Within a forest canopy, plant productivity depends on two suites of factors: 1) the functional diversity of plants at the level corresponding to functional type, species, individual, or leaf, and 2) the physical structure or canopy architecture, and therefore the within-canopy light environment, of the forest. Predictive models of plant productivity, from ‘big leaf’ to multi-layer, as well as demographic models, all assume that photosynthetic rates can be lumped into generalized classes (‘plant functional types’). Moreover, these models assume that the explicit handling of three-dimensional forest structure is not essential to estimating productivity. Yet we know these assumptions are inadequate. Important plant functional traits like foliar nitrogen concentrations ([N]L) and leaf mass per area (LMA) can vary significantly within a single species and through a canopy, where the amount of light a leaf receives is not due to its general canopy position but the locations of the leaves and branches that surround it.
Here we describe an emerging view of forests - as a collection of voxels, each with its own functional traits, leaf area density, and light environment. This voxelized view of forests allows for better connections between ecosystem ecology, landscape ecology, and remote sensing.
Results/Conclusions
Using data from NEON sites in the eastern United States, we show that airborne lidar and imaging spectroscopy can be used to build voxelized models of forests, which can then be incorporated into 3D radiative transfer models to estimate the forest light environment and photosynthetic rates. We compare estimates of forest productivity using both light use efficiency and eddy covariance. Our overall goal is to quantify the importance of 3D structure and function in estimates of forest carbon assimilation.