Temporal Changes in Gravel Beach Morphology of Dokdo Island Using Aerial Photos and Ground-based LiDAR Data

2021 ◽  
Vol 28 (2) ◽  
pp. 45-57
Author(s):  
Ji-Hyun Kang ◽  
Hye-jin Kim
Keyword(s):  
Temporal Changes ◽  
Lidar Data ◽  
Aerial Photos ◽  
Beach Morphology ◽  
Ground Based Lidar ◽  
Gravel Beach

scholarly journals Application of UAV-SfM photogrammetry and aerial lidar to a disastrous flood: repeated topographic measurement of a newly formed crevasse splay of the Kinu River, central Japan

2017 ◽  
Vol 17 (9) ◽  
pp. 1505-1519 ◽  
Author(s):  
Atsuto Izumida ◽  
Shoichiro Uchiyama ◽  
Toshihiko Sugai
Keyword(s):  
Lidar Data ◽  
High Definition ◽  
Central Japan ◽  
Aerial Photos ◽  
Data Resolution ◽  
Disastrous Flood ◽  
Surface Models ◽  
Elevation Changes ◽  
Aerial Vehicle ◽  
Crevasse Splay

Abstract. Geomorphic impacts of a disastrous crevasse splay that formed in September 2015 and its post-formation modifications were quantitatively documented by using repeated, high-definition digital surface models (DSMs) of an inhabited and cultivated floodplain of the Kinu River, central Japan. The DSMs were based on pre-flood (resolution: 2 m) and post-flood (resolution: 1 m) aerial light detection and ranging (lidar) data from January 2007 and September 2015, respectively, and on structure-from-motion (SfM) photogrammetry data (resolution: 3.84 cm) derived from aerial photos taken by an unmanned aerial vehicle (UAV) in December 2015. After elimination of systematic errors among the DSMs and down-sampling of the SfM-derived DSM, elevation changes on the order of 10−1 m – including not only topography but also growth of vegetation, vanishing of flood waters, and restoration and repair works – were detected. Comparison of the DSMs showed that the volume eroded by the flood was more than twice the deposited volume in the area within 300–500 m of the breached artificial levee, where the topography was significantly affected. The results suggest that DSMs based on a combination of UAV-SfM and lidar data can be used to quantify, rapidly and in rich detail, topographic changes on floodplains caused by floods.

scholarly journals A Novel Method for Feature Extraction and Classification for an Aerial Image and LiDAR Data with Genetic Algorithm

2012 ◽  
pp. 264-270
Author(s):  
Manjunath B. E ◽  
D. G. Anand ◽  
Mahant. G. Kattimani
Keyword(s):  
Feature Extraction ◽  
Light Detection And Ranging ◽  
Aerial Image ◽  
Lidar Data ◽  
Light Detection ◽  
Aerial Photos ◽  
The Earth ◽  
Digital Elevation ◽  
Land Surveying ◽  
Novel Method

Airborne Light Detection and Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. In particular, LiDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. Aerial photos with LiDAR data were processed with genetic algorithms not only for feature extraction but also for orthographical image. DSM provided by LiDAR reduced the amount of GCPs needed for the regular processing, thus the reason both efficiency and accuracy are highly improved. LiDAR is an acronym for Light Detection and Ranging, which is typically defined as an integration of three technologies into a single system, which is capable of acquiring a data to produce accurate Digital Elevation Models.

scholarly journals A Model Study of Building Seismic Damage Information Extraction and Analysis on Ground-Based LiDAR Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fan Yang ◽  
Xintao Wen ◽  
Xiaoshan Wang ◽  
Xiaoli Li ◽  
Zhiqiang Li
Keyword(s):  
High Precision ◽  
Laser Scanning ◽  
Full Range ◽  
Scientific Basis ◽  
Building Damage ◽  
Vector Model ◽  
Lidar Data ◽  
Emergency Relief ◽  
Cloud Data ◽  
Ground Based Lidar

Earthquake disasters can have a serious impact on people’s lives and property, with damage to buildings being one of the main causes of death and injury. A rapid assessment of the extent of building damage is essential for emergency response management, rescue operations, and reconstruction. Terrestrial laser scanning technology can obtain high precision light detection and ranging (LiDAR) point cloud data of the target. The technology is widely used in various fields; however, the quantitative analysis of building seismic information is the focus and difficulty of ground-based LiDAR data analysis processing. This paper takes full advantage of the high-precision characteristics of ground-based LiDAR data. A triangular network vector model (TIN-shaped model) was created in conjunction with the alpha shapes algorithm, solving the problem of small, nonvisually identifiable postearthquake building damage feature extraction bias. The model measures the length, width, and depth of building cracks, extracts the amount of wall tilt deformation, and labels the deformation zone. The creation of this model can provide scientific basis and technical support for postearthquake emergency relief, assessment of damage to buildings, extraction of deformation characteristics of other structures (bridges, tunnels, dams, etc.), and seismic reinforcement of buildings. The research data in this paper were collected by the author’s research team in the first time after the 2013 Lushan earthquake and is one of the few sets of foundation of LiDAR data covering the full range of postearthquake building types in the region, with the data information mainly including different damage levels of different structural types of buildings. The modeling analysis of this data provides a scientific basis for establishing the earthquake damage matrix of buildings in the region.

Cluster-Based Roof Covering Damage Detection in Ground-Based Lidar Data

2015 ◽  
Vol 58 ◽  
pp. 19-27 ◽  
Author(s):  
Alireza G. Kashani ◽  
Andrew J. Graettinger
Keyword(s):  
Damage Detection ◽  
Lidar Data ◽  
Ground Based Lidar

Tessellation of Ground-Based LIDAR Data for ICP Registration

2008 ◽  
Author(s):  
J.W. Harrison ◽  
P.J.W. Iles ◽  
F.P. Ferrie ◽  
S. Hefford ◽  
K. Kusevic ◽  
...  
Keyword(s):  
Lidar Data ◽  
Ground Based Lidar

scholarly journals Application of Lidar Data to Assist Airmass Discrimination at the Whistler Mountaintop Observatory

2012 ◽  
Vol 51 (10) ◽  
pp. 1733-1739 ◽  
Author(s):  
John P. Gallagher ◽  
Ian G. McKendry ◽  
Paul W. Cottle ◽  
Anne Marie Macdonald ◽  
W. Richard Leaitch ◽  
...  
Keyword(s):  
Vapor Condensation ◽  
Lidar Data ◽  
Free Troposphere ◽  
Atmospheric Layer ◽  
Lidar System ◽  
Air Samples ◽  
Water Vapor Condensation ◽  
Air Chemistry ◽  
Ground Based Lidar ◽  
Time Series Images

AbstractA ground-based lidar system that has been deployed in Whistler, British Columbia, Canada, since the spring of 2010 provides a means of evaluating vertical aerosol structure in a mountainous environment. This information is used to help to determine when an air chemistry observatory atop Whistler Mountain (2182 m MSL) is within the free troposphere or is influenced by the valley-based planetary boundary layer (PBL). Three case studies are presented in which 1-day time series images of backscatter data from the lidar are analyzed along with concurrent meteorological and air-chemistry datasets from the mountaintop site. The cases were selected to illustrate different scenarios of diurnal PBL evolution that are expected to be common during their respective seasons. The lidar images corroborate assumptions about PBL influence as derived from analysis of diurnal trends in water vapor, condensation nuclei, and ozone. Use of all of these datasets together bolsters efforts to determine which atmospheric layer the site best represents, which is important when evaluating the provenance of air samples.

scholarly journals A Hybrid GWR-Based Height Estimation Method for Building Detection in Urban Environments

2014 ◽  
Vol II-2 ◽  
pp. 23-29 ◽  
Author(s):  
X. Wei ◽  
X. Yao
Keyword(s):  
Urban Areas ◽  
Estimation Method ◽  
Urban Environments ◽  
Lidar Data ◽  
Building Detection ◽  
Building Roof ◽  
Aerial Photos ◽  
The Road ◽  
Urban Modelling ◽  
Gwr Model

LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR model, LiDAR data provide the height information of spatial objects which is the dependent variable, while the brightness values from multiple bands of the aerial photo serve as the independent variables. The proposed method can thus estimate the height at each pixel from values of its surrounding pixels with consideration of the distances between the pixels and similarities between their brightness values. Clusters of contiguous pixels with higher estimated height values distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed hybrid method is better than those by image classification of aerial photos along or by height extraction of LiDAR data alone. We argue that this simple and effective method can be very useful for automatic detection of buildings in urban areas.

scholarly journals Application of UAV-SfM photogrammetry and aerial LiDAR to a disastrous flood: multitemporal topographic measurement of a newly formed crevasse splay of the Kinu River, central Japan

2017 ◽  
Author(s):  
Atsuto Izumida ◽  
Shoichiro Uchiyama ◽  
Toshihiko Sugai
Keyword(s):  
Urban Areas ◽  
Lidar Data ◽  
Residential Areas ◽  
High Definition ◽  
Central Japan ◽  
Aerial Photos ◽  
Ground Survey ◽  
Disastrous Flood ◽  
Aerial Vehicle ◽  
Crevasse Splay

Abstract. Geomorphic impacts of a disastrous crevasse splay that formed in September 2015 and its post-formation modifications were quantitatively documented by using multitemporal, high-definition digital surface models (DSMs) of an inhabited and cultivated floodplain of the Kinu River, central Japan. The DSMs used were based on pre-flood (resolution, 2 m) and post-flood (resolution, 1 m) aerial light detection and ranging (LiDAR) data from January 2007 and September 2015, respectively, and structure-from-motion (SfM) photogrammetry data (resolution, 3.8 cm) derived from aerial photos taken by an unmanned aerial vehicle (UAV) in December 2015. After elimination of systematic errors among the DSMs, differential DSMs were produced by subtraction and topographic changes on the order of 10−1 m were detected. These changes were found to be consistent with previously reported ground survey data. The detected changes included not only topographic changes but also growth of vegetation, vanishing of floodwaters, and restoration and repair works carried out by people. The results suggest that DSMs with different resolutions and acquisition periods acquired by using a combination of UAV-SfM and LiDAR data can be used to quantify, rapidly and in rich detail, sudden topographic changes on floodplains caused by floods. Moreover, they have the great advantage that they can be used to archive such changes that occur in residential areas and urban areas where their preservation potential is low.

scholarly journals Intercomparison of Water Vapor Data Measured with Lidar during IHOP_2002. Part I: Airborne to Ground-Based Lidar Systems and Comparisons with Chilled-Mirror Hygrometer Radiosondes

2007 ◽  
Vol 24 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Andreas Behrendt ◽  
Volker Wulfmeyer ◽  
Hans-Stefan Bauer ◽  
Thorsten Schaberl ◽  
Paolo Di Girolamo ◽  
...  
Keyword(s):  
Water Vapor ◽  
Sea Level ◽  
Great Plains ◽  
Ground Level ◽  
Low Noise ◽  
Lidar Data ◽  
Equal Weight ◽  
Height Range ◽  
Nasa Goddard Space ◽  
Ground Based Lidar

Abstract The water vapor data measured with airborne and ground-based lidar systems during the International H2O Project (IHOP_2002), which took place in the Southern Great Plains during 13 May–25 June 2002 were investigated. So far, the data collected during IHOP_2002 provide the largest set of state-of-the-art water vapor lidar data measured in a field campaign. In this first of two companion papers, intercomparisons between the scanning Raman lidar (SRL) of the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and two airborne systems are discussed. There are 9 intercomparisons possible between SRL and the differential absorption lidar (DIAL) of Deutsches Zentrum für Luft- und Raumfahrt (DLR), while there are 10 intercomparisons between SRL and the Lidar Atmospheric Sensing Experiment (LASE) of the NASA Langley Research Center. Mean biases of (−0.30 ± 0.25) g kg−1 or −4.3% ± 3.2% for SRL compared to DLR DIAL (DLR DIAL drier) and (0.16 ± 0.31) g kg−1 or 5.3% ± 5.1% for SRL compared to LASE (LASE wetter) in the height range of 1.3–3.8 km above sea level (450–2950 m above ground level at the SRL site) were found. Putting equal weight on the data reliability of the three instruments, these results yield relative bias values of −4.6%, −0.4%, and +5.0% for DLR DIAL, SRL, and LASE, respectively. Furthermore, measurements of the Snow White (SW) chilled-mirror hygrometer radiosonde were compared with lidar data. For the four comparisons possible between SW radiosondes and SRL, an overall bias of (−0.27 ± 0.30) g kg−1 or −3.2% ± 4.5% of SW compared to SRL (SW drier) again for 1.3–3.8 km above sea level was found. Because it is a challenging effort to reach an accuracy of humidity measurements down to the ∼5% level, the overall results are very satisfactory and confirm the high and stable performance of the instruments and the low noise errors of each profile.

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