Uncertainty within satellite LiDAR estimations of vegetation and topography

Abstract

This paper demonstrates the ability to identify representative ground elevation and vegetation height estimates within the Ice, Cloud and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) waveforms for an area of mixed vegetation and varied topography. Estimating vegetation height within large-footprint Light Detection and Ranging (LiDAR) waveforms relies on the ability to estimate the uppermost canopy surface (signal beginning) and an elevation representing the ground surface, both of which are influenced by vegetation properties and topographic slope. We examined sources of uncertainty for vegetation height estimation from ICESat/GLAS data using airborne LiDAR data, field measurements and the FLIGHT radiative transfer model. In comparison with an independent 10-m resolution digital terrain model (DTM), a method using Gaussian decomposition of the satellite waveform produced a mean bias of −0.10 m when estimating ground elevation. A second method of estimating vegetation height using waveform extent and a terrain index effectively removed slope as an error source but produced a greater ground surface offset (−0.83 m). The two methods of estimating vegetation height compared well with airborne LiDAR estimates (correlation coefficient (R ²) = 0.68, root mean square error (RMSE) = 4.4 m and R ² = 0.61, RMSE = 4.9 m, respectively). However, the complex interplay of the structural and optical properties of the intercepted vegetation and slope requires further understanding. A tool such as FLIGHT provides a useful means to explore the sensitivity of the waveform to both vegetation properties and topographic slope.

Acknowledgements

Many thanks are due to John Armston, Queensland Remote Sensing Centre, Indooroopilly, Australia, for kindly allowing use of the in-house LiDAR processing software. The Forestry Commission Forest Research Agency is also acknowledged for use of a subset of airborne LiDAR data, the Subcompartment Database, and the Forest Research Environmental Database for the Forest of Dean. This research is funded by the UK Natural Environment Research Council (NERC). We also thank the anonymous reviewers for their helpful comments and suggestions.