How important is individual tree information for biomass modeling and mapping?

Earth's forests represent one of the largest carbon stores on the planet. Mapping forest carbon stock accurately is critical for improving our understanding of carbon cycling and for forest management. LiDAR remote sensing has become the premier technology for accurately measuring and mapping forest biomass. However, there is more information content in high-resolution LiDAR datasets than has been systematically used for biomass modeling to date. LiDAR datasets are typically processed to provide measures of maximum and average height and various density profiles. These methods ignore the detailed spatial distribution of LiDAR returns that can be used to locate and characterize individual trees. This research applies a watershed-based tree detection algorithm to high-resolution LiDAR datasets and uses statistics describing the distribution of tree size as an additional input to more traditional LiDAR-based biomass models. This research includes analysis from a wide variety of both natural and managed forest systems, including the Sierra Nevada, California, Hubbard Brook, New Hampshire, Parker Track, North Carolina, and the Smithsonian Environmental Research Center, Maryland.