Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner

Wagner, Wolfgang; Ullrich, Andreas; Ducic, V.; Melzer, Thomas; Studnicka, Nick

doi:10.1016/j.isprsjprs.2005.12.001

Cited by 620 publications

(525 citation statements)

References 15 publications

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“…There are disadvantages to using ALS compared to traditional photogrammetric techniques and these include the lower spatial resolution and the lack of spectral information. However, new ALS systems are capable of recording the intensity and sometimes the full waveform of the target (Wagner et al, 2006;Reitberger et al, 2008;Chauve et al, 2009), and furthermore synergy with high spatial resolution images improve the semi-automatic classification of features from laser data (Kaartinen and Hyyppä, 2008).…”

Section: Remote Sensing: Targeted Mappingmentioning

confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

113

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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Section: Remote Sensing: Targeted Mappingmentioning

confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

113

View full text Add to dashboard Cite

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“…Similar to the proposed deconvolution method, the regularization parameter was estimated by the L-curve method in the case of Tikhonov regularization. The exact number of expected pulses was assumed to be known in order to apply the Gaussian decomposition method through application of nonlinear optimization, e.g., the Levenberg-Marquardt algorithm [14]. Sparsity-based regularization CVX package L-curve [65,66] Qualitative comparisons of the proposed method against the other four methods are shown in Figure 4 for selected samples.…”

Section: Deconvolution Results For Synthetic Waveformsmentioning

confidence: 99%

“…Gaussian waveform decomposition methods for the extraction of a parametric description of the pulse properties, including range, width and amplitude, have been developed for instance by Hofton et al [21] for a laser vegetation imaging sensor (LVIS), by Persson et al [22] for the TopEye Mark II lidar system and by Wagner et al [14] for small footprint data. Generally, a set of Gaussian functions is considered to both fit to the received backscattered waveform and to characterize each pulse shape in this approach.…”

Section: Decomposition Methodsmentioning

confidence: 99%

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

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Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted.

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“…The benefit of full-waveform data is the approximation of the entire backscattered signal by digitization, which facilitates the extraction of additional features of each reflecting object within the ALS footprint. To detect and extract specific object reflections, Gaussian pulse estimation was applied using Riegl's software RiANALYZE 3.0.8 (RIEGL, Horn, Austria) [52] in order to obtain representative echo descriptions (cf. Figure 2).…”

Section: Airborne Laser Scanning Datamentioning

confidence: 99%

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

Leiterer

Torabzadeh

Furrer

et al. 2015

Forests

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Canopy layers form essential structural components, affecting stand productivity and wildlife habitats. Airborne laser scanning (ALS) provides horizontal and vertical information on canopy structure simultaneously. Existing approaches to assess canopy layering often require prior information about stand characteristics or rely on pre-defined height thresholds. We developed a multi-scale method using ALS data with point densities >10 pts/m 2 to determine the number and vertical extent of canopy layers (canopylayer, canopylength), seasonal variations in the topmost canopy layer (canopytype), as well as small-scale heterogeneities in the canopy (canopyheterogeneity). We first tested and developed the method on a small forest patch (800 ha) and afterwards tested transferability and robustness of the method on a larger patch (180,000 ha). We validated the approach using an extensive set of ground data, achieving overall accuracies >77% for canopytype and canopyheterogeneity, and >62% for canopylayer and canopylength. We conclude that our method provides a robust characterization of canopy layering supporting automated canopy structure monitoring. OPEN ACCESSForests 2015, 6 4147

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Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner

Cited by 620 publications

References 15 publications

An overview of recent remote sensing and GIS based research in ecological informatics

An overview of recent remote sensing and GIS based research in ecological informatics

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

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