Exploring the Measurement of Forests with Full Waveform Lidar through Monte-carlo Ray Tracing

This paper presents results from two simulation studies which attempt to measure forest height with full waveform lidar. MonteCarlo ray tracing is used to simulate a full waveform lidar response over explicitly represented 3D forest models. Gaussian decomposition and multi-spectral edge detection are used to estimate tree top and ground positions over a range of forest ages, stand densities and ground slopes. The error of the height estimates are precisely quantified by comparison with the 3D model height. This paper discusses the development and testing of the inversion of tree height from simulations of lidar data, assuming a fixed set of lidar characteristics (corresponding to an LVIS-like instrument). It is shown that Gaussian decomposition performs reasonably well (mean 5.3m overestimate for <75% cover) for all but the densest canopies over flat ground. The potential of multi-spectral edge detection to separate ground and canopy returns from blurred waveform is demonstrated. The methods presented will be refined and extended to instrument-specific cases.