Integrating global land cover products for improved forest cover characterization: An application in North America

Xiao Peng Song, Chengquan Huang, Min Feng, Joseph O. Sexton, Saurabh Channan, John R. Townshend

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

Six widely used coarse-resolution global land cover data-sets - Global Land Cover Characterization (GLCC), Global Land Cover 2000 (GLC2000), GlobCover land cover product (GlobCover), MODIS land cover product (MODIS LC), the University of Maryland land cover product (UMD LC), and the MODIS Vegetation Continuous Fields tree cover layer (MODIS VCF) disagree substantially in their estimates of forest cover. Employing a regression tree model trained on higher-resolution, Landsat-based data, these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America. Evaluated using a withheld test sample, the integrated percent forest cover (IPFC) data-set has a root mean square error of 11.75% - substantially better than the 17.37% of GLCC, 17.61% of GLC2000, 17.96% of GlobCover, 15.23% of MODIS LC, 19.25% of MODIS VCF, and 15.15% of UMD LC, respectively. Although demonstrated for forest, this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.

Original languageEnglish
Pages (from-to)709-724
Number of pages16
JournalInternational Journal of Digital Earth
Volume7
Issue number9
DOIs
StatePublished - Oct 2014

Keywords

  • North America
  • data fusion
  • forest
  • land cover
  • regression tree

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