Characterizing individual tree‐level snags using airborne lidar‐derived forest canopy gaps within closed‐canopy conifer forests

Abstract

Abstract Airborne lidar is often used to calculate forest metrics about trees, but it may also provide a wealth of information about the space between trees. Forest canopy gaps are defined by the absence of vegetative structure and serve important roles for wildlife, such as facilitating animal movement. Forest canopy gaps also occur around snags, keystone structures that provide important substrates to wildlife species for breeding, roosting and foraging. We wanted to test a method for quantifying canopy gaps around individual snags and live trees, with the working hypothesis that snags would have more gaps surrounding them overall than live trees. We evaluated canopy gaps around individual snags (n = 270) and live trees (n = 2,186) and evaluated correlations between canopy structure and snag occurrence in dense conifer stands of the Idaho Panhandle National Forest, United States. We paired airborne lidar with ground reference data collected at fixed‐radius plots (n = 53) to evaluate local gap structure. The r package ForestGapR was used to quantify canopy gaps throughout the canopy to determine where the differences were greatest. A canopy space profile was created for each tree by mapping gaps (a) vertically every 2 m in height (2–50 m above‐ground), and (b) horizontally across small (16 m2), medium (36 m2) and large (64 m2) footprint sizes. Our results suggest that this method is robust for quantifying canopy gaps around individual trees. The canopy space profiles were distinctly different for snags and live trees, with more canopy gaps within the area surrounding snags relative to live trees. The greatest differences occurred at mid‐canopy heights (~20 m above‐ground) and at the smallest footprint size (16 m2). These results show potential to improve the understanding of gap dynamics in closed‐canopy conifer forests, and we suggest that snag modelling could be improved by incorporating lidar‐derived canopy gap analyses alongside existing methodologies.