Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing

Fei Yuan, Kali E. Sawaya, Brian C. Loeffelholz, Marvin E. Bauer

Research output: Contribution to journalArticle

606 Scopus citations

Abstract

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing metropolitan areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) data in the seven-county Twin Cities Metropolitan Area of Minnesota for 1986, 1991, 1998, and 2002. The overall seven-class classification accuracies averaged 94% for the four years. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 80% to 90%. The maps showed that between 1986 and 2002 the amount of urban or developed land increased from 23.7% to 32.8% of the total area, while rural cover types of agriculture, forest and wetland decreased from 69.6% to 60.5%. The results quantify the land cover change patterns in the metropolitan area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyze changes in land cover over time that can be used as inputs to land management and policy decisions.

Original languageEnglish (US)
Pages (from-to)317-328
Number of pages12
JournalRemote Sensing of Environment
Volume98
Issue number2-3
DOIs
StatePublished - Oct 15 2005

Keywords

  • Change detection
  • Land cover classification
  • Landsat
  • Multitemporal

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