Design and Evaluation of Multispectral LiDAR for the Recovery of Arboreal Parameters

A.M. Wallace, Aongus McCarthy, Caroline Nichol, Ren Ximing, Simone Morak, Daniel Martinez-Ramirez, Iain Woodhouse, Gerald Buller

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Multispectral light detection and ranging (LiDAR) has the potential to recover structural and physiological data from arboreal samples and, by extension, from forest canopies when deployed on aerial or space platforms. In this paper, we describe the design and evaluation of a prototype multispectral LiDAR system and demonstrate the measurement of leaf and bark area and abundance profiles using a series of experiments on tree samples “viewed from above” by tilting living conifers such that the apex is directed on the viewing axis. As the complete recovery of all structural and physiological parameters is ill posed with a restricted set of four wavelengths, we used leaf and bark spectra measured in the laboratory to constrain parameter inversion by an extended reversible jump Markov chain Monte Carlo algorithm. However, we also show in a separate experiment how the multispectral LiDAR can recover directly a profile of Normalized Difference Vegetation Index (NDVI), which is verified against the laboratory spectral measurements. Our work shows the potential of multispectral LiDAR to recover both structural and physiological data and also highlights the fine spatial resolution that can be achieved with time-correlated single-photon counting
Original languageEnglish
Pages (from-to)4942
Number of pages4954
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number8
Early online date21 Nov 2013
Publication statusPublished - 1 Aug 2014


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