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

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Bibliographic Details
Title: Land-cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling.
Authors: Göttlicher, D.1 (AUTHOR) dietrich.goettlicher@staff.uni-marburg.de, Obregón, A.1 (AUTHOR), Homeier, J.2 (AUTHOR), Rollenbeck, R.1 (AUTHOR), Nauss, T.1 (AUTHOR), Bendix, J.1 (AUTHOR)
Source: International Journal of Remote Sensing. Apr2009, Vol. 30 Issue 8, p1867-1886. 20p. 4 Color Photographs, 3 Diagrams, 4 Charts, 2 Graphs, 1 Map.
Subject Terms: *Land use, *Plants, *Vegetation management, *Forests & forestry, Landsat satellites, Distribution (Probability theory), Dempster-Shafer theory
Geographic Terms: Andes
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. [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
Description
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. [ABSTRACT FROM AUTHOR]
ISSN:01431161
DOI:10.1080/01431160802541531