Abstract
Vegetation canopy structure is a critically important
habit characteristic for many threatened and endangered birds
and other animal species, and it is key information needed
by forest and wildlife managers for monitoring and managing
forest resources, conservation planning and fostering biodiversity.
Advances in Light Detection and Ranging (LiDAR) technologies
have enabled remote sensing-based studies of vegetation canopies
by capturing three-dimensional structures, yielding information
not available in two-dimensional images of the landscape pro-
vided by traditional multi-spectral remote sensing platforms.
However, the large volume data sets produced by airborne LiDAR
instruments pose a significant computational challenge, requiring
algorithms to identify and analyze patterns of interest buried
within LiDAR point clouds in a computationally efficient manner,
utilizing state-of-art computing infrastructure. We developed
and applied a computationally efficient approach to analyze a
large volume of LiDAR data and to characterize and map the
vegetation canopy structures for 139,859 hectares (540 sq. miles)
in the Great Smoky Mountains National Park. This study helps
improve our understanding of the distribution of vegetation and
animal habitats in this extremely diverse ecosystem.
