Charactering understory structure with multi-band LiDAR data.

Understory vegetation is an important component of the boreal forest, providing wildlife habitat, influencing nutrient cycling, forest succession, and fire regimes. Previous studies have quantified understory structure using single-band LiDAR, which collects data at a single wavelength. However, modern LiDAR sensors such as the Optech Titan enable multi-band data collection from a single platform over a variety of wavelengths. These data provide enhanced opportunity to characterize understory plant structure and species composition, through generation of spectral indices such as GNDVI. In this study, we assessed the ability of single- and multi-band LiDAR to capture a variety of understory structural attributes in the boreal forest of northern Alberta, Canada. Regression models were generated in order to identify correlations between LiDAR metrics and understory structural attributes measured in the field. The results of this study show that multi-band LiDAR provides more information about the plant community of the understory vegetation, compared to single-band LiDAR. However, single-band LiDAR had greater accuracy in determining the physical structural characteristics due to higher point density. The accuracy of models generated is highest in open areas with low canopy cover, and decreases under dense-canopy conditions. Our work demonstrates the value of multi-spectral LiDAR to researchers and stakeholders working on topics of forest ecology, restoration, and forest resource management, and explores the technical and processing differences between the two technologies.