Airborne LiDAR and Terrestrial Laser Scanning Derived Vegetation Obstruction Factors for Visibility Models

Research presented here explores the feasibility of leveraging vegetation data derived from airborne light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) for visibility modeling. Using LiDAR and TLS datasets of a lodgepole pine (Pinus contorta) dominant ecosystem, tree canopy and trunk obstructions were isolated relevant to a discrete visibility beam in a short-range line-of-sight model. Cumulative obstruction factors from vegetation were compared with reference visibility values from digital photographs along sightline paths. LiDAR-derived tree factors were augmented with single-scan TLS data for obstruction prediction. Good correlation between datasets was found up to 10 m from the terrestrial scanner, but fine scale visibility modeling was problematic at longer distances. Analysis of correlation and regression results reveal the influence of obstruction shadowing inherent to discrete LiDAR and TLS, potentially limiting the feasibility of modeling visibility over large areas with similar technology. However, the results support the potential for TLS-derived subcanopy metrics for augmenting large amounts of aerial LiDAR data to significantly improve models of forest structure. Subtle LiDAR processing improvements, including more accurate tree delineation through higher point density aerial data, combined with better vegetation quantification processes for TLS data, will advance the feasibility and accuracy of data integration.