Spatial forest structure reconstruction as a strategy for mitigating edge-bias in circular monitoring plots

• Systematic review of edge-bias mitigation methods.
• Identification of suitable methods for edge-bias alleviation for circular monitoring plots.
• First study on plus-sampling simulation.
• Spatial forest structure reconstruction is an excellent edge-bias compensation method.
• Reconstruction allows the use of the full potential of all circular plot data.

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.