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Journal of Environmental Quality Abstract - Remote Sensing and Environmental Degradation

Combining LiDAR and IKONOS Data for Eco-Hydrological Classification of an Ombrotrophic Peatland


This article in JEQ

  1. Vol. 39 No. 1, p. 260-273
    Received: Mar 11, 2009

    * Corresponding author(s): karen.anderson@exeter.ac.uk
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  1. K. Anderson *a,
  2. J. J. Bennieb,
  3. E. J. Miltonc,
  4. P. D. M. Hughesc,
  5. R. Lindsayd and
  6. R. Meadee
  1. a School of Geography, Univ. of Exeter, Cornwall Campus, Tremough, Penryn, Cornwall, TR10 9EZ
    b School of Biosciences, Univ. of Exeter, Cornwall Campus, Tremough, Penryn, Cornwall, TR10 9EZ
    c School of Geography, Univ. of Southampton, Highfield, Southampton, Hampshire, SO17 1BJ
    d Head of Wildlife Conservation, School of Health and Bioscience, Univ. of East London, Stratford Campus, Romford Rd., Stratford E15 4LZ
    e Former National Peatland Advisor to Natural England


Remote sensing techniques have potential for peatland monitoring, but most previous work has focused on spectral approaches that often result in poor discrimination of cover types and neglect structural information. Peatlands contain structural “microtopes” (e.g., hummocks and hollows) which are linked to hydrology, biodiversity and carbon sequestration, and information on surface structure is thus a useful proxy for peatland condition. The objective of this work was to develop and test a new eco-hydrological mapping technique for ombrotrophic (rain-fed) peatlands using a combined spectral-structural remote sensing approach. The study site was Wedholme Flow, Cumbria, UK. Airborne light dectection and ranging (LiDAR) data were used with IKONOS data in a combined multispectral-structural approach for mapping peatland condition classes. LiDAR data were preprocessed so that spatial estimates of minimum and maximum land surface height, variance and semi-variance (from semi-variogram analysis) were extracted. These were assimilated alongside IKONOS data into a maximum likelihood classification procedure, and thematic outputs were compared. Ecological survey data were used to validate the results. Considerable improvements in thematic separation of peatland classes were achieved when spatially-distributed measurements of LiDAR variance or semi-variance were included. Specifically, the classification accuracy improved from 71.8% (IKONOS data only) to 88.0% when a LiDAR semi-variance product was used. Of note was the improved delineation of management classes (including Eriophorum bog, active raised bog and degraded raised bog). The application of a combined textural-optical approach can improve land cover mapping in areas where reliance on purely spectral discrimination approaches would otherwise result in considerable thematic uncertainty.

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