IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v28y2023i4d10.1007_s13253-023-00548-z.html
   My bibliography  Save this article

A Variance Partitioning Multi-level Model for Forest Inventory Data with a Fixed Plot Design

Author

Listed:
  • Isa Marques

    (University of Göttingen, Chair of Statistics)

  • Paul F. V. Wiemann

    (University of Göttingen, Chair of Statistics
    Texas A &M University, College Station)

  • Thomas Kneib

    (University of Göttingen, Chair of Statistics)

Abstract

Forest inventories are often carried out with a particular design, consisting of a multi-level structure of observation plots spread over a larger domain and a fixed plot design of exact observation locations within these plots. Consequently, the resulting data are collected intensively within plots of equal size but with much less intensity at larger spatial scales. The resulting data are likely to be spatially correlated both within and between plots, with spatial effects extending over two different areas. However, a Gaussian process model with a standard covariance structure is generally unable to capture dependence at both fine and coarse scales of variation as well as for their interaction. In this paper, we develop a computationally feasible multi-level spatial model that accounts for dependence at multiple scales. We use a data-driven approach to determine the weight of each spatial process in the model to partition the variability of the measurements. We use simulated and German small tree inventory data to evaluate the model’s performance.Supplementary material to this paper is provided online.

Suggested Citation

  • Isa Marques & Paul F. V. Wiemann & Thomas Kneib, 2023. "A Variance Partitioning Multi-level Model for Forest Inventory Data with a Fixed Plot Design," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(4), pages 706-725, December.
    • Handle: RePEc:spr:jagbes:v:28:y:2023:i:4:d:10.1007_s13253-023-00548-z
    DOI: 10.1007/s13253-023-00548-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-023-00548-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-023-00548-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:jagbes:v:28:y:2023:i:4:d:10.1007_s13253-023-00548-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.