Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots
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RESEARCH PAPER
Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots Yung-Han Hsu 1
&
Yingbing Chen 1 & Ting-Ru Yang 1 & John A. Kershaw Jr. 1 & Mark J. Ducey 2
Received: 15 December 2019 / Accepted: 16 June 2020 # INRAE and Springer-Verlag France SAS, part of Springer Nature 2020
Abstract & Key message This study presents an easy-to-apply variable probability sample design that is an efficient and costeffective 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. Keywords LiDAR-assisted inventory . Variable probability sampling . Big BAF sampling . Ratio estimation . Sampling with covariates . Sampling to correct
Handling Editor: Jean-Michel Leban Authors’ contributions Conceptualization: John A. Kershaw; Formal Analysis: Yung-Han Hsu; Writing—original draft: Yung-Han Hsu; Writing—review and editing: John A. Kershaw, Ting-Ru Yang, Yingbing Chen, Mark Ducey; Supervision: John Kershaw * Yung-Han Hsu [email protected] Yingbing Chen [email protected] Ting-Ru Yang [email protected] John A. Kershaw, Jr. [email protected]
Mark J. Ducey [email protected]
1
Faculty of Forestry and Environment Management, University of New Brunswick, PO Box 4400, Fredericton, NB E3B 5A3, Canada
2
Department of Natural Resources and the Environment, University of New Hampshire, 114 James Hall, 56 College Road, Durham, NH 03824, USA
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1 Introduction Airborne light detection and ranging (LiDAR) scanning (ALS) is a remote sensing technique that can provide threedimensional spatial information about forest canopies and the underlying terrain. Based on this information, ALS is used to
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