Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDar Data

Andrew Michael Wallace, Caroline Nichol, Iain Woodhouse

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


We describe the use of Bayesian inference techniques, notably Markov chain
Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest
structural and biochemical parameters from multispectral LiDAR (Light Detection and
Ranging) data. We use a variable dimension, multi-layered model to represent a forest
canopy or tree, and discuss the recovery of structure and depth profiles that relate to
photochemical properties. We first demonstrate how simple vegetation indices such as the
Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and
light absorption, and Photochemical Reflectance Index (PRI) which is a measure of
vegetation light use efficiency, can be measured from multispectral data. We further
describe and demonstrate our layered approach on single wavelength real data, and on
simulated multispectral data derived from real, rather than simulated, data sets. This
evaluation shows successful recovery of a subset of parameters, as the complete recovery
problem is ill-posed with the available data. We conclude that the approach has promise,
and suggest future developments to address the current difficulties in parameter inversion.
Original languageEnglish
Pages (from-to)509-531
Number of pages23
JournalRemote Sensing
Issue number2
Publication statusPublished - 17 Feb 2012


  • laser radar
  • multispectral canopy LiDAR
  • forest structure and biochemistry
  • parameter inversion
  • Monte Carlo methods
  • Markov processes

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