Single-Scan Stem Reconstruction Using Low-Resolution Terrestrial Laser Scanner Data

Kelbe, David; Aardt, Jan van; Romanczyk, Paul; Leeuwen, Martin van; Cawse‐Nicholson, Kerry

doi:10.1109/jstars.2015.2416001

Cited by 44 publications

(47 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), realized by the Rochester Institute of Technology (Kelbe et al. , ) and extensively refined by UMB. The detailed specifications of the instruments are included in Figure .…”

Section: Methodsmentioning

confidence: 99%

Observing ecosystems with lightweight, rapid‐scanning terrestrial lidar scanners

Paynter

Saenz

Genest

et al. 2016

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

A new wave of terrestrial lidar scanners, optimized for rapid scanning and portability, such as the Compact Biomass Lidar (CBL), enable and improve observations of structure across a range of important ecosystems. We performed studies with the CBL in temperate and tropical forests, caves, salt marshes and coastal areas subject to erosion. By facilitating additional scanning points, and therefore view angles, this new class of terrestrial lidar alters observation coverage within samples, potentially reducing uncertainty in estimates of ecosystem properties. The CBL has proved competent at reconstructing trees and mangrove roots using the same cylinder-based Quantitative Structure Models commonly utilized for data from more capable instruments (Raumonen et al. 2013). For tropical trees with morphologies that challenge standard reconstruction techniques, such as the buttressed roots of Ceiba trees and the multiple stems of strangler figs, the CBL was able to provide the versatility and the speed of deployment needed to fully characterize their unique features. For geomorphological features, the deployment flexibility of the CBL enabled sampling from optimal view-angles, including from a novel suspension system for sampling salt marsh creeks. Overall, the practical aspects of these instruments, which improve deployment logistics, and therefore data acquisition rate, are shown to be emerging capabilities, greatly increasing the potential for observation, particularly in highly temporally dynamic, inaccessible and geometrically complex ecosystems. In order to better analyze information quality across these diverse and challenging ecosystems, we also provide a novel and much-needed conceptual framework, the microstate model, to characterize and mitigate uncertainties in terrestrial lidar observations.

show abstract

“…), realized by the Rochester Institute of Technology (Kelbe et al. , ) and extensively refined by UMB. The detailed specifications of the instruments are included in Figure .…”

Section: Methodsmentioning

confidence: 99%

Observing ecosystems with lightweight, rapid‐scanning terrestrial lidar scanners

Paynter

Saenz

Genest

et al. 2016

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

show abstract

“…After more than a decade of research in the field of the use of terrestrial laser scanning (TLS) for forestry, conclusions of many research papers presented TLS lack of mobility as the greatest disadvantage of this technology (Liang et al 2014Kelbe et al 2015). Mobile mapping systems (MMS), which include mobile laser scanner and additional cameras, can provide point clouds with density similar to TLS.…”

mentioning

confidence: 99%

Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy

Čerňava¹,

Tuček²,

Koreň³

et al. 2017

J. For. Sci.

View full text Add to dashboard Cite

AbstractČerňava J., Tuček J., Koreň M., Mokroš M. (2017): Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy. J. For. Sci., 63: 433-441.Mobile laser scanning (MLS) is time-efficient technology of geospatial data collection that proved its ability to provide accurate measurements in many fields. Mobile innovation of the terrestrial laser scanning has a potential to collect forest inventory data on a tree level from large plots in a short time. Valuable data, collected using mobile mapping system (MMS), becomes very difficult to process when Global Navigation Satellite System (GNSS) outages become too long. A heavy forest canopy blocking the GNSS signal and limited accessibility can make mobile mapping very difficult. This paper presents processing of data collected by MMS under a heavy forest canopy. DBH was estimated from MLS point cloud using three different methods. Root mean squared error varied between 2.65 and 5.57 cm. Our research resulted in verification of the influence of MLS coverage of tree stem on the accuracy of DBH data.

show abstract

“…Examples for studies, which provided comparable accuracy metrics, are from Liang and Hyyppä [8], who reported on average a bias of 0.5 cm and a RMSE of 1.4 cm for their test sites, McDaniel et al [6] with a median error of 9.8 cm and an RMSE of 13.2 cm and Maas et al [20], who achieved a bias of 0.6 cm and a RMSE of 2.1 cm. R 2 values were reported by Kelbe et al [9], who achieved a R 2 of 0.8 in combination with an RMSE of 6.4 cm and by Yao et al [14] with an R 2 of 0.62 and an RMSE of 7.6 cm.…”

Section: Discussionmentioning

confidence: 78%

Tree Stem Diameter Estimation From Volumetric TLS Image Data

Heinzel

Huber

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Recently, a new method on tree stem isolation using volumetric image data from terrestrial laser scans (TLS) has been introduced by the same authors. The method transfers TLS data into a voxel grid data structure and isolates the tree stems from the overall forest vegetation. While the stem detection method yields on a three dimensional localisation of the tree stems, the present study introduces a supplemental technique, which accurately estimates the diameter at breast height (DBH) from the stem objects. Often, large pieces of the stems are occluded by other vegetation and are only partially represented in the laser scanning data, not covering the complete circumference. Therefore, it was not possible to measure the diameter at 130 cm height directly on the stem imagery. Instead, a method has been developed, which estimated the diameter from the fragmented stem information at the specific cross sections. The stem information was processed in a way, which allowed applying a Hough transform to the image for fitting circles to the cross sections. In contrast to other studies, Hough transform was applied to single stem images with information from other vegetation parts already being removed. Even in cases where only a single and very small fragment of a stem is available, the diameter could be estimated from the curvature. It also has been demonstrated that the image resolution for DBH measurement can be significantly higher than the resolution used for stem isolation in order to increase the precision. Verification of the computed DBH on nine spatially independent test sites showed that applying the Hough transform to single stem cross section images produced accurate results. When excluding the five strongest individual outliers a bias of −0.02 cm, a root mean square error (RMSE) of 2.9 cm and a R 2 of 0.98 were achieved.

show abstract

Single-Scan Stem Reconstruction Using Low-Resolution Terrestrial Laser Scanner Data

Cited by 44 publications

References 44 publications

Observing ecosystems with lightweight, rapid‐scanning terrestrial lidar scanners

Observing ecosystems with lightweight, rapid‐scanning terrestrial lidar scanners

Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy

Tree Stem Diameter Estimation From Volumetric TLS Image Data

Contact Info

Product

Resources

About