Edge-correction needs in estimating indices of spatial forest structure

Abstract

Indices quantifying spatial forest structure are frequently used to monitor spatial aspects of tree attributes including biodiversity in research plots of limited size. The treatment of edge trees, which are close to the plot boundaries, can affect the estimation of such indices that include neighbour effects, since some of their neighbours are likely to fall outside the plot. This paper investigates whether and under what circumstances edge-correction methods are necessary and evaluates the performance of six different approaches: no edge correction, translation, reflection, buffer zone, and two new nearest-neighbour methods. The performance of edge-correction methods depends strongly on the algorithmic structure of the indices and the spatial pattern of tree positions involved. Some edge-correction methods introduce more error than ignoring edge bias altogether. For indices accounting for the diversity of tree positions and especially for those computing angles, translation or buffer zone methods reduce the estimation error regardless of the sample size. The use of the reflection method is associated with large bias values. One of the new nearest-neighbour edge-correction methods proves to be capable of reducing the bias considerably. The results confirm the need for sufficiently large monitoring plots to avoid bias from edge effects. Where this is impossible, neighbours beyond the plot boundary need to be included in the survey, thus providing unbiased estimates but at the cost of extra measurements. Sensitivity analysis is required for newly introduced indices prior to their first application.