IDEAS home Printed from https://ideas.repec.org/a/caa/jnljfs/v64y2018i1id111-2017-jfs.html
   My bibliography  Save this article

Adaptive k-tree sample plot for the estimation of stem density: An empirical approach

Author

Listed:
  • Hormoz SOHRABI

Abstract

Available budgets for the inventory of non-commercial woodlands are small. Therefore, there has been increased interest in using distance methods, such as k-tree sampling, which are faster than fixed plot sampling. In low-density woodlands, large search areas for k nearest trees contradict any practical advantage over sampling with fixed area plots. Here, a modification of a k-tree sample plot with an empirical approach to estimating the number of trees per unit area in low-density woodlands is presented. The standard and modified k-tree sample plots have been tested in one actual and three simulated forests with different spatial patterns. The modified method was superior to other combinations of methods in terms of relative bias and relative efficiency. Considering statistical and practical aspects of sampling for tree density, the modified method is more promising than is the standard one.

Suggested Citation

  • Hormoz SOHRABI, 2018. "Adaptive k-tree sample plot for the estimation of stem density: An empirical approach," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 64(1), pages 17-24.
    • Handle: RePEc:caa:jnljfs:v:64:y:2018:i:1:id:111-2017-jfs
    DOI: 10.17221/111/2017-JFS
    as

    Download full text from publisher

    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/111/2017-JFS.html
    Download Restriction: free of charge
    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/111/2017-JFS.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/111/2017-JFS?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.

    References listed on IDEAS

    as
    1. S. Magnussen, 2015. "A fixed count sampling estimator of stem density based on a survival function," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 61(11), pages 485-495.
    2. Li‐Chun Zhang & Raymond L. Chambers, 2004. "Small area estimates for cross‐classifications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 479-496, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. María Dolores Esteban & María José Lombardía & Esther López-Vizcaíno & Domingo Morales & Agustín Pérez, 2020. "Small area estimation of proportions under area-level compositional mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 793-818, September.
    2. Berg, Emily J. & Fuller, Wayne A., 2012. "Estimators of error covariance matrices for small area prediction," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2949-2962.
    3. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    4. Angela Luna & Li-Chun Zhang & Alison Whitworth & Kirsten Piller, 2015. "Small Area Estimates Of The Population Distribution By Ethnic Group In England: A Proposal Using Structure Preserving Estimators," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 585-602, December.
    5. Thomas Zimmermann, 2019. "Einsatzmöglichkeiten von Small Area-Verfahren bei Kohortenschätzungen im Zensus 2021 [Applicablity of small area estimation methods for demographic cohorts in the Census 2021]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 13(2), pages 157-177, September.
    6. Suesse Thomas & Namazi-Rad Mohammad-Reza & Mokhtarian Payam & Barthélemy Johan, 2017. "Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1021-1050, December.
    7. Lucie Dostál & Siegfried Gabler & Matthias Ganninger & Ralf Münnich, 2016. "Frame Correction Modelling with Applications to the German Register-Assisted Census 2011," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 904-920, September.
    8. Alison Whitworth & Kirsten Piller & Angela Luna & Li-Chun Zhang, 2015. "Small area estimates of the population distribution by ethnic group in England: a proposal using structure preserving estimators," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 585-602, December.
    9. Luna Angela & Zhang Li-Chun & Whitworth Alison & Piller Kirsten, 2015. "Small Area Estimates of the Population Distribution by Ethnic Group in England: A Proposal Using Structure Preserving Estimators," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 585-602, December.
    10. María Dolores Esteban & María José Lombardía & Esther López-Vizcaíno & Domingo Morales & Agustín Pérez, 2023. "Small area estimation of average compositions under multivariate nested error regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 651-676, June.

    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:caa:jnljfs:v:64:y:2018:i:1:id:111-2017-jfs. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    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.