Classification of forest vegetation in north-central Minnesota using Landsat Multispectral Scanner and Thematic Mapper data

Margaret M Moore, M. E. Bauer

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Computer classifications of Landsat-5 Thematic Mapper (TM) and Multispectral Scanner (MSS) data were evaluated to determine how forest and sensor characteristics affect the classification accuracy of Minnesota forest cover types. The Landsat classification maps were compared on a pixel by pixel basis with a digitized reference map of Itasca State Park. Classification results were compared for statistically significant differences using discrete multivariate statistics. Classification accuracies ranged from 26 to 86%, depending upon the sensor, number of classes, and performance measure used. -from Authors

Original languageEnglish (US)
Pages (from-to)330-342
Number of pages13
JournalForest Science
Volume36
Issue number2
StatePublished - 1990

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Landsat multispectral scanner
scanners
Landsat
taxonomy
vegetation
sensors (equipment)
pixel
sensor
forest cover
multivariate analysis

ASJC Scopus subject areas

  • Forestry
  • Plant Science

Cite this

Classification of forest vegetation in north-central Minnesota using Landsat Multispectral Scanner and Thematic Mapper data. / Moore, Margaret M; Bauer, M. E.

In: Forest Science, Vol. 36, No. 2, 1990, p. 330-342.

Research output: Contribution to journalArticle

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