IDEAS home Printed from https://ideas.repec.org/a/igg/jmdem0/v9y2018i1p1-21.html
   My bibliography  Save this article

Machine Learning Classification of Tree Cover Type and Application to Forest Management

Author

Listed:
  • Duncan MacMichael

    (University of Washington, Bothell, USA)

  • Dong Si

    (University of Washington, Bothell, USA)

Abstract

This article is driven by three goals. The first is to use machine learning to predict tree cover types, helping to address current challenges faced by U.S. forest management agencies. The second is to bring previous research in the area up-to-date, owing to a lack of development over time. The third is to improve on previous results with new data analysis, higher accuracy, and higher reliability. A Deep Neural Network (DNN) was constructed and compared with three baseline traditional machine learning models: Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN). The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.

Suggested Citation

  • Duncan MacMichael & Dong Si, 2018. "Machine Learning Classification of Tree Cover Type and Application to Forest Management," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 9(1), pages 1-21, January.
    • Handle: RePEc:igg:jmdem0:v:9:y:2018:i:1:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2018010101
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Mohamad M. Awad & Marco Lauteri, 2021. "Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests," Sustainability, MDPI, vol. 13(10), pages 1-15, May.
    2. Ma, Wu & Lin, Guang & Liang, Jingjing, 2020. "Estimating dynamics of central hardwood forests using random forests," Ecological Modelling, Elsevier, vol. 419(C).

    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:igg:jmdem0:v:9:y:2018:i:1:p:1-21. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.