Effective LAI and CHP of a single tree from small-footprint full-waveform LiDAR

Fieber, K. D., Davenport, I. J., Tanase, M. A., Ferryman, J. M., Gurney, R. J., Walker, J. P. and Hacker, J. M. (2014) Effective LAI and CHP of a single tree from small-footprint full-waveform LiDAR. IEEE Geoscience and Remote Sensing Letters, 11 (9). 1634 -1638. ISSN 1545-598X doi: 10.1109/LGRS.2014.2303500

Abstract/Summary

This letter has tested the canopy height profile (CHP) methodology as a way of effective leaf area index (LAIe) 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 LAIe of a single live Callitris glaucophylla tree. LAIe was extracted from raw waveform as an intermediate step in the CHP methodology, with two different vegetation-ground reflectance ratios. Discrete point LAIe estimates were derived from the gap probability using the following: 1) single ground returns and 2) all ground returns. LiDAR LAIe retrievals were subsequently compared to hemispherical photography estimates, yielding mean values within ±7% 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 R2 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/36386
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	IEEE Geoscience and Remote Sensing Society
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