The effect of plot co-registration error on the strength of regression between LiDAR canopy metrics and total standing volume in a Pinus radiata forest

• Main Author: Slui, Benjamin Thomas
• Language: en
• Published: University of Canterbury. School of Forestry 2015
• Subjects: LiDAR; Plot co-registration error; Pinus radiata; radiata pine; LiDAR-based forest inventory
• Online Access: http://hdl.handle.net/10092/10460

Background: The objective of this study was to verify the effect that plot locational errors, termed plot co-registration errors, have on the strength of regression between LiDAR canopy metrics and the measured total standing volume (TSV) of plots in a Pinus radiata forest. Methods: A 737 hectare plantation of mature Pinus radiata located in Northern Hawkes Bay was selected for the study. This forest had been measured in a pre-harvest inventory and had aerial LiDAR assessment. The location of plots was verified using a survey-grade GPS. Least square linear regression models were developed to predict TSV from LiDAR canopy metrics for a sample of 204 plots. The regression strength, accuracy and bias was compared for models developed using either the actual (verified) or the incorrect (intended) locations for these plots. The change to the LiDAR canopy metrics after the plot co-registration errors was also established. Results: The plot co-registration error in the sample ranged from 0.7 m to 70.3 m, with an average linear spatial error of 10.6 m. The plot co-registration errors substantially reduced the strength of regression between LiDAR canopy metrics and TSV, as the model developed from the actual plot locations had an R2 of 44%, while the model developed from the incorrect plot locations had an R2 of 19%. The greatest reductions in model strength occurred when there was less than a 60% overlap between the plots defined by correct and incorrect locations. Higher plot co-registration errors also caused significant changes to the height and density LiDAR canopy metrics that were used in the regression models. The lower percentile elevation LiDAR metrics were more sensitive to plot co- registration errors, compared to higher percentile metrics. Conclusion: Plot co-registration errors have a significant effect on the strength of regressions formed between TSV and LiDAR canopy metrics. This indicates that accurate measurements of plot locations are necessary to fully utilise LiDAR for inventory purposes in forests of Pinus radiata.