Improved Sampling for Terrestrial and Mobile Laser Scanner Point Cloud Data

Puttonen, Eetu; Lehtomäki, Matti; Kaartinen, Harri; Zhu, Long; Kukko, Antero; Jaakkola, Anttoni

doi:10.3390/rs5041754

Cited by 45 publications

(28 citation statements)

References 25 publications

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“…The anticipated workflow contains four procedures. First, the original point cloud is down sampled by a leveled histogram sampling method [40]. Second, the terrain points are identified and removed by a hierarchical approach [41], so that trees are assigned with normalized heights above the ground.…”

Section: Methodsmentioning

confidence: 99%

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Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

et al. 2016

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Abstract:Terrestrial laser scanning (TLS) is a promising technique for plot-wise acquisition of geometric attributes of forests. However, there still exists a need for TLS applications in mountain forests where tree stems' growing directions are not vertical. This paper presents a novel method to model tree stems precisely in an alpine landslide-affected forest using TLS. Tree stems are automatically detected by a two-layer projection method. Stems are modeled by fitting a series of cylinders based on a 2D-3D random sample consensus (RANSAC)-based approach. Diameter at breast height (DBH) was manually measured in the field, and stem curves were measured from the point cloud as reference data. The results showed that all trees in the test area can be detected. The root mean square error (RMSE) of estimated DBH was 1.80 cm (5.5%). Stem curves were automatically generated and compared with reference data, as well as stem volumes. The results imply that the proposed method is able to map and model the stem curve precisely in complex forest conditions. The resulting stem parameters can be employed in single tree biomass estimation, tree growth quantification and other forest-related studies.

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Section: Methodsmentioning

confidence: 99%

“…The considerable redundancy calls for down sampling approaches. In this study, the point cloud sampling was performed with a sampling routine named leveled histogram sampling [40]. The routine was selected based on its speed and effectiveness.…”

Section: Down Samplingmentioning

confidence: 99%

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

et al. 2016

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“…In this study, we further integrate an advanced sampling method to test whether sampling of point cloud will speed up the processing while at the same time retain the quality of stem reconstruction. Point cloud sampling was performed with a sampling routine presented in Puttonen et al (2013), namely leveled histogram sampling. The routine was selected based on its speed and effectiveness.…”

Section: Sampling Techniquementioning

confidence: 99%

Fast and Robust Stem Reconstruction in Complex Environments Using Terrestrial Laser Scanning

Wang¹,

Hollaus²,

Puttonen³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Terrestrial Laser Scanning (TLS) is an effective tool in forest research and management. However, accurate estimation of tree parameters still remains challenging in complex forests. In this paper, we present a novel algorithm for stem modeling in complex environments. This method does not require accurate delineation of stem points from the original point cloud. The stem reconstruction features a self-adaptive cylinder growing scheme. This algorithm is tested for a landslide region in the federal state of Vorarlberg, Austria. The algorithm results are compared with field reference data, which show that our algorithm is able to accurately retrieve the diameter at breast height (DBH) with a root mean square error (RMSE) of ∼1.9 cm. This algorithm is further facilitated by applying an advanced sampling technique. Different sampling rates are applied and tested. It is found that a sampling rate of 7.5% is already able to retain the stem fitting quality and simultaneously reduce the computation time significantly by ∼88%.

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“…A variety of LiDAR techniques have emerged, and LiDAR data are often acquired from various platforms, for example airborne [2,3], mobile [4,5] and terrestrial platforms [6,7]. Acquiring data from a side view, terrestrial LiDAR can obtain detailed facade information of objects at relatively high resolution.…”

Section: Introductionmentioning

confidence: 99%

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

Cheng

Tong

et al. 2015

Remote Sensing

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Abstract:A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an initial matching relationship between the terrestrial corners and airborne corners is automatically derived using a matching technique based on maximum matching corner pairs with minimum errors (MTMM). Finally, a set of leading points are generated from matched airborne corners, and a shiftable leading point method is proposed. The key feature of this approach is the implementation of the concept of shiftable leading points in the final step. Since the geometric accuracy of terrestrial LiDAR data is much better than that of airborne LiDAR data, leading points corresponding to anomalous airborne corners could be modified for the improvement of the geometric accuracy of registration. OPEN ACCESSRemote Sens. 2015, 7 1916The experiment demonstrates that the proposed approach can advance the geometric accuracy of two-platform LiDAR data registration effectively.

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Improved Sampling for Terrestrial and Mobile Laser Scanner Point Cloud Data

Cited by 45 publications

References 25 publications

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

Fast and Robust Stem Reconstruction in Complex Environments Using Terrestrial Laser Scanning

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

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