Author(s)

Georgios Stamatellos, Aristeidis Georgakis^*

Laboratory of Forest Biometrics, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessalo-niki, Greece.

The Bitterlich Sampling (horizontal point sampling) is a common method in forest inventories. By this method, the Horvitz-Thompson estimator is used in a number of independent sampling points for the estimation of overall tree volume in a forest area/stand. In this paper, confidence intervals are constructed and evaluated using the normal approach and two bootstrap methods; the percentile method (C_α) and the bias-corrected and accelerated method (BC_α). The simulation results show that the normal confidence interval has better coverage of true value at sample size 10. At sample sizes 20 and 30, it seems that there are no substantial differences in coverage between confidence intervals, although it could be noted a small superiority of BC_α method. At sample size 40, the coverage of the three confidence intervals is higher than the nominal coverage (95%).

Simulation, Sample Size, Forest Management, Fir Forest

Stamatellos, G. and Georgakis, A. (2020) Normal and Bootstrap Confidence Intervals in Bitterlich Sampling. Open Journal of Forestry, 10, 58-65. doi: 10.4236/ojf.2020.101005.