A neural network method to determine the presence or absence of permafrost near Mayo, Yukon Territory, Canada

David W. Leverington, Claude R. Duguay

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

A neural network was used to predict the presence or absence of the permafrost table within 1.5 m below the ground surface, over two study areas near Mayo, Yukon Territory. Input sources used in neural network classifications included land cover (derived from Landsat Thematic Mapper (TM) imagery), equivalent latitude, aspect, and TM band 6 (thermal infrared imagery). For the first study area, maximum median agreement between predicted and field-measured permafrost-table conditions, produced using land cover and equivalent latitude data as input to the neural network, was over 90%. The agreement percentage produced by classification of the second study area, using land cover and equivalent latitude, and using correlative permafrost-surface relations from the first study area, was 60%. Training data, the portability of which is critical in region-wide predictions of active-layer conditions, cannot be transferred between the two study areas examined here.

Original languageEnglish
Pages (from-to)205-215
Number of pages11
JournalPermafrost and Periglacial Processes
Volume8
Issue number2
DOIs
StatePublished - 1997

Keywords

  • Discontinuous permafrost
  • Neural network
  • Remote sensing

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