LiDAR remote sensing of forest canopy structure and its relationship to forest health and productivity in a northern hardwood forest

Background/Question/Methods

High-resolution LiDAR data (flown summer of 2009) were acquired for the Hubbard Brook Experimental Forest (HBEF) in New Hampshire.  LiDAR data were classified into four canopy structural categories: 1) high crown and understory closure, 2) high crown closure and low understory closure, 3) low crown closure and high understory closure, and 4) low crown and understory closure.  Nearby plots from each of the four LiDAR categories were grouped into “blocks” to assess the spatial consistency of data.  Ground-based measures of forest canopy structure (e.g., percent crown and understory closure) and forest health and productivity (e.g., xylem increment growth) were collected on nine 50m-plots from each LiDAR category (36 plots total), during summer of 2012.  These data were used to assess the ability of LiDAR to accurately quantify forest canopy structure, and determine the relationship between LiDAR and forest health and productivity.  Standard dendrochronological methods were used to collect and process cores from dominant and co-dominant sugar maple (Acer saccharum Marsh. - a species shown to be declining in growth at HBEF) trees to assess the relationship of LiDAR to forest health and productivity.  Xylem increment was converted to basal area increment (BAI) to evaluate growth trends.

Results/Conclusions

We found significant correspondence among LiDAR categories and ground-based measures of percent understory closure.  LiDAR categories with differing understory classification (e.g., high crown closure and high understory closure versus low crown closure and low understory closure) had different ground-based percent understory closure based on Tukey HSD tests.  In contrast, LiDAR categories and ground-based measures of percent crown closure showed no significant relationships.  Block was included as a source of variation in all statistical models and showed a significant relationship with ground-based measures of crown closure and all temporal measures of BAI assessed.  Mean BAI for 2009 and mean BAI for 1999-2008 were significantly different among LiDAR categories.  Significantly different BAI of dominant and co-dominant trees, and among LiDAR categories suggests varying regimes of competition and light availability.  However, because LiDAR is a static measure, and because block was commonly significant for BAI measures, we hypothesize that other site factors that we measured (e.g., foliar nutrition, soil moisture, arthropod abundance, etc.) likely influence growth dynamics of sugar maple at HBEF.