Land-cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling

  • Authors:

  • D. Gottlicher;A. Obregon;J. Homeier;R. Rollenbeck;T. Nauss;J. Bendix

  • Affiliations:

  • Department of Geography, Laboratory for Climatology and Remote Sensing, University of Marburg, 35032 Marburg, Germany;Department of Geography, Laboratory for Climatology and Remote Sensing, University of Marburg, 35032 Marburg, Germany;Plant Ecology, Albrecht-von-Haller-Institute for Plant Sciences, University of Gottingen, 37073 Gottingen, Germany;Department of Geography, Laboratory for Climatology and Remote Sensing, University of Marburg, 35032 Marburg, Germany;Department of Geography, Laboratory for Climatology and Remote Sensing, University of Marburg, 35032 Marburg, Germany;Department of Geography, Laboratory for Climatology and Remote Sensing, University of Marburg, 35032 Marburg, Germany

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2009

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Abstract

A land-cover classification is needed to deduce surface boundary conditions for a soil-vegetation-atmosphere transfer (SVAT) scheme that is operated by a geoecological research unit working in the Andes of southern Ecuador. Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to classify distinct vegetation types in the tropical mountain forest. Besides a hard classification, a soft classification technique is applied. Dempster-Shafer evidence theory is used to analyse the quality of the spectral training sites and a modified linear spectral unmixing technique is selected to produce abundancies of the spectral endmembers. The hard classification provides very good results, with a Kappa value of 0.86. The Dempster-Shafer ambiguity underlines the good quality of the training sites and the probability guided spectral unmixing is chosen for the determination of plant functional types for the land model. A similar model run with a spatial distribution of land cover from both the hard and the soft classification processes clearly points to more realistic model results by using the land surface based on the probability guided spectral unmixing technique.