https://dx.doi.org/10.1016/j.foreco.2017.10.005">
 

Title

Comparison of lidar- and allometry-derived canopy height models in an eastern deciduous forest

Abstract

Tree crown geometry and height, especially when coupled with remotely sensed data, can aid in the characterization of tree and forest structure. In this study, we develop mixed-effects model allometric equations for tree height, crown radius, and crown depth using data collected on 374 trees across 14 species within the extent of the joint Center for Tropical Forest Science (CTFS) and Smithsonian Institute’s Forest Global Earth Observatory (ForestGEO) MegaPlot on Prospect Hill at Harvard Forest, Massachusetts. We applied allometry to a census of the 35-ha plot on Prospect Hill to evaluate tree height and crown radius estimates using a lidar canopy height model. We found significant relationships using stem diameter-at-breast-height (DBH) and species to estimate tree height (ρr2 = 0.70, RMSE = 2.96 m), crown depth (ρr2 = 0.35, RMSE = 3.24 m) and crown radius (ρr2 = 0.43, RMSE = 1.22 m). Using Fast Fourier Transforms (FFTs), we compared the power spectra of a lidar canopy height model to five synthetic canopy height models derived from allometric estimates of height and crown radius. The FFTs showed good agreement between lidar and synthetic canopy height models (CHMs) at spatial wavelengths longer than 64 m, or about the distance across 3–4 dominant tree crowns, and poorer agreement at shorter spatial wavelengths, which we attribute to the simple crown shape applied to modeled crowns and a lack of crown overlap in the synthetic CHMs compared to the lidar CHM. At the tree level, some species exhibited tight links between lidar-measured height and estimated tree height (e.g., Quercus rubra, Quercus velutina, Pinus strobus), suggesting height allometry provided reasonable estimates of tree height for some species despite a negative bias in the synthetic canopy height models relative to the lidar canopy height model.

Publication Date

12-15-2017

Journal Title

Forest Ecology and Management

Publisher

Elsevier

Digital Object Identifier (DOI)

https://dx.doi.org/10.1016/j.foreco.2017.10.005

Document Type

Article

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