The Slope Imaging Multi-polarization Photon-counting Lidar: Development and performance results

Dabney, Philip W.; Harding, David J.; Abshire, James B.; Huss, Tim; Jodor, Gabriel; Machan, Roman; Marzouk, Joe; Rush, Kurt; Seas, Antonios; Shuman, Christopher A.; Sun, Xiaoli; Valett, Susan; Vasilyev, Aleksey; Yu, Anthony W.; Zheng, Yunhui

doi:10.1109/igarss.2010.5650862

Cited by 33 publications

(24 citation statements)

References 10 publications

(10 reference statements)

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“…These are much more sensitive to low intensity signals, requiring lower powered lasers and so making the instrument lighter and cheaper. NASA's SIMPL (Dabney et al, 2010) and ICESat II (Harding et al, 2011) instruments currently in development are examples and Moussavi, Abdalati, Scambos, and Neuenschwander (2014) demonstrated that they can measure forest height. However they require repeated measurements to measure energy, either from a fixed position (Hernandez-Marin, Wallace, & Gibson, 2007;Wallace et al, 2012) or by averaging over an area (Moussavi et al, 2014), which introduces statistical errors on top of the error from inversion methods.…”

Section: System Pulsementioning

confidence: 98%

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: System Pulsementioning

confidence: 98%

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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“…SIMPL is a multi-beam laser altimeter developed through the NASA Earth Science Technology Office Instrument Incubator Program [7]. SIMPL is a pathfinder for more efficient, next-generation spaceflight laser altimeters including ICESat-2.…”

Section: Methodsmentioning

confidence: 99%

Airborne polarimetric, two-color laser altimeter measurements of lake ice cover: A pathfinder for NASA's ICESat-2 spaceflight mission

Harding

Dabney

Valett

et al. 2011

2011 IEEE International Geoscience and Remote Sensing Symposium

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The ICESat-2 mission will continue NASA's spaceflight laser altimeter measurements of ice sheets, sea ice and vegetation using a new measurement approach: micropulse, single photon ranging at 532 nm. Differential penetration of green laser energy into snow, ice and water could introduce errors in sea ice freeboard determination used for estimation of ice thickness. Laser pulse scattering from these surface types, and resulting range biasing due to pulse broadening, is assessed using SIMPL airborne data acquired over icecovered Lake Erie. SIMPL acquires polarimetric lidar measurements at 1064 and 532 nm using the micropulse, single photon ranging measurement approach.

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“…The recently developed and active airborne photon counting LiDAR system called SIMPL (Slope Imaging Multi-polarization Photon Counting Lidar) [26] is a dual channel system that measures at two wavelengths (532 nm and 1,064 nm) in parallel and perpendicular polarization channels, and is a time-correlated single photon counting system deployed in an airborne platform. In this paper, we also use a time-correlated photon counting LiDAR system to acquire measurements from a small sample conifer to illustrate our techniques.…”

Section: Forest Monitoring Using Lidar Datamentioning

confidence: 99%

Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

2012

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Abstract:We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion.

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The Slope Imaging Multi-polarization Photon-counting Lidar: Development and performance results

Cited by 33 publications

References 10 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

Airborne polarimetric, two-color laser altimeter measurements of lake ice cover: A pathfinder for NASA's ICESat-2 spaceflight mission

Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

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