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Extending Airborne Electromagnetic Surveys for Regional Active Layer and Permafrost Mapping with Remote Sensing and Ancillary Data, Yukon Flats Ecoregion, Central Alaska

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  • Neal J. Pastick
  • M. Torre Jorgenson
  • Bruce K. Wylie
  • Burke J. Minsley
  • Lei Ji
  • Michelle A. Walvoord
  • Bruce D. Smith
  • Jared D. Abraham
  • Joshua R. Rose

Abstract

Machine‐learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at‐sensor reflectance, thermal, TM‐derived spectral indices, digital elevation models and other relevant spatial data to estimate near‐surface (0–2.6‐m depth) resistivity at 30‐m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active‐layer thickness (ALT) (

Suggested Citation

  • Neal J. Pastick & M. Torre Jorgenson & Bruce K. Wylie & Burke J. Minsley & Lei Ji & Michelle A. Walvoord & Bruce D. Smith & Jared D. Abraham & Joshua R. Rose, 2013. "Extending Airborne Electromagnetic Surveys for Regional Active Layer and Permafrost Mapping with Remote Sensing and Ancillary Data, Yukon Flats Ecoregion, Central Alaska," Permafrost and Periglacial Processes, John Wiley & Sons, vol. 24(3), pages 184-199, July.
    • Handle: RePEc:wly:perpro:v:24:y:2013:i:3:p:184-199
    DOI: 10.1002/ppp.1775
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