Updating cover type maps using sequential indicator simulation

S. Magnussen, S. de Bruin

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)

Abstract

Maximum posterior probability (MAP) maps of forest inventory (FI) cover type classes were produced from a maximum likelihood (ML) classified TM image and 5% (2%) systematic reference sampling of actual cover types for of nine 2 x 2 kin study sites in New Brunswick, Canada. MAP cover type maps were obtained via sequential indicator simulation (SIS) using collocated indicator cokriging. A 5% reference sampling increased the coefficient of accuracy of MAP cover type maps by about 0.2 compared to the accuracy of the ML classified maps. MAP prediction errors were obtained for global and small area estimates of cover type extent. MAP-based cover type statistics of extent and precision were compatible with corresponding results for maximum likelihood bias-corrected estimates (MLE). Spatial autocorrelation of MAP prediction errors declined rapidly with distance and were near 0 for distances of more than 3-4 Landsat TM pixels. MAP cover type maps produced by SIS are attractive when both global and local estimates of precision of map-derived statistics are needed. (C) 2003 Published by Elsevier Inc.
Original languageEnglish
Pages (from-to)161-170
JournalRemote Sensing of Environment
Volume87
Issue number2-3
DOIs
Publication statusPublished - 2003

Keywords

  • land-cover
  • accuracy assessment
  • classification
  • improve
  • prediction
  • agreement
  • imagery
  • volume
  • models
  • issues

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