Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates

Ronald E. McRoberts, Daniel G. Wendt, Mark D. Nelson, Mark H. Hansen

Research output: Contribution to journalArticle

70 Scopus citations

Abstract

Estimates of forest area were obtained for the states of Indiana, Iowa, Minnesota, and Missouri in the United States using stratified analyses and observations from forest inventory plots measured in federal fiscal year 1999. Strata were created by aggregating the land cover classes of the National Land Cover Data (NLCD), and strata weights were calculated as proportions of strata pixel counts. The analyses focused on improving the precision of unbiased forest area estimates and included evaluation of the correspondence between forest/nonforest aggregations of the NLCD classes and observed attributes of forest inventory plots, evaluation of the utility of the NLCD as a stratification tool, and estimation of the effects on precision of image registration and plot location errors. The results indicate that the combination of NLCD-based stratification of inventory plots and stratified analyses increases the precision of forest area estimates and that the estimates are only slightly adversely affected by image registration and plot location errors.

Original languageEnglish (US)
Pages (from-to)36-44
Number of pages9
JournalRemote Sensing of Environment
Volume81
Issue number1
DOIs
StatePublished - Jun 12 2002

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