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Using earth observation data from multiple sources to map rare habitats in a coastal conservation area
Authors:Graham Thackrah  Peter Rhind  Clive Hurford  Mike Barnsley
Institution:(1) Department of Geography, University College London, WC1E 6BT London;(2) Countryside Council for Wales, Plas Penrhos, Ffordd Penrhos, LL57 2LQ Bangor, Gwynedd, UK;(3) Department of Geography, University of Wales, Swansea
Abstract:Kenfig NNR (National Nature Reserve) is a coastal sand dune system in south Wales, UK. The site is an important location for the conservation of the fen orchidLiparis loeselii, a significant proportion of the UK population is found solely on the site. Approaches to the mapping and monitoring of the habitats at Kenfig NNR using EO (Earth Observation) methods are investigated. Typical airborne EO missions over such sites produce more than a single source of EO data; these may include various optical imaging sensors with different spectral ranges, film cameras and ranging devices to measure topography. Conservation managers are thus presented with the problem of which sources of data to use when producing a land cover map of the site of interest. Using a data set gathered over the Kenfig NNR site, we investigate land cover mapping methods for conservation. The land cover types of interest typically cover small areas within a much larger site so they present a hard problem for the EO data and associated classification methods to solve. Land cover classifications produced from the data sets provide a set of competing hypotheses of land cover type for the site. Methods we use to resolve this competition between the data sets include voting methods, data fusion methods and a method utilising fuzzy logic to aggregate information. This paper is intended to act as an introduction to some of the issues involved in using EO data for habitat mapping in highly heterogeneous coastal dune environments and to present some preliminary results of the performance of each method.
Keywords:Dune slack  Kenfig NNR            Liparis loeselii            Vegetation survey
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