Application of spectral and environmental variables to map the Kissimmee Prairie ecosystem using classification trees

S. Griffin, J. Rogan, D. Runfola

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

This paper compares a variety of classification tree-based approaches to map 10 vegetation cover classes and a single built-up class in the Kissimmee Prairie Ecosystem, an endangered grass-shrubland landscape in south-central Florida (USA). This comparison is provided to identify an effective and replicable mapping methodology and facilitate the ongoing regional-scale management and monitoring of grass-shrubland ecosystems. Results showed that the best-performing models included environmental variables, due to the ability of these variables to help distinguish spectrally similar classes. The highest overall proportional accuracy of 81% was the result of incorporating linear spectral mixture analysis and geo-environmental variables into the classification tree.
Original languageEnglish
Pages (from-to)299-323
Number of pages25
JournalGIScience and Remote Sensing
Volume48
Issue number3
DOIs
StatePublished - 2011

Bibliographical note

Cited By :8

Export Date: 26 December 2018

Correspondence Address: Griffin, S.; Clark UniversityUnited States

Keywords

  • environmental effect
  • grassland
  • mapping
  • multispectral image
  • prairie
  • shrubland
  • vegetation cover
  • Florida [United States]
  • United States

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