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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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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]
Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Land-cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling.
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  Data: International Journal of Remote Sensing. Apr2009, Vol. 30 Issue 8, p1867-1886. 20p. 4 Color Photographs, 3 Diagrams, 4 Charts, 2 Graphs, 1 Map.
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  Data: *<searchLink fieldCode="DE" term="%22Land+use%22">Land use</searchLink><br />*<searchLink fieldCode="DE" term="%22Plants%22">Plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Vegetation+management%22">Vegetation management</searchLink><br />*<searchLink fieldCode="DE" term="%22Forests+%26+forestry%22">Forests & forestry</searchLink><br /><searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Dempster-Shafer+theory%22">Dempster-Shafer theory</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Andes%22">Andes</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/01431160802541531
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        Text: English
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        PageCount: 20
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      – SubjectFull: Land use
        Type: general
      – SubjectFull: Plants
        Type: general
      – SubjectFull: Vegetation management
        Type: general
      – SubjectFull: Forests & forestry
        Type: general
      – SubjectFull: Landsat satellites
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      – SubjectFull: Distribution (Probability theory)
        Type: general
      – SubjectFull: Dempster-Shafer theory
        Type: general
      – SubjectFull: Andes
        Type: general
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      – TitleFull: Land-cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling.
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              Text: Apr2009
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              Y: 2009
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