LAUSR

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. 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. 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. 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. 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. 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.