Using nonlinear quantile regression to estimate the self-thinning boundary curve

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  • Authors: Cao, Quang V.; Dean, Thomas J.
  • Publication Year: 2015
  • Publication Series: Proceedings - Paper (PR-P)
  • Source: In: Proceedings of the 17th biennial southern silvicultural research conference. e–Gen. Tech. Rep. SRS–203. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 3 p.

Abstract

The relationship between tree size (quadratic mean diameter) and tree density (number of trees per unit area) has been a topic of research and discussion for many decades. Starting with Reineke in 1933, the maximum size-density relationship, on a log-log scale, has been assumed to be linear. Several techniques, including linear quantile regression, have been employed to obtain parameters of the self-thinning line. Some authors recently considered that restriction on the maximum diameter at lower spatial densities resulted in a curvilinear relationship. In this study, a nonlinear quantile regression based on the 99th quantile was used to characterize this upper boundary. The resulting self-thinning curve fit the curvilinear boundary much better than did the Reineke’s self-thinning line

  • Citation: Cao, Quang V.; Dean, Thomas J. 2015. Using nonlinear quantile regression to estimate the self-thinning boundary curve. In Proceedings of the 17th biennial southern silvicultural research conference. e–Gen. Tech. Rep. SRS–203. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 3 p.
  • Posted Date: February 10, 2015
  • Modified Date: February 12, 2015

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