This study presents an easy-to-apply variable probability sample design that is an efficient and cost-effective method to correct for local bias in regional LiDAR-assisted forest inventory estimates. This design is especially useful for small woodlot owners.
Context Light detection and ranging (LiDAR)-derived forest inventory estimates are generally unbiased at landscape levels but may be biased locally. One solution to correct local bias is to use ground-based double sampling with ratio estimation where the LiDAR estimates form the large sample covariate and the ground plots are used to estimate a correction or calibration ratio.
Aims Our objectives were to test the performance of different sample strategies, to correct for local bias, and to determine the most efficient and cost-effective sampling design.
Methods We compared five sample selection methods and four plot types using simulation. Sample sizes and inventory costs required to achieve 5% standard error were calculated to assess sampling efficiency.
Results The results showed that bias can be corrected successfully using a doubling sampling approach with ratio estimation, and that variable probability selection methods were more efficient than equal probability selection methods. A big basal area factor (BAF) plot was the most cost-effective on-the-ground plot type.
Conclusion The most efficient and cost-effective sampling design was list sampling with big BAF plots. This combination can be used to calibrate LiDAR-derived forest inventory estimates for a variety of forest attributes.
LiDAR-assisted inventory, Variable probability sampling, Big BAF sampling, Ratio estimation, Sampling with covariates, Sampling to correct
Hsu, Y., Chen, Y., Yang, T. et al. Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots. Annals of Forest Science 77, 75 (2020). https://doi.org/10.1007/s13595-020-00976-8
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The datasets generated during and/or analyzed during the current study are available in the University of New Brunswick’s DataVerse data archive, https://doi.org/10.25545/Z8WRBJ