Evaluating Prospects for Improved Forest Parameter Retrieval From Satellite LiDAR Using a Physically-Based Radiative Transfer Model

A space-based full-waveform LiDAR system, optimised for vegetation analysis, offers the opportunity for global biophysical parameter retrieval of the world's forests. However the conditions under which signals from the ground and vegetation can be detected will vary as a result of sensor specifications, vegetation characteristics and underlying surface properties. This paper demonstrates the utility of a ray tracing radiative transfer model for assessing sensitivity to site-specific conditions (e. g., topography, canopy and ground reflectance) that will improve our ability to estimate structural parameters in forest ecosystems. Specifications for the LiDAR instrument planned for NASA's Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission, a vegetation-focused mission that was cancelled in 2011, were used to explore the effect of slope on the estimation of vegetation height. Slope was a limitation for NASA's previous satellite LiDAR mission, ICESat, which used a large, 70 m footprint, designed primarily for cryospheric applications. Simulations with the FLIGHT model suggested that the smaller footprint (similar to 25 m diameter) would enable ground to be reliably identified with automated peak-finding algorithms for canopy cover of 77% and slopes up to 30 degrees. The challenging objective of detecting a ground signal for almost complete canopy closure of 98% was achieved in the simulations for slopes up to 10 degrees. These results suggest that a satellite LiDAR instrument optimised for vegetation analysis will provide good estimates of vegetation height for all but the most extreme forest environments. The reduced footprint diameter in comparison with the ICESat instrument, GLAS, and continuous along-track sampling will provide a unique dataset to allow improved confidence of the distribution of forest parameters.