Modeling vegetation pattern using digital terrain data

Frank W. Davis, Scott Goetz

Research output: Contribution to journalArticlepeer-review

128 Scopus citations


Using a geographic information system (GIS), digital maps of environmental variables including geology, topography and calculated clear-sky solar radiation, were weighted and overlaid to predict the distribution of coast live oak (Ouercus agrifolia) forest in a 72 km2 region near Lompoc, California. The predicted distribution of oak forest was overlaid on a map of actual oak forest distribution produced from remotely sensed data, and residuals were analyzed to distinguish prediction errors due to alteration of the vegetation cover from those due to defects of the statistical predictive model and due to cartographic errors. Vegetation pattern in the study area was associated most strongly with geologic substrate. Vegetation pattern was also significantly associated with slope, exposure and calculated monthlysolar radiation. The proportion of observed oak forest occurring on predicted oak forest sites was 40% overall, but varied substantially between substrates and also depended strongly on forest patch size, with a much higher rate of success for larger forest patches. Only 21% of predicted oak forest sites supported oak forest, and proportions of observed vegetation on predicted oak forest sites varied significantly between substrates. The non-random patterns of disagreement between maps of predicted and observed forest indicated additional variables that could be included to improve the predictive model, as well as the possible magnitude of forest loss due to disturbances in different parts of the landscape.

Original languageEnglish (US)
Pages (from-to)69-80
Number of pages12
JournalLandscape Ecology
Issue number1
StatePublished - Mar 1990
Externally publishedYes


  • California
  • geographic information system
  • oak forest
  • patch size
  • predictive mapping
  • remote sensing

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecology
  • Nature and Landscape Conservation


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