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.

Nearest Neighborhood Characteristics of a Tropical Mixed Broadleaved Forest Stand

“…To eliminate the edge effect of the estimates in M i , W i and U i calculation, we applied the nearest neighbor edge correction method proposed by Pommerening and Stoyan [32].…”

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

“…The above tree position diversity indices could be significantly influenced by the edge trees since some of their neighbor trees might fall outside the plot [42]. It is therefore necessary to conduct edge correction.…”

Tree-Level Harvest Optimization for Structure-Based Forest Management Based on the Species Mingling Index

“…Therefore, the appropriate distance to the edge of the tract cannot be determined simply based on the number of neighbors of each tree, as it is unknown a priori how many of these neighbors will remain after harvest. While this (not using a buffer when assessing the mingling index) is a limitation of the current study, a buffer zone assumption has been shown to reduce estimation error [15].…”

“…However, this assumption was met with negative review and, therefore, was ignored in the current work. We further felt that the nearest neighbor methods of [15] were not applicable to this work, because in our case, some trees will be removed (harvested) and [15] no trees are removed. Therefore, the appropriate distance to the edge of the tract cannot be determined simply based on the number of neighbors of each tree, as it is unknown a priori how many of these neighbors will remain after harvest.…”

“…Tang et al [13] illustrated how the spatial structure for partial harvests of Phyllostachys edulis (Haubrich) (moso bamboo) in China can be improved through the use of Monte Carlo simulation. Further, Tscheschel and Stoyan [14] illustrated a process using simulated annealing to reconstruct a point pattern, which was further explored in [15] for reconstructing landscape-level individual tree distributions from sample plot distributions. Finally, the species mingling index has been optimized for the development of spatially diverse hypothetical forests, whereby trees of different species can swap locations [1].…”