Forest ecosystem monitoring is an important task of forest science worldwide and its outcomes have
important consequences both for national forest policies and local decision making. Often forest monitoring
is implemented using circular plots of limited size. Edge effects can seriously bias the results of spatially
explicit analyses of circular plot data and little research has been carried out on how to mitigate this
problem. In this study, we have compared the method of spatial forest structure reconstruction to traditional
plus-sampling, to the reflection method and to a situation where no edge-bias mitigation method is
used at all. Reconstruction is a non-parametric modelling method based on simulated annealing. In the
context of this study, the arithmetic means of structural summary characteristics are used to extrapolate
spatial patterns previously measured in the core area of the monitoring plots to the margins outside. The
computer experiments were based on 706 circular monitoring plots of the Estonian long-term monitoring
network maintained by the Estonian University of Life Sciences, Tartu. The results clearly indicate the
superiority of the reconstruction method and suggest that this approach has great potential for future
spatially-explicit data analyses and modelling involving circular monitoring plots. Further improvements
can be expected from using density functions and histograms of structural summary characteristics
instead of arithmetic means.