Explicitly incorporating spatial dependence in predictive vegetation models in the form of explanatory variables: A Mojave Desert case study

Jennifer Miller, Janet Franklin

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Predictive vegetation modeling is defined as predicting the distribution of vegetation across a landscape based upon its relationship with environmental factors. These models generally ignore or attempt to remove spatial dependence in the data. When explicitly included in the model, spatial dependence can increase model accuracy. We develop presence/absence models for 11 vegetation alliances in the Mojave Desert with classification trees and generalized linear models, and use geostatistical interpolation to calculate spatial dependence terms used in the models. Results were mixed across models and methods, but in general, the spatial dependence terms more consistently increased model accuracy for widespread alliances. GLMs had higher accuracy in general.

Original languageEnglish (US)
Pages (from-to)411-435
Number of pages25
JournalJournal of Geographical Systems
Volume8
Issue number4
DOIs
StatePublished - Oct 1 2006

Keywords

  • Classification tree
  • Generalized linear model
  • Predictive vegetation models
  • Spatial dependence

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

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