Radiometrically Calibrated Features of Full-Waveform Lidar Point Clouds Based on Statistical Moments

Roncat, Andreas; Briese, Christian; Jansa, Josef; Pfeifer, Norbert

doi:10.1109/lgrs.2013.2274557

Cited by 19 publications

(19 citation statements)

References 25 publications

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“…Some interpretation will be needed to identify the location and distribution of individual targets, especially when the system pulse causes their waveforms to overlap. This can be achieved by function fitting (Hofton et al, 2000), by examining turning points, which is a necessary step to choose initial estimates for function fitting or by deconvolution (Hancock et al, 2008;Roncat et al, 2014). An assessment of the relative accuracies of these will form a future paper.…”

Section: Discussionmentioning

confidence: 99%

“…Function fitting (GAU, GGA, LOG and SPL after deconvolution) gives easily understandable metrics (location, energy and width for each feature) that can be used for classifying vegetation Roncat, Briese, Jansa, & Pfeifer, 2014). However these can also be derived from turning points in the waveform when using the sum and this identification of turning points, peaks and widths is a necessary step in order to choose initial estimates required for function fitting.…”

Section: Diffuse Target Conclusionmentioning

confidence: 99%

“…However these can also be derived from turning points in the waveform when using the sum and this identification of turning points, peaks and widths is a necessary step in order to choose initial estimates required for function fitting. Alternatively deconvolution could be used to retrieve the target profile (Hancock et al, 2008;Roncat et al, 2014). The relative accuracies of the resulting metrics (location and width) need to be assessed and will form a future investigation, but for return energy (and so target reflectance) these conclusions will still hold and it is possible to use different algorithms for range and energy.…”

Section: Diffuse Target Conclusionmentioning

confidence: 99%

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

Hancock

Armston

et al. 2015

Remote Sensing of Environment

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a b s t r a c tFull 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.

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Section: Discussionmentioning

confidence: 99%

Section: Diffuse Target Conclusionmentioning

confidence: 99%

Section: Diffuse Target Conclusionmentioning

confidence: 99%

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

Hancock

Armston

et al. 2015

Remote Sensing of Environment

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show abstract

“…The de-convolution is linked with the radiometric calibration presented in Section 1.2.3 (Wagner 2010). The most common way to process WF data is to fit Gaussian (Hofton et al 2000;Wagner et al 2006) or some other functions (Mallet et al 2010;Roncat et al 2014a) to the WFs. It then becomes possible to calculate the locations (peaks) and additional attributes for each echo.…”

Section: Interpretation and Use Of Waveform Lidar Datamentioning

confidence: 99%

“…The bulk of the literature uses Gaussian WF decomposition, because software tools are available for the task (Roncat et al 2014b). Other functions can also be fitted to the WF (Mallet et al 2010;Roncat et al 2014a). In vegetation, the WF is always a sum of the signals from several distinct scatterers that overlap due to their convolution with the emitted pulse.…”

Section: Wf Features and Their Use In Classification (Objectives IV Amentioning

confidence: 99%

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

Hovi¹

2015

Diss. For.

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Airborne LiDAR Data Processing

Mallet¹,

Chehata²,

Bailly³

2016

Optical Remote Sensing of Land Surface

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Radiometrically Calibrated Features of Full-Waveform Lidar Point Clouds Based on Statistical Moments

Cited by 19 publications

References 25 publications

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

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

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

Airborne LiDAR Data Processing

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