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McRoberts, R. E., Magnussen, S., Tomppo, E. O., & Chirici, G. (2011). Parametric, Bootstrap, and Jackknife Variance Estimators for the k-Nearest Neighbors Technique with Illustrations Using Forest Inventory and Satellite Image Data. Remote Sensing of Environment, 115, 3165-3174. https://doi.org/10.1016/j.rse.2011.07.002

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• TITLE: Normal and Bootstrap Confidence Intervals in Bitterlich Sampling

  AUTHORS: Georgios Stamatellos, Aristeidis Georgakis

  KEYWORDS: Simulation, Sample Size, Forest Management, Fir Forest

  JOURNAL NAME: Open Journal of Forestry, Vol.10 No.1, December 13, 2019

  ABSTRACT: 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 (Cα) and the bias-corrected and accelerated method (BCα). 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 BCα method. At sample size 40, the coverage of the three confidence intervals is higher than the nominal coverage (95%).