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

Abstract

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.

Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data

“…In contrast, small-footprint full-waveform ALS systems have become available commercially since 2004 [28]; these systems provide new opportunities for forestry studies. The full-waveform systems digitize and record the entire backscattered signal of each emitted pulse, and allow the recording of geometric and physical properties of intercepted objects [29]. The FWF systems have advantages with regard to not limiting the number of returns for each laser pulse; providing additional investigation possibilities, e.g., point cloud density can be enhanced by processing the FWF return signal; and additional metrics can be extracted by modeling the received waveforms [30].…”

Ecological connectivity in the three-dimensional urban green volume using waveform airborne lidar

“…The aircraft was carrying a lidar sensor (Leica ALS50-II) able to capture full-waveform data1819 and an “Eagle” spectrometer covering the electromagnetic spectrum from 407 nm to 1007.10 nm in 253 separate wavebands and at a 2 m grid resolution (with the exception of a small area of Luton where the hyperspectral Eagle data were captured at 4 m spatial resolution due to flying height restrictions near a major commercial airport).…”

Radiometric calibration of a dual-wavelength terrestrial laser scanner using neural networks

“…The raw binary files recorded by the sensor were processed to extract intensity and range for each return using the Centre of Gravity method which sums the waveform value above a predetermined threshold (Hancock et al 2015). The mean intensity value of each sub-panel was extracted by averaging footprints over selected surface areas for both wavelengths.…”