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Detection of Spatial Discontinuities in Vegetation Data by a Moving Window Algorithm

  • Heiko Balzter
  • Paul Braun
  • Wolfgang Köhler
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

In order to detect boundaries in vegetation the moving window algorithm (MWA) was applied to point-quadrat data obtained from a meadow classified as Lolio-Cynosuretum. Squared Euclidean distance was used as a dissimilarity measure. The phytosociologicai classification into a mown and an unmown area by character species was confirmed by the results of MWA. MWA is a suitable multivariate method for the detection of spatial discontinuities in vegetation data.

Keywords

Land Surface Temperature Central Pixel Vegetation Data Chihuahuan Desert Character Species 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • Heiko Balzter
    • 1
  • Paul Braun
    • 1
  • Wolfgang Köhler
    • 1
  1. 1.Institut für Pflanzenbau und Pflanzenzüchtung II, Biometrie und PopulationsgenetikJustus-Liebig-Universität GießenGießenGermany

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