Estimation of ground deformation caused by the earthquake (M7.2) in Japan, 2008, from the geomorphic image analysis of high resolution LiDAR DEMs

Mukoyama, Sakae

doi:10.1007/s11629-011-2106-7

Cited by 26 publications

(9 citation statements)

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“…The resultant discontinuity in displacements across the Sanhazama river may reflect (1) shallow faulting along the trend of the river channel or (2) NE-directed slumping of the southern flank of the Sanhazama valley. Superficially, our horizontal displacement field resembles the one obtained from particle image velocimetry (Mukoyama, 2012).…”

Section: Iwate-miyagi Lidar Differencingsupporting

confidence: 58%

“…A "sliding window" approach -coupled with careful filtering -could potentially be used to remove some of the noise, and other variants of ICP could also be tested (e.g., Rusinkiewicz and Levoy, 2001), including ones which incorporate spot height errors (Glennie et al, 2014). Other topography differencing techniques are also available including DEM pixel tracking (Leprince et al, 2012), point cloud cross-correlation (Borsa and Minster, 2012), and particle image velocimetry (Mukoyama, 2012;Aryal et al, 2012). There is potential in all of these methods to include the intensity values of the lidar returns as an additional constraint on horizontal displacements -as discussed by Borsa and Minster (2012) -although this adaptation is perhaps best suited to areas with sparser vegetation than in our Japan examples.…”

Section: Technical Issues With Respect To Lidar Differencingmentioning

confidence: 99%

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Coseismic fault zone deformation revealed with differential lidar: Examples from JapaneseMw∼7 intraplate earthquakes

Nissen

Maruyama

Arrowsmith

et al. 2014

Earth and Planetary Science Letters

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Section: Iwate-miyagi Lidar Differencingsupporting

confidence: 58%

Section: Technical Issues With Respect To Lidar Differencingmentioning

confidence: 99%

Coseismic fault zone deformation revealed with differential lidar: Examples from JapaneseMw∼7 intraplate earthquakes

Nissen

Maruyama

Arrowsmith

et al. 2014

Earth and Planetary Science Letters

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“…After removal of vegetation and other erroneous points, a 2D-3D displacement field can be obtained directly with a 3D piecewise Iter-1020 ative Closest Point (ICP) operation (Besl and McKay, 1992;Teza et al, 2007;Nissen et al, 2012), point cloud cross-correlation (Borsa and Minster, 2012), or with 2D correlation techniques (PIV) applied to a 2.5D DTM (Aryal et al, 2012;Mukoyama, 2012). Distance and volume can be quantified directly with a 3D cloud to cloud distance calculation (Lague et al, 2013), or with cloud to mesh 1025 comparisons (Rosser et al, 2005;Day et al, 2013a), or vertical subtraction of gridded data (Lane and Chandler, 2003;Wheaton et al, 2010;Schurch et al, 2011;Wheaton et al, 2013;Pelletier and Orem, 2014).…”

mentioning

confidence: 99%

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

Passalacqua

Belmont

Staley

et al. 2015

Earth-Science Reviews

282

245

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International audienceThe study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection of the most appropriate type of HRT data for a given application is not trivial. No definitive approach exists for identifying and filtering erroneous or unwanted data, yet inappropriate filtering can create artifacts or eliminate/distort critical features. Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses. For ease of use, gridded products are typically preferred rather than the more information-rich point cloud representations. Thus many users take advantage of only a fraction of the available data, which has furthermore been subjected to a series of operations often not known or investigated by the user. Lastly, standard HRT analysis work-flows are yet to be established for many popular HRT operations, which has contributed to the limited use of point cloud data.In this review, we identify key research questions relevant to the Earth-surface processes community within the theme of mass and energy transfer across landscapes and offer guidance on how to identify the most appropriate topographic data type for the analysis of interest. We describe the operations commonly performed from raw data to raster products and we identify key considerations and suggest appropriate work-flows for each, pointing to useful resources and available tools. Future research directions should stimulate further development of tools that take advantage of the wealth of information contained in the HRT data and address the present and upcoming research needs such as the ability to filter out unwanted data, compute spatially variable estimates of uncertainty and perform multi-scale analyses. While we focus primarily on HRT applications for mass and energy transfer, we envision this review to be relevant beyond the Earth-surface processes community for a much broader range of applications involving the analysis of HRT

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“…The method has now advanced beyond those experiments to a modern digital image co-registration and correlation technique. As a sensitive change detection technique, it is effective in geotechnical studies using close range photography and with the growth of LiDAR has been used in the field to study deformation after earthquakes and from landslides (Aryal et al, 2015(Aryal et al, , 2012Mukoyama, 2011). However, using 3D point clouds presents its own problems.…”

Section: Change Detection Techniquesmentioning

confidence: 99%

Airborne Light Detection and Ranging (Lidar) Derived Deformation From the Mw 6.0 24 August, 2014 South Napa Earthquake Estimated by Two and Three Dimensional Point Cloud Change Detection Techniques

Lyda¹,

Zhang²,

Glennie³

et al. 2016

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

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ABSTRACT:Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, "moving window," to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

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Estimation of ground deformation caused by the earthquake (M7.2) in Japan, 2008, from the geomorphic image analysis of high resolution LiDAR DEMs

Cited by 26 publications

References 1 publication

Coseismic fault zone deformation revealed with differential lidar: Examples from JapaneseMw∼7 intraplate earthquakes

Coseismic fault zone deformation revealed with differential lidar: Examples from JapaneseMw∼7 intraplate earthquakes

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

Airborne Light Detection and Ranging (Lidar) Derived Deformation From the Mw 6.0 24 August, 2014 South Napa Earthquake Estimated by Two and Three Dimensional Point Cloud Change Detection Techniques

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