2012
DOI: 10.1016/j.rse.2011.11.015
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Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

Abstract: Access to the published version may require journal subscription. Published with permission from: Elsevier.Standard set statement from the publisher: NOTICE: this is the author's version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitte… Show more

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Cited by 75 publications
(46 citation statements)
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References 29 publications
(35 reference statements)
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“…For the past decade, most ALS systems produced point cloud (or discrete-return) data [21]. These systems record multiple returns (n ≤ 5) per transmitted pulse with each return representing the 3D position and intensity of the reflected light.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…For the past decade, most ALS systems produced point cloud (or discrete-return) data [21]. These systems record multiple returns (n ≤ 5) per transmitted pulse with each return representing the 3D position and intensity of the reflected light.…”
Section: Introductionmentioning
confidence: 99%
“…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]. The analysis of full-waveform data has improved the urban area classification [31], and the estimations of forest structural variables, e.g., DBH [32], canopy height [33], number of stems [34], stem volume [21] and biomass [35].…”
Section: Introductionmentioning
confidence: 99%
“…Wagner et al [16] tested LMS-Q560 data and demonstrated that the decomposition was successful for approximately 98% of waveform profiles. These clouds are subsequently used as the discrete data [17], or by building regression models using the calculated waveform metrics with field measurements to estimate forest parameters [18][19][20]. However, neither approach uses all of the information provided in the waveform data.…”
Section: Introductionmentioning
confidence: 99%
“…in forestry (Hyyppä et al, 2012;Naesset, 2007), plantation (Fieber et al, 2013;Görgens et al, 2015) and urban areas (Höfle et al, 2012;Richter et al, 2013;Yan et al, 2015). The estimation of tree characteristics like the structure and canopy profile (Latifi et al, 2015;Leiterer et al, 2012;Lindberg et al, 2012), the leaf area index (Alonzo et al, 2015;Fieber et al, 2014), the volume and above ground biomass (Allouis et al, 2013;Cao et al, 2014;Pirotti et al, 2014)as well as the classification of tree species (Ghosh et al, 2014;GuangCai et al, 2012;Heinzel and Koch, 2011;Hollaus et al, 2009;Holmgren and Persson, 2004;Hovi et al, 2016) have been applied, especially in forested areas. In contrast to closed forest stands, urban trees are characterized by large species diversity within small areas and a complex (typically anthropogenic) shape.…”
Section: Introductionmentioning
confidence: 99%