Plots
ANU

 Plots
Forest Measurement and Modelling.


Unbiased estimates of timber and many other natural resource quantities can be obtained from any fixed-area plot size and plot shape (rectangular, square, circular, and narrow-width rectangular called striplines or transects). However, the optimum size and shape for a plot will vary with forest conditions. For important forest inventories, a pilot study to determine the relative efficiency of different sizes and shapes by comparing the respective sampling errors and costs may be worthwhile (Gambill et al 1985).

A guiding principle in choosing the size of unit is to have it large enough to include a representative number of trees but small enough so that the time required for measurement is not excessive. Thus, the size of unit should be related to the distribution and variation of the elements of the population. In the past, the following plot sizes have been commonly used:
  • 0.5 ha plots in a stand of mature eucalypts.

  • 0.05 ha plots in a stand of poles.

  • 0.0005 ha plots in a stand of regeneration.

  • 0.05 to 0.02 ha plots in coniferous plantation.

Concentric or multi-area plots are sometimes used in mixed forest containing a wide range of tree sizes from mature veterans down to saplings/regeneration. Measurement of the large trees is done on the plots of greatest area whereas that of the smaller trees is confined to the inner plot of smallest area. Circular plots with three concentric radii plots have been commonly used in New South Wales and many other states. In Tasmania, bi-area plots are created by dividing rectangular plots down the centre line - large trees anywhere in a rectangular plot are measured, but small trees are only measured on one side of the centre line.

Except in very uniform populations, small plots yield more variable information than large plots, that is, the variation around the mean value in the smaller plots is greater than the variation around the mean of the larger plots. However, the lower costs of plot establishment and the wider distribution possible for smaller plots may give an increase in the precision of estimate of the mean.


[plot.htm] Revision: 7/2000
Cris.Brack@anu.edu.au