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Quantifying Landscape / Ecological Succession in a Coastal Dune System Using Sequential Aerial Photography and GIS

  • S. Shanmugam
  • M. Barnsley
Part of the Coastal Systems and Continental Margins book series (CSCM, volume 4)

Abstract

This chapter presents an attempt to measure the path of habitat and vegetation succession in a coastal dune system (Kenfig NNR, south Wales) using remote sensing and GIS. The loss of slack habitats associated with the continuing stabilization of this dune system is a major cause for concern. These habitats support a range of plant species, including the rare fen orchid, Liparis loeselii, as well as other hydrophytes. A decrease in their areal extent implies a reduction in biodiversity. To quantify the overall rate and spatial dimension of these changes, a series of aerial photographs dating from 1962 to 1994 were digitised and analysed in an image processing system. The resultant maps, transferred to a vector-based GIS, were used to derive a transition matrix for the dune system over this period of time. The results indicate that there has been a marked reduction in the total area of bare sand (19.6% of the dune system in 1962, but only 1.48% in 1994) and a decline in both the areal extent and the number of dune slacks. Over the same period of time, there has been an increase in Salix repens dominated habitats, at the expense of pioneer species. Analysis of the habitat maps, together with hydrological data, within the GIS suggests that even the dry slacks have the potential for further greening and to support invasive species. In terms of habitat management, however, there is still scope to restore many of the slacks to their original state. It is estimated that at least 24% of the area occupied by partially and moderately vegetated slacks could be rehabilitated.

Keywords

Geographical Information System Aerial Photograph Coastal Dune Areal Extent Dune System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • S. Shanmugam
  • M. Barnsley

There are no affiliations available

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