LiDAR Helps Differentiate Stand Health and Productivity Levels within a Northern Hardwood Forest

Abstract

Light detection and ranging (LiDAR) data can provide detailed information about three-dimensional forest structure. However, links between forest structure and tree function have not been fully evaluated using LiDAR. We assessed the relationship of LiDAR-derived structural categories to tree health and productivity on 36 hardwood plots at the Hubbard Brook Experimental Forest, New Hampshire, USA. We established nine plot replicates for each of four LiDAR-based vegetation categories: 1) high crown and high understory closure; 2) high crown and low understory closure; 3) low crown and high understory closure; and 4) low crown and low understory closure. Ground-based measures of canopy structure, site, stand and individual tree measures were collected on plots during summer 2012. Significant differences among LiDAR categories were found for several response variables. Lower basal area increment for sugar maple (Acer saccharum), decreased foliar nutrition for yellow birch (Betula alleghaniensis), and lower overall crown health were all associated with high understory closure provided that overstory closure was also high. These results suggest that LiDAR measures can be used to assess competitive interactions between overstory and understory vegetation, and that LiDAR shows promise for identifying stands with reduced health and productivity due to factors such as competition or overstocking.