Effective LAI and CHP of a Single Tree From Small-Footprint Full-Waveform LiDAR

This letter has tested the canopy height profile (CHP) methodology as a way of effective leaf area index (\hbox{LAI}-{\rm e}) and vertical vegetation profile retrieval at a single-tree level. Waveform and discrete airborne LiDAR data from six swaths, as well as from the combined data of six swaths, were used to extract the LAI_{\rm e} of a single live Callitris glaucophylla tree. LAI_{\rm e} was extracted from raw waveform as an intermediate step in the CHP methodology, with two different vegetation-ground reflectance ratios. Discrete point LAI_{\rm e} estimates were derived from the gap probability using the following: 1) single ground returns and 2) all ground returns. LiDAR LAI_{\rm e} retrievals were subsequently compared to hemispherical photography estimates, yielding mean values within \pm7% of the latter, depending on the method used. The CHP of a single dead Callitris glaucophylla tree, representing the distribution of vegetation material, was verified with a field profile manually reconstructed from convergent photographs taken with a fixed-focal-length camera. A binwise comparison of the two profiles showed very high correlation between the data reaching {\rm R} ² of 0.86 for the CHP from combined swaths. Using a study-area-adjusted reflectance ratio improved the correlation between the profiles, but only marginally in comparison to using an arbitrary ratio of 0.5 for the laser wavelength of 1550 nm.