynamics Subsidence 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anon. 1987.From pattern to process: the strategy of the Earth Observing System. EOS Science Steering Committee Report, Vol II. NASA, Washington DC pp. 140.Google Scholar
  2. Anon. 1989. REA-Riequilibrio e Ambiente.Progetto preliminare di massima delle opere alle bocche, Vol. I–II, parts 1–2. Consorzio Venezia Nuova, Ministero per i Lavori Pubblici, Magistrato alle Acque di Venezia.Google Scholar
  3. Anon. 1992.Progetto generale di massima degli interventi morfologici in laguna. Consorzio Venezia Nuova, Ministero per i Lavori Pubblici, Magistrato alle Acque di Venezia.Google Scholar
  4. Adami, A., Caielli, A., Cecconi, G. & Cianfruglia, A. 1992. Rilievi batimetrici svolti recentemente nella laguna di Venezia.Proc. 23° Idraulica e Costruzioni Idrauliche, Firenze, pp. D63–D74.Google Scholar
  5. Adams, J.B., Smith, M.O. & Johnson, P.E. 1986. Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site.J. Geophys. Res. 91/B8: 8098–8112.CrossRefGoogle Scholar
  6. Alberotanza, L. & Lechi, G.M. 1978. Frequency analysis of aerial thermal surveys on shallow water: a methodology to describe the geometric distribution of bottom morphology.Proc. Int. Symp. on Remote Sensing for Observation and Inventory of Earth Resources and Endagered Environment, Freiburg, Vol. 2, pp. 1149–1158.Google Scholar
  7. Betetto, E. 1973.Variazioni della morfologi lagunare desunte dal confronto fra le carte idrografiche della laguna di Venezia del 1931 e del 1971. Thesis, A.A 1972–73, Faculty of Science, Università di Padova, Padova.Google Scholar
  8. Carbognin, L., Marabini, F. & Tosi, L. 1995. Land subsidence and degradation of the Venetian littoral. In: Barends, Brouwer & Schroeder (eds.)Land subsidence, pp. 391–402. IAHS Publ., The Hague.Google Scholar
  9. Cavazzoni, S. & Crosera, F. 1987. Turbulent structures dependent on tidal currents in the bottom boundary layer of the Venice lagoon.Il Nuovo Cimento 10/4: 419–431.Google Scholar
  10. Cisotto, L. 1968. Confronti fra lo stato attuale della laguna di Venezia e quello risultante da una carta del 1534 e da altri documenti relativi alla vecchia laguna rinascimentale.Boll. Mus. Civ. Stor. Nat. Venezia 18: 69–89.Google Scholar
  11. Clark, J.A. & Primus, J.A. 1990. Sea-level changes resulting from future retreat of ice sheeets: an effect of CO2 warming of the climate. In: Toley, M.J. & Shennan, I. (eds.)Sea-level changes, pp. 356–370. Basil Blackwell Inc., Oxford.Google Scholar
  12. Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data.Remote Sens. Environ. 37: 35–46.CrossRefGoogle Scholar
  13. Fitzpatrick-Lins, K. 1981. Comparison of sampling procedures and data analysis for land-use and land-cover map.Photogram. Eng. Remote Sen. 47: 343–351.Google Scholar
  14. Gatto, P. & Carbognin, L. 1981. The lagoon of Venice. Natural environment trend and man-induced modification.Hydrol. Sci. Bull. 26/4: 379–391.Google Scholar
  15. Goldmann, A., Rabagliati, R. & Sguazzero, P. 1975. Propagazione della marea nella Laguna di Venezia: Analisi dei dati rilevati dalla rete mareografica lagunare negli anni 1972–73.Riv. Ital. Geofis. 2/2: 119–131.Google Scholar
  16. Gross, M.F., Hardisky, M.A., Klemas, V. & Wolf, P.L. 1987. Quantification of biomass of the marsh grassSpartina alterniflora Loisel using Landsat Thematic Mapper imagery.Photogram. Eng. Remote Sens. 53: 1577–1583.Google Scholar
  17. Haddad, K.D. & Ekberg, D.R. 1989.Potential of Landsat TM imagery for assessing the national status and trends of coastal wetlands. Proc. 5th Symp. on Coastal & Ocean Management, pp. 5192–5201. American Soc. Civil Eng, New York, NY.Google Scholar
  18. Hardin, P.J. 1994. Parametric and nearest-neighbor methods for hybrid classification: a comparison of pixel assignment accuracy.Photogramm. Eng. Remote Sensing 60: 1439–1448.Google Scholar
  19. Holben, B.N. & Shimabukuro, Y.E. 1993. Linear mixing model applied to coarse resolution data from multispectral satellite sensors.Int. J. Remote Sensing 14/11: 2231–2240.CrossRefGoogle Scholar
  20. Hurcom, S.J., Taberner, M. & Harrison, A.R. 1993.Mixture modelling of semi-arid vegetation using AVIRIS and SIRIS data. Proc. 25th ERIM Int. Symp., 4–8 April 1993, Vol. 1, pp. 123–134.Google Scholar
  21. Jensen, J.R., Cowen, D.J., Althausen, J.D., Narumalani, S. & Weatherbee, O. 1993a. An evaluation of the Coast Watch change detection protocol in South Carolina.Photogramm. Eng. Remote Sens. 59: 1039–1046.Google Scholar
  22. Jensen, J.R., Cowen, D.J., Althausen, J.D., Narumalani, S. & Weatherbee, O. 1993b. The detection and prediction of sea level changes on coastal wetlands using satellite imagery and a geographic information system.Geocarto Int. 4: 87–98.CrossRefGoogle Scholar
  23. Markham, B.L. & Barker, J.L. 1985. Spectral characterization of the LANDSAT Thematic Mapper sensors.Int. J. Remote Sensing 6(5): 697–716.CrossRefGoogle Scholar
  24. Nilsson, A. 1992.Greenhouse Earth. John Wiley & Sons, Chichester.Google Scholar
  25. Richards, J.A. 1986.Remote sensing digital image analysis: an introduction. Springer Verlag, Berlin.CrossRefGoogle Scholar
  26. Rusconi, A. 1987.Variazione delle superfici componenti il bacino lagunare. Pubblicazionen. 160, Ufficio Idrografico Magistrato alle Acque, Venezia.Google Scholar
  27. Santangelo, R., Tomasin, A., Ghermandi, G., Pugnaghi, S. & Canestrelli, P. 1982.High water in Venice. Proc. Conf. “Polders of the world”, Lelystad.Google Scholar
  28. Shimabukuro, Y.E. & Smith, J.A. 1991.The least-squares mixing models to generate fraction images derived from remote sensing multispectral data, IEEE Trans. Geosci. Remote Sens. GE-29/1: 16–20.CrossRefGoogle Scholar
  29. Swain, P.H. & Davis, S.M. 1978.Remote sensing: the quantitative approach. McGraw Hill, New York, NY.Google Scholar
  30. Terayama, Y., Ueda, Y., Arai, K. & Matsumoto, M. 1992.A comparative study on the methods for estimation of mixing ratio within a pixel. Proc. 17th ISPRS Symp., Vol. 29-B7, pp. 986–989. Washington, DC.Google Scholar
  31. Thomas, R.H. 1986. Future sea level rise and its early detection by satellite remote sensing. In: Titus, J.G. (ed.),Effects of changing stratospheric ozone and global climate, pp. 19–36. US Environ Protection Agency, Washington, DC.Google Scholar
  32. Verger, F. & Demathieu, P. 1973. Etude diachronique des surfaces d'eau et des surfaces mouillées sur deux images ERTS 1.Photo-Interprétation, 5: 1–7.Google Scholar
  33. Wigley, T.M.L. & Raper, S.C.B. 1993. Future changes in global mean temperature and sea level. In: Warrick, R.A., Barrow, E.M. & Wigley, T.M.L. (eds.),Climate and sea level changes: observations, projections and implications, pp. 111–133. Cambridge University Press, Cambridge.Google Scholar
  34. Zilioli, E., Brivio, P.A., Arrigazzi, M. & Lechi, G.M. 1994. Sub-pixel estimation of the Venice lagoon wetlands using Thematic Mapper data. In: Chavez, P.S., Jr., Marino, C.M. & Schowengerdt, R.A. (eds.)Recent Advances in Remote Sensing and Hyperspectral Remote Sensing, pp. 101–108. SPIE 2318, Bellingham, Washington, DC.CrossRefGoogle Scholar

Copyright information

© EUCC; Opulus Press Uppsala 1996

Authors and Affiliations

  1. 1.Remote Sensing Dept.Istituto di Recerca sul Rischio Sismico-CNR-IRRSMilanoItaly

Personalised recommendations