Comparison of terrestrial and airborne LiDAR derived crown metrics for describing forest structure at Eglin Air Force Base, Florida, USA

Background/Question/Methods

Light Detection and Ranging (LiDAR) has been increasingly used in forest ecology and management. Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two LiDAR systems currently being used for remote sensing of forested ecosystems. The aim of this study was to compare crown metrics derived from TLS and ALS data for describing forest structure at Eglin Air Force Base, Florida, USA, where the longleaf pine (Pinus palustris Mill.) forest has an open canopy structure. Lidar data were collected over the study area, and represented by four (7854 m²) plots with coincident TLS and ALS scans. Canopy Height Models were created from TLS and ALS point clouds separately, and by combining them (TLS + ALS). Individual trees were detected from the CHMs and crown metrics, such as crown height (CTH), crown projected area (CA) and crown volume (CV) were computed for each tree using the rLiDAR package. In this study, forest structure was assessed by the number of trees and distribution of crown metrics derived from TLS and ALS. We used the results of the combination of TLS + ALS as the reference, and Kolmogorov-Smirnov tests were applied to compare crown metrics between TLS, ALS and TLS +ALS.

Results/Conclusions

TLS and ALS detected fewer trees when processed individually compared to ALS+TLS. The relative difference for the number of individual trees detected between TLS and ALS compared to the TLS+ALS were -2.68 and -23.08%. The mean of CH, CA and CV were 7.06 m, 3.65 m² and 15.37 m³ for TLS; 7.32 m, 6.13 m² and 20.87 m³ for ALS, and 7.64 m, 9.08 m² and 33.33 m³ for TLS + ALS. Kolmogorov-Smirnov tests showed that CH, CA and CV computed individually from TLS and ALS differed significantly (p-value < 0.05) from TLS + ALS. This confirmed our hypothesis that combining the points from the TLS and ALS would produce a higher level of accuracy, as it provides a denser point cloud from which to identify trees and derive crown metrics. The next step for evaluation of these data sets is to collect detailed ground data at these plots to compare with the LiDAR results, as currently field data have not been collected to get the actual tree counts and crown attributes.