scholarly journals Identification and Extraction of Geomorphological Features of Landslides Using Slope Units for Landslide Analysis

2020 ◽  
Vol 9 (4) ◽  
pp. 274
Author(s):  
Kai Wang ◽  
Hui Xu ◽  
Shaojie Zhang ◽  
Fangqiang Wei ◽  
Wanli Xie
Keyword(s):  
Yunnan Province ◽  
Extraction Methods ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Accurate Representation ◽  
Slope Unit ◽  
Chongqing Municipality ◽  
Jiangjia Gully ◽  
Landslide Stability ◽  
Geomorphological Features

A slope unit is commonly used as calculation unit for regional landslide analysis. However, the capacity of the slope unit to reflect the geomorphological features of actual landslides still needs to be verified. This is because such accurate representation is critical to ensure the physical meaning of results from subsequent landslide stability analysis. This paper presents work conducted on landslides and slope extraction in two areas in China: The Jiangjia Gully area (Yunnan Province) and Fengjie County (Chongqing Municipality). Ground-based light detection and ranging (LiDAR) data are combined with field landslide terrace measurements to allow for the comparison of slope unit extraction methods (conventional vs. MIA-HSU) in terms of their ability to reflect the geomorphological features of shallow and deep-seated landslides. The results indicate that slope unit boundaries extracted by the conventional method do not match the geomorphological variations of actual landslides, and the method is therefore deficient in meaningfully extracting slope units for further landslide analysis. By contrast, slope units obtained using the MIA-HSU method accurately reflects the geomorphological features of both shallow and deep-seated landslides, and thus provides clearer geomorphological meaning and more reasonable calculation units for regional landslide assessment and prediction.

scholarly journals Analysis and Prediction of Gap Dynamics in a Secondary Deciduous Broadleaf Forest of Central Japan Using Airborne Multi-LiDAR Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 100
Author(s):  
Kazuho Araki ◽  
Yoshio Awaya
Keyword(s):  
Linear Regression Analysis ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Gap Dynamics ◽  
Central Japan ◽  
Broadleaf Forest ◽  
Strong Linear Correlation ◽  
Using Data ◽  
Deciduous Broadleaf Forest ◽  
Lidar Observations

Gaps are important for growth of vegetation on the forest floor. However, monitoring of gaps in large areas is difficult. Airborne light detection and ranging (LiDAR) data make precise gap mapping possible. We formulated a method to describe changes in gaps by time-series tracking of gap area changes using three digital canopy height models (DCHMs) based on LiDAR data collected in 2005, 2011, and 2016 over secondary deciduous broadleaf forest. We generated a mask that covered merging or splitting of gaps in the three DCHMs and allowed us to identify their spatiotemporal relationships. One-fifth of gaps merged with adjacent gaps or split into several gaps between 2005 and 2016. Gap shrinkage showed a strong linear correlation with gap area in 2005, via lateral growth of gap-edge trees between 2005 and 2016, as modeled by a linear regression analysis. New gaps that emerged between 2005 and 2011 shrank faster than gaps present in 2005. A statistical model to predict gap lifespan was developed and gap lifespan was mapped using data from 2005 and 2016. Predicted gap lifespan decreased greatly due to shrinkage and splitting of gaps between 2005 and 2016.

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 Lessons from LiDAR data use in the Netherlands

2017 ◽  
Vol 1 (2) ◽  
pp. 661-670 ◽  
Author(s):  
Willem Frans Beex
Keyword(s):  
The Netherlands ◽  
Best Practices ◽  
Cultural Heritage ◽  
Land Surface ◽  
New Technology ◽  
Data Use ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Natural Land ◽  
Imaging Detection

Light Detection And Ranging or Laser Imaging Detection And Ranging (LiDAR) is not really a new technology. However, it does provide the data from which accurate models of the natural land surface completely stripped of buildings and vegetation can be derived. Interestingly for Cultural Heritage and Archaeology, most of the data is already freely available for research. This is certainly the case in the Netherlands, with the “Actueel Hoogtemodel Nederland 2”, or “AHN2”. The density of the measured points is at least 50 centimetres, which means that the remains of structures larger than one by one metre can be detected. As a result, many unknown structures have been discovered with it. However, these excellent results have blinded many Cultural Heritage and Archaeology practitioners to obvious mistakes when interpreting LiDAR data. This paper is intended to highlight best-practices for the use of LiDAR data by Cultural Heritage professionals.

Rainfall thresholds for the occurrence of debris flows in the Jiangjia Gully, Yunnan Province, China

2015 ◽  
Vol 195 ◽  
pp. 335-346 ◽  
Author(s):  
Jianqi Zhuang ◽  
Peng Cui ◽  
Gonghui Wang ◽  
Xiaoqing Chen ◽  
Javed Iqbal ◽  
...  
Keyword(s):  
Debris Flows ◽  
Yunnan Province ◽  
Rainfall Thresholds ◽  
Jiangjia Gully

scholarly journals Tunnel Vision

2016 ◽  
Vol 4 (2) ◽  
pp. 192-204 ◽  
Author(s):  
Thomas G. Garrison ◽  
Dustin Richmond ◽  
Perry Naughton ◽  
Eric Lo ◽  
Sabrina Trinh ◽  
...  
Keyword(s):  
3D Models ◽  
Present Form ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Technical Expertise ◽  
Ancient Maya ◽  
Light Detection ◽  
Terrestrial Lidar ◽  
Tunnel Vision ◽  
El Zotz

AbstractArchaeological tunneling is a standard excavation strategy in Mesoamerica. The ancient Maya built new structures atop older ones that were no longer deemed usable, whether for logistical or ideological reasons. This means that as archaeologists excavate horizontal tunnels into ancient Maya structures, they are essentially moving back in time. As earlier constructions are encountered, these tunnels may deviate in many directions in order to document architectural remains. The resultant excavations often become intricate labyrinths, extending dozens of meters. Traditional forms of archaeological documentation, such as photographs, plan views, and profile drawings, are limited in their ability to convey the complexity of tunnel excavations. Terrestrial Lidar (light detection and ranging) instruments are able to generate precise 3D models of tunnel excavations. This article presents the results of a model created with a Faro™ Focus 3D 120 Scanner of tunneling excavations at the site of El Zotz, Guatemala. The lidar data document the excavations inside a large mortuary pyramid, including intricately decorated architecture from an Early Classic (A.D. 300–600) platform buried within the present form of the structure. Increased collaboration between archaeologists and scholars with technical expertise maximizes the effectiveness of 3D models, as does presenting digital results in tandem with traditional forms of documentation.

scholarly journals Lagrangian Coherent Structure Analysis of Terminal Winds: Three-Dimensionality, Intramodel Variations, and Flight Analyses

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Brent Knutson ◽  
Wenbo Tang ◽  
Pak Wai Chan
Keyword(s):  
Coherent Structures ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Flow Structures ◽  
Lidar Data ◽  
Two Dimensional ◽  
Lagrangian Coherent Structure ◽  
Flight Data ◽  
Velocity Information ◽  
Hong Kong International Airport

We present a study of three-dimensional Lagrangian coherent structures (LCS) near the Hong Kong International Airport and relate to previous developments of two-dimensional (2D) LCS analyses. The LCS are contrasted among three independent models and against 2D coherent Doppler light detection and ranging (LIDAR) data. Addition of the velocity information perpendicular to the LIDAR scanning cone helps solidify flow structures inferred from previous studies; contrast among models reveals the intramodel variability; and comparison with flight data evaluates the performance among models in terms of Lagrangian analyses. We find that, while the three models and the LIDAR do recover similar features of the windshear experienced by a landing aircraft (along the landing trajectory), their Lagrangian signatures over the entire domain are quite different—a portion of each numerical model captures certain features resembling those LCS extracted from independent 2D LIDAR analyses based on observations.

scholarly journals Sonar and LiDAR investigation of lineaments offshore between central New England and the New England seamounts, USA

2019 ◽  
pp. 057-091
Author(s):  
Ronald T. Marple ◽  
James D. Hurd, Jr.
Keyword(s):  
New England ◽  
New Hampshire ◽  
Gulf Of Maine ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Submarine Canyons ◽  
Multibeam Echosounder ◽  
Accreted Terranes ◽  
Isles Of Shoals ◽  
Regional Gravity

High-resolution multibeam echosounder (MBES) and light detection and ranging (LiDAR) data, combined with regional gravity and aeromagnetic anomaly maps of the western Gulf of Maine, reveal numerous lineaments between central New England and the New England seamounts. Most of these lineaments crosscut the NE-SWtrending accreted terranes, suggesting that they may be surface expressions of deep basement-rooted faults that have fractured upward through the overlying accreted terranes or may have formed by the upward push of magmas produced by the New England hotspot. The 1755 Cape Ann earthquake may have occurred on a fault associated with one of these lineaments. The MBES data also reveal a NW-SE-oriented scarp just offshore from Biddeford Pool, Maine (Biddeford Pool scarp), a 60-km-long, 20-km-wide Isles of Shoals lineament zone just offshore from southeastern New Hampshire, a 50-m-long zone of mostly low-lying, WNW-ESE-trending, submerged ridge-like features and scarps east of Boston, Massachusetts, and a ~180-km-long, WNW-ESE-trending Olympus lineament zone that traverses the continental margin south of Georges Bank. Three submarine canyons are sinistrally offset ~1–1.2 km along the Thresher canyon lineament of the Olympus lineament zone.

scholarly journals Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds

2014 ◽  
Vol XL-5 ◽  
pp. 629-632 ◽  
Author(s):  
Y. Yu ◽  
J. Li ◽  
H. Guan ◽  
D. Zai ◽  
C. Wang
Keyword(s):  
Point Cloud ◽  
Euclidean Distance ◽  
Point Clouds ◽  
Shape Descriptor ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Automated Extraction ◽  
Normalized Cut ◽  
Model Driven ◽  
Automated Algorithm

This paper presents an automated algorithm for extracting 3D trees directly from 3D mobile light detection and ranging (LiDAR) data. To reduce both computational and spatial complexities, ground points are first filtered out from a raw 3D point cloud via blockbased elevation filtering. Off-ground points are then grouped into clusters representing individual objects through Euclidean distance clustering and voxel-based normalized cut segmentation. Finally, a model-driven method is proposed to achieve the extraction of 3D trees based on a pairwise 3D shape descriptor. The proposed algorithm is tested using a set of mobile LiDAR point clouds acquired by a RIEGL VMX-450 system. The results demonstrate the feasibility and effectiveness of the proposed algorithm.

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