What question did this study set out to answer?

This work aims to evaluate the effect of borderline trees on the estimation of basal area in forest sampling using point sampling techniques.

February 22, 2026Open Access

Brief Communication: Borderline Trees in Point Sampling

Key Points

This work aims to evaluate the effect of borderline trees on the estimation of basal area in forest sampling using point sampling techniques.
Conducted a simulation to compare estimation precision with and without checking borderline trees.
Estimated basal area per unit area using point sampling methods.
Measured the bias introduced by tallying borderline trees as a time-saving measure.
Bias introduced by not checking borderline trees is generally small, typically less than 1%.
Precision in estimation can improve by approximately 10% when time is reallocated from checking borderline trees to measuring additional sample units.
Even with the potential bias, checking borderline trees remains a recommended practice in forest sampling.

Abstract

Abstract Point sampling is a popular technique used in forest sampling due to its speed. Users can obtain an estimate of basal area per unit area without leaving the sample unit center point. However, borderline trees are a common occurrence. That is, users are unable to discern on the instrument if a tree should be included on the sample unit. Rather than accurately determining sample status by comparing the measured horizontal distance to the limiting distance, users often simply tally every other borderline tree. This scheme saves time but may introduce bias. Through a simulation, this work demonstrates that bias is usually small (< 1%) but increases dramatically before the estimate reaches a theoretical maximum. If users can accept trivial bias, the simulation demonstrates that gains (~ 10%) in estimate precision can be obtained partially by allocating the time normally spent checking borderline trees to measuring more sample units. Despite these potential gains in precision, checking borderline trees is still good practice. If more precision is needed but time and resources are limited, it may be prudent not to check borderline trees but instead, design an inventory with more sample units.

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