IDEAS home Printed from https://ideas.repec.org/a/wly/perpro/v8y1997i2p205-215.html
   My bibliography  Save this article

A Neural Network Method to Determine the Presence or Absence of Permafrost near Mayo, Yukon Territory, Canada

Author

Listed:
  • David W. Leverington
  • Claude R. Duguay

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. © 1997 John Wiley & Sons, Ltd. Un réseau neural a été utilisé pour prédire la présence ou l'absence d'une table de pergélisol dans le 1,5 m qui se trouve sous la surface du sol dans deux sites étudiés près de Mayo, dans le Yukon. Les données introduites dans le réseau comprennent la couverture du sol (d'après un image Landsat TM), la latitude équivalente, l'aspect et l'image thermique infra‐rouge (TM bande 6). Pour la première région étudiée, l'accord maximum médian entre les conditions prédites par le modèle et les conditions mesurées sur le terrain en utilisant seulement la couverture du sol et la latitude équivalente a été supérieure à 90%. Le pourcentage de concordance résultant de l'utilisation des mêmes données dans le second site en utilisant les relations utilisées dans le premier site, n'a été que de 60%. Les relations obtenues en un endroit pour prédire les conditions de la couche active ne sont donc pas transférables entre les deux régions qui ont été considérées ici. © 1997 John Wiley & Sons, Ltd.

Suggested Citation

  • David W. Leverington & Claude R. Duguay, 1997. "A Neural Network Method to Determine the Presence or Absence of Permafrost near Mayo, Yukon Territory, Canada," Permafrost and Periglacial Processes, John Wiley & Sons, vol. 8(2), pages 205-215, April.
  • Handle: RePEc:wly:perpro:v:8:y:1997:i:2:p:205-215
    DOI: 10.1002/(SICI)1099-1530(199732)8:23.0.CO;2-5
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/(SICI)1099-1530(199732)8:23.0.CO;2-5
    Download Restriction: no

    File URL: https://libkey.io/10.1002/(SICI)1099-1530(199732)8:23.0.CO;2-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Seth William Campbell & Martin Briggs & Samuel G. Roy & Thomas A. Douglas & Stephanie Saari, 2021. "Ground‐penetrating radar, electromagnetic induction, terrain, and vegetation observations coupled with machine learning to map permafrost distribution at Twelvemile Lake, Alaska," Permafrost and Periglacial Processes, John Wiley & Sons, vol. 32(3), pages 407-426, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:perpro:v:8:y:1997:i:2:p:205-215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1099-1530 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.