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Harvard Forest Research Project 2022

  • Title: The influence of canopy structure and foliar chemistry on remote sensing observations
  • Principal investigator: Scott Ollinger (
  • Institution: University of New Hampshire - Main Campus
  • Primary contact: Jack Hastings (
  • Team members: Kaitlyn Baillorgeon; Jessica Gersony; Jack Hastings; Scott Ollinger; Andrew Ouimette
  • Abstract:

    The biochemistry, physiology, and orientation of foliage is a key driver of productivity in forests, and has spawned multiple leaf trait remote sensing studies as a means of leaf-to-ecosystem scaling. Although these studies have demonstrated that leaf traits such as foliar nitrogen concentration (%N), leaf mass per unit area (LMA), and associated patterns of forest growth can be estimated from remotely sensed data, the mechanisms responsible remain unclear because, in whole canopies, leaf-level reflectance properties are confounded by the influence of structural variables at the leaf, stem and whole-crown scales. This produces tremendous complexity in the number, size, and spatial arrangement of reflecting leaf surfaces and canopy gaps, while also affecting radiative transfer through forest canopies.

    The goal of the proposed research is to understand linkages between canopy structure and leaf traits that influence photosynthetic capacity toward improving interpretation of remote sensing data and to better map/monitor rates of productivity in forest ecosystems. Specific objectives include: (a) Assess fine-scale leaf chemistry, leaf physiology, and canopy structure interactions by conducting baseline field measurements at two forested sites; (b) Examine linkages between leaf spectra and canopy structure, using existing data from UAS and airborne platforms, including high spatial and spectral resolution imagery and high point density LiDAR data; (c) Improve our understanding of radiative transfer (RT) within the forest canopy and examine the effects of leaf-canopy relationships resolved through objectives (a) and (b), by conducting RT model simulations; and (d) Determine how changes in chemical or structural leaf/vegetation traits influence the measured remote sensing signals by (i) performing a mechanistic, rigorous analysis of the coupling (and scaling) between structure and traits; and (ii) explaining spectral variability due to structural differences