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

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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

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To link to this article 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
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
Faculty of Science > School of Mathematical and Physical Sciences > Department of Meteorology
ID Code:36386
Publisher:IEEE Geoscience and Remote Sensing Society

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