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.
Highlights
Managers and ecologists must develop cost-effective techniques to evaluate fundamental forest attributes that influence forest health and productivity, such as stand structure and complexity, and canopy closure
Percent branch dieback was significantly different among Light detection and ranging (LiDAR) categories, with High understory Low understory (HH) and Low crown Low understory (LL) categories exhibiting greater dieback than the High crown Low understory (HL) category (Table 1)
Basal area of live understory and intermediate trees (DBH 2.5 - 12.5 cm) was significantly greater (P ≤ 0.05) on HH plots compared to all other LiDAR categories, with Low crown High understory (LH) plots exhibiting greater live basal area despite showing no statistical difference from HL and LL plots (Table 1)
Summary
Managers and ecologists must develop cost-effective techniques to evaluate fundamental forest attributes that influence forest health and productivity, such as stand structure and complexity, and canopy closure. Other studies have utilized LiDAR to quantify forest structure and canopy functional traits (i.e., normalized difference vegetation index—NDVI) across elevation gradients (Asner et al, 2014), as well as relating LiDAR-derived gap frequency and forest structure to edaphic and geological patterns (Higgins et al, 2015) These studies evaluated LiDAR at broad landscape-scales, with no ground-based measures for direct comparisons. By contrast, Gökkaya et al (2015) evaluated the utility of LiDAR in conjunction with spaceborne imaging spectroscopy in predicting ground-based measures of foliar macronutrients at the canopy level in mixed wood boreal forests Their analysis found that the inclusion of LiDAR data with imaging spectroscopy significantly improved prediction accuracy of canopy macronutrients. In forests characterized by high structural heterogeneity, Ewald et al (2018) compared ground-based measures of N and phosphorus to remotely-sensed data, finding that LiDAR-derived variables improved model predictions from imaging spectroscopy alone
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
