A new composite k-tree estimator of stem density

摘要

This study presents a generally applicable and robust k-tree composite estimator of density. We propose toestimate stem density by a weighted average of 16individual density estimators. The weights given to individual estimators are inversely proportional to the relative fit (Akaike's corrected information criterion) of each estimator to the assumed distribution of observed k-tree distances. The performance of the proposed estimator is evaluated in simulated simple random sampling with k = 3 and 6 in 58 forest stands (54 actual and 4 simulated) and 600 replications. Sample sizes were 15 and 30 locations per stand. Eleven estimators were novel, including three designed for regular spatial patterns. Absolute stand-level bias with k = 6 varied from 0.1 to 8.1% (mean 1.8%), and a bias larger than 6% was limited to 3 stands with either pronounced density gradients or a strong clustering of stem locations. Root mean squared errors were approximately 16% (k = 6 and n — 15) versus 12% for sampling with comparable fixed-area plots. Coverage of computed 95% confidence intervals ranged from 0.72 to 0.99 (median = 0.98 with n = 15 and 0.95 with n = 30), with 98% of all intervals achieving a coverage of 0.85 or better. In seven stands used in an assessment of a novel spatial point pattern reconstruction k-tree density estimator (RDE) by Nothdurft et al. (Can J For Res 40:953-967, 2010), the average absolute bias of with k = 6 was 1.5 versus 0.7% for .