Developing Hyperspectral LiDAR for Structural and Biochemical Analysis of Forest Data

Daniel Sergio Martinez Ramirez, Gerald Stuart Buller, Aongus McCarthy, Simone Morak, Caroline J. Nichol, Ximing Ren, Andrew Michael Wallace, Iain H. Woodhouse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Single wavelength LiDAR has been used successfully for recovering structural data from forest canopies (Leuwen 2010). However, multispectral canopy LiDAR (F Morsdorf 2009) can also provide information on the vertical distribution of physiological processes which informs on actual carbon sequestration as well as existing stocks, and can disambiguate ground from canopy returns. This is critical to better understand and predict the impact of climate change, and to understand the seasonal dynamics of ecosystem carbon uptake in response to environmental drivers such as water, temperature, light and nutrient availability. We report progress to develop multispectral and hyperspectral LiDAR systems to recover structural and physiological data. This includes innovations in instrumentation, specifically the development of time-correlated photon counting LiDAR systems (Buller 2007) to record full waveform depth profiles at many wavelengths, and in processing that data to recover the necessary forest canopy parameters. To that end we develop further a variable dimension structural model coupled to spectral simulation using the PROSPECT model.
In general the problem of parameter inversion in a four dimensional data space (x,y,z,) is ill-posed, due to the variation in both PROSPECT parameters and material abundance, which leads us to consider the use of a greater diversity of wavelength to better constrain the problem. We evaluate both instrumental performance and parameter inversion using simulated data and by a series of
measurements on conifer samples, for which we make separate manual measurements of structure and physiology to provide ground truth. A comparison of the LiDAR-derived parameters with the ground truth shows the potential for accurate structural and physiological recovery when the number
of parameters is constrained, and leads us to make recommendations on the future development of multiple wavelength LiDAR systems in this context.
Original languageEnglish
Title of host publicationProceedings of 32nd EARSeL Symposium and 36th General Assembly
Pages59-60
Number of pages2
Publication statusPublished - May 2012
Event36th EARSeL General Assembly and 32nd Annual Symposium - Mykonos, Greece
Duration: 21 May 201224 May 2012

Conference

Conference36th EARSeL General Assembly and 32nd Annual Symposium
CountryGreece
CityMykonos
Period21/05/1224/05/12

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    Martinez Ramirez, D. S., Buller, G. S., McCarthy, A., Morak, S., Nichol, C. J., Ren, X., Wallace, A. M., & Woodhouse, I. H. (2012). Developing Hyperspectral LiDAR for Structural and Biochemical Analysis of Forest Data. In Proceedings of 32nd EARSeL Symposium and 36th General Assembly (pp. 59-60)