In forest monitoring, the second strategy of using small, frequently
replicated sample units is mostly implemented with circular
plot designs. Whilst circular plot shapes can markedly ease
mensuration effort (van Laar and Akça, 2007), edge-bias compensation
for the data involved is less straightforward. Most edge-bias
mitigation methods have been developed for rectangular plots (see
Radtke and Burkhart, 1998; Pommerening and Stoyan, 2006) because
the necessary mathematics are less complex for this plot
shape. One of the few methods that are applicable to circular plots
is the transformation of circular plots to square ones followed by
the torus edge-correction as suggested by Williams et al. (2001).
Windhager (1997) reflects neighbouring trees through points of
the circular plot boundaries defined by lines parallel to the line
connecting the subject tree and the plot centre. In general, reflection
and translation methods are difficult to apply to circular
In forest monitoring, the second strategy of using small, frequently
replicated sample units is mostly implemented with circular
plot designs. Whilst circular plot shapes can markedly ease
mensuration effort (van Laar and Akça, 2007), edge-bias compensation
for the data involved is less straightforward. Most edge-bias
mitigation methods have been developed for rectangular plots (see
Radtke and Burkhart, 1998; Pommerening and Stoyan, 2006) because
the necessary mathematics are less complex for this plot
shape. One of the few methods that are applicable to circular plots
is the transformation of circular plots to square ones followed by
the torus edge-correction as suggested by Williams et al. (2001).
Windhager (1997) reflects neighbouring trees through points of
the circular plot boundaries defined by lines parallel to the line
connecting the subject tree and the plot centre. In general, reflection
and translation methods are difficult to apply to circular
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