IDEAS home Printed from https://ideas.repec.org/a/taf/clarxx/v37y2012i3p365-382.html
   My bibliography  Save this article

Nature as a Model for Large-scale Planting Design: Variable Classification Method

Author

Listed:
  • Ali Sepahi

Abstract

Two methods, the Regression Method and the Least Difference Method, for large-scale planting design resembling native plant communities had previously been developed. They involved selection of a plant palette from a native (model) plant community suitable for the site's climate and overall soil characteristics; followed by plant placement based on topo-edafic variables. The respective articles provided a basis for the development of software to be used by landscape designers. The Variable Classification Method proposed in the present paper is a simplified version that can be readily applied without requiring the development of special software. It involves: a) site analysis of the project site using the available site analysis software, b) classifying the topo-edaphic data of the grid cells on the project site along with those of the sample plots in the model community, and c) assigning species composition of the sample plots to the grid cells with matching topo-edaphic classes.

Suggested Citation

  • Ali Sepahi, 2012. "Nature as a Model for Large-scale Planting Design: Variable Classification Method," Landscape Research, Taylor & Francis Journals, vol. 37(3), pages 365-382.
    • Handle: RePEc:taf:clarxx:v:37:y:2012:i:3:p:365-382
    DOI: 10.1080/01426397.2011.638740
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01426397.2011.638740
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01426397.2011.638740?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.

    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:taf:clarxx:v:37:y:2012:i:3:p:365-382. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/clar20 .

    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.