Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

Remote Sensing

Andrew Wallace²  Caroline Nichol¹ 

[1] School of GeoSciences, Crew Building, University of Edinburgh, West Mains Road, Edinburgh EH9 3JN, UK; E-Mails:;Joint Research Institute in Signal and Image Processing, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK

关键词: laser radar;    multispectral canopy LiDAR;    forest structure and biochemistry;    parameter inversion;    Monte Carlo methods;    Markov processes;   

DOI  :  10.3390/rs4020509

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.