Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory

Forest Ecology and Management
By: , and 



Recently airborne Light Detection And Ranging (LiDAR) has emerged as a highly accurate remote sensing modality to be used in operational scale forest inventories. Inventories conducted with the help of LiDAR are most often model-based, i.e. they use variables derived from LiDAR point clouds as the predictive variables that are to be calibrated using field plots. The measurement of the necessary field plots is a time-consuming and statistically sensitive process. Because of this, current practice often presumes hundreds of plots to be collected. But since these plots are only used to calibrate regression models, it should be possible to minimize the number of plots needed by carefully selecting the plots to be measured. In the current study, we compare several systematic and random methods for calibration plot selection, with the specific aim that they be used in LiDAR based regression models for forest parameters, especially above-ground biomass. The primary criteria compared are based on both spatial representativity as well as on their coverage of the variability of the forest features measured. In the former case, it is important also to take into account spatial auto-correlation between the plots. The results indicate that choosing the plots in a way that ensures ample coverage of both spatial and feature space variability improves the performance of the corresponding models, and that adequate coverage of the variability in the feature space is the most important condition that should be met by the set of plots collected.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory
Series title Forest Ecology and Management
DOI 10.1016/j.foreco.2012.12.019
Volume 292
Year Published 2013
Language English
Publisher Elsevier
Publisher location Amsterdam, Netherlands
Contributing office(s) Southwest Biological Science Center
Description 11 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Forest Ecology and Management
First page 75
Last page 85
Country United States