scholarly journals Brief communication: 3-D reconstruction of a collapsed rock pillar from Web-retrieved images and terrestrial lidar data – the 2005 event of the west face of the Drus (Mont Blanc massif)

2017 ◽  
Vol 17 (7) ◽  
pp. 1207-1220 ◽  
Author(s):  
Antoine Guerin ◽  
Antonio Abellán ◽  
Battista Matasci ◽  
Michel Jaboyedoff ◽  
Marc-Henri Derron ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Point Cloud ◽  
Rock Avalanche ◽  
Lidar Data ◽  
The West ◽  
Terrestrial Lidar ◽  
The Difference ◽  
Historical Photographs ◽  
D Model ◽  
Mont Blanc Massif

Abstract. In June 2005, a series of major rockfall events completely wiped out the Bonatti Pillar located in the legendary Drus west face (Mont Blanc massif, France). Terrestrial lidar scans of the west face were acquired after this event, but no pre-event point cloud is available. Thus, in order to reconstruct the volume and the shape of the collapsed blocks, a 3-D model has been built using photogrammetry (structure-from-motion (SfM) algorithms) based on 30 pictures collected on the Web. All these pictures were taken between September 2003 and May 2005. We then reconstructed the shape and volume of the fallen compartment by comparing the SfM model with terrestrial lidar data acquired in October 2005 and November 2011. The volume is calculated to 292 680 m3 (±5.6 %). This result is close to the value previously assessed by Ravanel and Deline (2008) for this same rock avalanche (265 000 ± 10 000 m3). The difference between these two estimations can be explained by the rounded shape of the volume determined by photogrammetry, which may lead to a volume overestimation. However it is not excluded that the volume calculated by Ravanel and Deline (2008) is slightly underestimated, the thickness of the blocks having been assessed manually from historical photographs.

scholarly journals Brief communication: 3D reconstruction of a collapsed rock pillar from web-retrieved images and terrestrial LiDAR data – The 2005 event of the West face of the Drus (Mont-Blanc massif)

2016 ◽  
Author(s):  
Antoine Guerin ◽  
Antonio Abellán ◽  
Battista Matasci ◽  
Michel Jaboyedoff ◽  
Marc-Henri Derron ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Model ◽  
Rock Avalanche ◽  
Lidar Data ◽  
The West ◽  
Terrestrial Lidar ◽  
The Face ◽  
The Difference ◽  
Historical Photographs ◽  
Mont Blanc Massif

Abstract. In June 2005, a series of major rockfall events completely wiped out the Bonatti Pillar located in the legendary Drus West face (Mont-Blanc massif, France). Terrestrial LiDAR scans of the face were acquired after this event but no pre-event point cloud is available. Thus, in order to reconstruct the volume and the shape of the collapsed blocks, a 3D model has been built using photogrammetry (SfM) based on 30 pictures collected on the Web. All these pictures were taken between September 2003 and May 2005. We then reconstructed the shape and volume of the fallen compartment by comparing the SfM model with terrestrial LiDAR data acquired in October 2005 and November 2011. The volume is calculated to 292’680 m3 (±5 %). This result is close to the value previously assessed by Ravanel and Deline (2008) for this same rock-avalanche (265’000 ± 10’000 m3). The difference between these two estimations can be explained by the rounded shape of the volume determined by photogrammetry, which may lead to a volume overestimation. However it is not excluded that the volume calculated by Ravanel and Deline (2008) is slightly underestimated, the thickness of the blocks having been assessed manually from historical photographs.

scholarly journals Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes

2020 ◽  
Vol 10 (21) ◽  
pp. 7409 ◽  
Author(s):  
Waldemar Kociuba
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Mineral Resources ◽  
Noise Removal ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Differential Analysis ◽  
Terrestrial Lidar ◽  
Surface Changes

High resolution terrestrial laser scanning data (TLS; terrestrial LiDAR) provide an excellent background for quantitative resource estimation through the comparative analysis of topographic surface changes. However, unlike airborne LiDAR data, which is usually provided as classified and contains a class of ground points, raw TLS data include all of the points of the scanned space within the specified scanner range. In effect, utilizing the latter data to estimate the volume of the resource by the differential analysis of digital elevation models (DEMs) requires the data to be specially prepared, i.e., separating from the point cloud only the data that represent the relevant class. In the case of natural resources, e.g., mineral resources, the class is represented by ground points. This paper presents the results that were obtained by differential analysis of high resolution DEMs (DEM of difference (DoD) method) using TLS data that were processed both manually (operator noise removal) and with the use of the automatic Cloth Simulation Filter (CSF) algorithm. Three different time pairs of DoD data were analyzed for a potential gravel-cobble deposit area of 45,444 m2, which was located at the bottom of the mouth section of the Scott River in south-east Svalbard. It was found that the applied method of ground point classification had very little influence on the errors in the range of estimating volumetric parameters of the mineral resources and measurement uncertainty. Moreover, it was shown that the point cloud density had an influence on the CSF filtering efficiency and spatial distribution of errors.

scholarly journals QUALIFICATION OF POINT CLOUDS MEASURED BY SFM SOFTWARE

2015 ◽  
Vol XL-4/W5 ◽  
pp. 125-130 ◽  
Author(s):  
K. Oda ◽  
S. Hattori ◽  
H. Saeki ◽  
T. Takayama ◽  
R. Honma
Keyword(s):  
Coordinate System ◽  
Structure From Motion ◽  
Point Cloud ◽  
Point Clouds ◽  
Sum Of Squares ◽  
Iterative Closest Point ◽  
Test Results ◽  
Reference Coordinate System ◽  
The Difference

This paper proposes a qualification method of a point cloud created by SfM (Structure-from-Motion) software. Recently, SfM software is popular for creating point clouds. Point clouds created by SfM Software seems to be correct, but in many cases, the result does not have correct scale, or does not have correct coordinates in reference coordinate system, and in these cases it is hard to evaluate the quality of the point clouds. To evaluate this correctness of the point clouds, we propose to use the difference between point clouds with different source of images. If the shape of the point clouds with different source of images is correct, two shapes of different source might be almost same. To compare the two or more shapes of point cloud, iterative-closest-point (ICP) is implemented. Transformation parameters (rotation and translation) are iteratively calculated so as to minimize sum of squares of distances. This paper describes the procedure of the evaluation and some test results.

scholarly journals Estimating Leaf Area Density of Individual Trees Using the Point Cloud Segmentation of Terrestrial LiDAR Data and a Voxel-Based Model

2017 ◽  
Vol 9 (11) ◽  
pp. 1202 ◽  
Author(s):  
Shihua Li ◽  
Leiyu Dai ◽  
Hongshu Wang ◽  
Yong Wang ◽  
Ze He ◽  
...  
Keyword(s):  
Leaf Area ◽  
Point Cloud ◽  
Lidar Data ◽  
Area Density ◽  
Terrestrial Lidar ◽  
Leaf Area Density ◽  
Point Cloud Segmentation ◽  
Individual Trees

THE WEST SALT CREEK ROCK AVALANCHE, SAG POND, AND OUTBURST FLOOD: MORPHOLOGICAL AND SEDIMENTOLOGICAL SIGNATURES FROM A SERIES OF EXTRAORDINARY EVENTS

2017 ◽  
Author(s):  
Jeffrey A. Coe ◽  
◽  
Erin K. Bessette-Kirton ◽  
Rex L. Baum ◽  
Joel B. Smith ◽  
...  
Keyword(s):  
Rock Avalanche ◽  
Outburst Flood ◽  
The West ◽  
Salt Creek

scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

scholarly journals Ground volume assessment using ’Structure from Motion’ photogrammetry with a smartphone and a compact camera

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Rafał Wróżyński ◽  
Krzysztof Pyszny ◽  
Mariusz Sojka ◽  
Czesław Przybyła ◽  
Sadżide Murat-Błażejewska
Keyword(s):  
Open Source Software ◽  
Structure From Motion ◽  
Point Cloud ◽  
Field Measurements ◽  
Video Frames ◽  
Actual Volume ◽  
Volume Assessment ◽  
Uniform Model ◽  
Free Open Source ◽  
Test Embankment

AbstractThe article describes how the Structure-from-Motion (SfM) method can be used to calculate the volume of anthropogenic microtopography. In the proposed workflow, data is obtained using mass-market devices such as a compact camera (Canon G9) and a smartphone (iPhone5). The volume is computed using free open source software (VisualSFMv0.5.23, CMPMVSv0.6.0., MeshLab) on a PCclass computer. The input data is acquired from video frames. To verify the method laboratory tests on the embankment of a known volume has been carried out. Models of the test embankment were built using two independent measurements made with those two devices. No significant differences were found between the models in a comparative analysis. The volumes of the models differed from the actual volume just by 0.7‰ and 2‰. After a successful laboratory verification, field measurements were carried out in the same way. While building the model from the data acquired with a smartphone, it was observed that a series of frames, approximately 14% of all the frames, was rejected. The missing frames caused the point cloud to be less dense in the place where they had been rejected. This affected the model’s volume differed from the volume acquired with a camera by 7%. In order to improve the homogeneity, the frame extraction frequency was increased in the place where frames have been previously missing. A uniform model was thereby obtained with point cloud density evenly distributed. There was a 1.5% difference between the embankment’s volume and the volume calculated from the camera-recorded video. The presented method permits the number of input frames to be increased and the model’s accuracy to be enhanced without making an additional measurement, which may not be possible in the case of temporary features.

Full-Waveform Terrestrial Lidar Data Classification Using Raw Samples of Digitized Waveform

2021 ◽  
Author(s):  
Mohammad Pashaei ◽  
Michael J. Starek ◽  
Philippe Tissot ◽  
Jacob Berryhill
Keyword(s):  
Data Classification ◽  
Lidar Data ◽  
Terrestrial Lidar ◽  
Full Waveform

Numerical Model of Current Speed in Bojong Salawe Beach, Pangandaran West Java Province

2021 ◽  
Vol 2 (1) ◽  
pp. 17-23
Author(s):  
Subiyanto Subiyanto ◽  
Nira na Nirwa ◽  
Yuniarti Yuniarti ◽  
Yudi Nurul Ihsan ◽  
Eddy Afrianto
Keyword(s):  
Numerical Models ◽  
High Tide ◽  
Movement Pattern ◽  
Numerical Data ◽  
Current Speed ◽  
The West ◽  
Instantaneous Speed ◽  
The Difference ◽  
West Monsoon ◽  
Current Movement

The purpose of this study was to determine the hydrodynamic conditions at Bojong Salawe beach. The method used in this research is a quantitative method, where numerical data is collected to support the formation of numerical models such as wind, bathymetry, and tide data. The hydrodynamic model will be made using Mike 21 with the Flow Model FM module to determine the current movement pattern based on the data used. In the west monsoon with a maximum instantaneous speed of 0.04 - 0.08 m/s, while in the east monsoon it moves with a maximum instantaneous speed of 0,4 – 0,44 m/s. The dominant direction of current movement tends to the northeast. The results indicate the current speed during the east monsoon is higher than the west monsoon. The difference in the current speed is also influenced by the tide conditions; higher during high tide and lower during low tide. Monsoons also have a role in the current movements, though the effect is not very significant.

Error Analysis of a Mobile Terrestrial LiDAR System

GEOMATICA ◽  
2014 ◽  
Vol 68 (3) ◽  
pp. 183-194 ◽  
Author(s):  
M. Leslar ◽  
B. Hu ◽  
J.G. Wang
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Sensitivity Study ◽  
Error Sources ◽  
Terrestrial Lidar ◽  
Lever Arm ◽  
Ideal Situation ◽  
Individual Variables ◽  
The Individual ◽  
Source Of Error

The understanding of the effects of error on Mobile Terrestrial LiDAR (MTL) point clouds has not increased with their popularity. In this study, comprehensive error analyses based on error propagation theory and global sensitivity study were carried out to quantitatively describe the effects of various error sources in a MTL system on the point cloud. Two scenarios were envisioned; the first using the uncertainties for measurement and calibration variables that are normally expected for MTL systems as they exist today, and the second using an ideal situation where measurement and calibration values have been well adjusted. It was found that the highest proportion of error in the point cloud can be attributed to the boresight and lever arm parameters for MTL systems calibrated using non-rigours methods. In particular, under a loosely controlled error condition, the LiDAR to INS Z lever arm and the LiDAR to INS roll angle contributed more error in the output point cloud than any other parameter, including the INS position. Under tightly controlled error conditions, the INS position became the dominant source of error in the point cloud. In addition, conditional variance analysis has shown that the majority of the error in a point cloud can be attributed to the individual variables. Errors caused by the interactions between the diverse variables are minimal and can be regarded as insignificant.

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