Enhancing online waveform processing by adding new point attributes

Pfennigbauer, Martin; Wolf, Clifford; Ullrich, Andreas

doi:10.1117/12.2015733

Cited by 10 publications

(11 citation statements)

References 7 publications

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“…Therefore, ρ a,i = ρ i · cos θ if there is only a single return, which is consistent with findings by Hancock, et al [19]. Standard deviation s i is a complex quantity that depends on the projection of the 2-D energy profile P l (β) onto the oblique target [41]. Unless s s s i , there is no direct link betweenP i and I i .…”

Section: Radiative Transfer Under Realistic Conditionssupporting

confidence: 88%

“…The GD technique was developed to describe the waveform as a combination of discrete Gaussian profiles defined by peak amplitude, time shift, and standard deviation [37]. These quantities can be stored in point data with or without storage of the waveform [38][39][40][41][42][43]. Although these idealized quantities and the full waveform radiometric calibration [44,45] have been discussed previously, the physical interpretations under realistic conditions Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Yin

Cook

et al. 2019

Remote Sensing

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Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability (P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.

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Section: Radiative Transfer Under Realistic Conditionssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Yin

Cook

et al. 2019

Remote Sensing

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“…The impact and reflection of a laser beam on inclined surfaces leads to a stretched received echo pulse. The relationship between widening of the echo pulse σ r and angle of incidence β is well explained within [51] and given by following equation:…”

Section: Lrtls Accuracy Estimationmentioning

confidence: 99%

“…The relationship of pulse widening (relative and absolute) and incidence angle of the laser beam at different scanning distances calculated after[51] and using the Riegl VZ ® -6000 specific parameters θ = 0.12 mrad and σ s = 3 ns.…”

mentioning

confidence: 99%

Derivation of Three-Dimensional Displacement Vectors from Multi-Temporal Long-Range Terrestrial Laser Scanning at the Reissenschuh Landslide (Tyrol, Austria)

et al. 2018

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Deep-seated gravitational slope deformations (DSGSDs) endanger settlements and infrastructure in mountain areas all over the world. To prevent disastrous events, their activity needs to be continuously monitored. In this paper, the movement of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) is quantified based on point clouds acquired with a Riegl VZ®-6000 long-range laser scanner in 2016 and 2017. Geomorphological features (e.g., block edges, terrain ridges, scarps) travelling on top of the landslide are extracted from the acquired point clouds using morphometric attributes based on locally computed eigenvectors and -values. The corresponding representations of the extracted features in the multi-temporal data are exploited to derive 3D displacement vectors based on a workflow exploiting the iterative closest point (ICP) algorithm. The subsequent analysis reveals spatial patterns of landslide movement with mean displacements in the order of 0.62 ma − 1 , corresponding well with measurements at characteristic points using a differential global navigation satellite system (DGNSS). The results are also compared to those derived from a modified version of the well-known image correlation (IMCORR) method using shaded reliefs of the derived digital terrain models. The applied extended ICP algorithm outperforms the raster-based method particularly in areas with predominantly vertical movement.

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“…A nonperpendicular angle of incidence leads to the broadening of the return pulse. Pfennigbauer et al [28] demonstrated the weak effect of pulse broadening for the RIEGL VZ-instrument, mainly due to the small beam divergence of the instrument (nominally 0.35 mrad). They stated that it was questionable whether an increase in pulse width was measurable for incidence angles below 5 • .…”

Section: Experimental Setup For Evaluating the Radiometric Calibramentioning

confidence: 99%

Evaluation of the Range Accuracy and the Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments for Data Interoperability

Calders

Disney

Armston

et al. 2017

IEEE Trans. Geosci. Remote Sensing

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Terrestrial laser scanning (TLS) data provide 3-D measurements of vegetation structure and have the potential to support the calibration and validation of satellite and airborne sensors. The increasing range of different commercial and scientific TLS instruments holds challenges for data and instrument interoperability. Using data from various TLS sources will be critical to upscale study areas or compare data. In this paper, we provide a general framework to compare the interoperability of TLS instruments. We compare three TLS instruments that are the same make and model, the RIEGL VZ-400. We compare the range accuracy and evaluate the manufacturer's radiometric calibration for the uncalibrated return intensities. Our results show that the range accuracy between instruments is comparable and within the manufacturer's specifications. This means that the spatial XYZ data of different instruments can be combined into a single data set. Our findings demonstrate that radiometric calibration is instrument specific and needs to be carried out Manuscript

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Enhancing online waveform processing by adding new point attributes

Cited by 10 publications

References 7 publications

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Derivation of Three-Dimensional Displacement Vectors from Multi-Temporal Long-Range Terrestrial Laser Scanning at the Reissenschuh Landslide (Tyrol, Austria)

Evaluation of the Range Accuracy and the Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments for Data Interoperability

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