POINT CLOUD METRICS FOR SEPARATING STANDING ARCHAEOLOGICAL REMAINS AND LOW VEGETATION IN ALS DATA

Three-dimensional (3D) reconstruction is a useful technique for the documentation, characterization, and evaluation of small archeological objects. In this research, a comparison among different photogrammetric setups that use different lenses (macro and standard zoom) and dense point cloud generation calibration processes for real specific objects of archaeological interest with different textures, geometries, and materials is raised using an automated data collection. The data acquisition protocol is carried out from a platform with control points referenced with a metrology absolute arm to accurately define a common spatial reference system. The photogrammetric reconstruction is performed considering a camera pre-calibration as well as a self-calibration. The latter is common for most data acquisition situations in archaeology. The results for the different lenses and calibration processes are compared based on a robust statistical analysis, which entails the estimation of both standard Gaussian and non-parametric estimators, to assess the accuracy potential of different configurations. As a result, 95% of the reconstructed points show geometric discrepancies lower than 0.85 mm for the most unfavorable case and less than 0.35 mm for the other cases.

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UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

Remote Sensing ◽

10.3390/rs12223829 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3829

Author(s):

Martin Slavík ◽

Karel Kuželka ◽

Roman Modlinger ◽

Ivana Tomášková ◽

Peter Surový

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Morphological Changes ◽

Tree Canopy ◽

High Temporal Resolution ◽

Human Observer ◽

Individual Tree ◽

Dead Trees ◽

Multi Temporal ◽

Cloud Metrics

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.

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Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment

Forests ◽

10.3390/f6103704 ◽

2015 ◽

Vol 6 (12) ◽

pp. 3704-3732 ◽

Cited By ~ 84

Author(s):

Joanne White ◽

Christoph Stepper ◽

Piotr Tompalski ◽

Nicholas Coops ◽

Michael Wulder

Keyword(s):

Forest Inventory ◽

Point Cloud ◽

Coastal Forest ◽

Forest Environment ◽

Cloud Metrics

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Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

TECHART Journal of Arts and Imaging Science ◽

10.15323/techart.2016.05.3.2.32 ◽

2016 ◽

Vol 3 (2) ◽

pp. 32

Author(s):

Rekha Patil ◽

Ashok Kumar Patil ◽

Youngho Chai

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Interactive Editing

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A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.136.1078 ◽

2016 ◽

Vol 136 (8) ◽

pp. 1078-1084

Author(s):

Shoichi Takei ◽

Shuichi Akizuki ◽

Manabu Hashimoto

Keyword(s):

Point Cloud ◽

Multi Scale ◽

Feature Based ◽

Shell Region

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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