Waveform lidar over vegetation: an evaluation of inversion methods for estimating return energy

Hancock, Steven, Armston, John, Li, Zhan, Gaulton, Rachel, Lewis, Philip, Disney, Mathias, Danson, F. Mark, Strahler, Alan, Schaaf, Crystal, Anderson, Karen and Gaston, Kevin J. (2015) Waveform lidar over vegetation: an evaluation of inversion methods for estimating return energy. Remote Sensing of Environment, 164 208-224. doi:10.1016/j.rse.2015.04.013


Author Hancock, Steven
Armston, John
Li, Zhan
Gaulton, Rachel
Lewis, Philip
Disney, Mathias
Danson, F. Mark
Strahler, Alan
Schaaf, Crystal
Anderson, Karen
Gaston, Kevin J.
Title Waveform lidar over vegetation: an evaluation of inversion methods for estimating return energy
Journal name Remote Sensing of Environment   Check publisher's open access policy
ISSN 0034-4257
1879-0704
Publication date 2015-07
Sub-type Article (original research)
DOI 10.1016/j.rse.2015.04.013
Open Access Status DOI
Volume 164
Start page 208
End page 224
Total pages 17
Place of publication New York, NY, United States
Publisher Elsevier
Collection year 2016
Language eng
Abstract Full waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33 cm and 1 cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪ 15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design.
Keyword Lidar
Full waveform
Forests
Reflectance
Signal processing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2016 Collection
 
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