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    Rights statement: This is the author’s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, 144, 2018 DOI: 10.1016/j.isprsjprs.2018.08.003

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A new multi-resolution based method for estimating local surface roughness from point clouds

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A new multi-resolution based method for estimating local surface roughness from point clouds. / Fan, Lei; Atkinson, P.M.

In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 144, 10.2018, p. 369-378.

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Fan, Lei ; Atkinson, P.M. / A new multi-resolution based method for estimating local surface roughness from point clouds. In: ISPRS Journal of Photogrammetry and Remote Sensing. 2018 ; Vol. 144. pp. 369-378.

Bibtex

@article{0217c4fe700447b192c4112b2fa71cc5,
title = "A new multi-resolution based method for estimating local surface roughness from point clouds",
abstract = "From some empirical and theoretical research on the digital elevation model (DEM) accuracy obtained for different source data densities, it can be observed that when the same degree of data reduction is applied to a whole area, the rate of change in the DEM error is statistically greater in local areas where the surface is rougher. Based on this observation, it is possible to characterize surface roughness or complexity from the differences between two digital elevation models (DEMs) built using point clouds that represent the same terrain surface but are of different spatial resolutions (or data spacings). Following this logic, a new approach for estimating surface roughness is proposed in this article. Numerical experiments are used to test the effectiveness of the approach. The study datasets considered in this article consist of four elevation point clouds obtained from terrestrial laser scanning (TLS) and airborne light detection and ranging (LiDAR). These types of topographical data are now used widely in Earth science and related disciplines. The method proposed was found to be an effective means of quantifying local terrain surface roughness. {\textcopyright} 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)",
keywords = "DEM error, Laser scanning, Point cloud, Terrain surface roughness, Digital instruments, Forestry, Geomorphology, Landforms, Laser applications, Numerical methods, Optical radar, Surveying, Surveying instruments, Three dimensional computer graphics, Digital elevation model, Light detection and ranging, Numerical experiments, Spatial resolution, Terrestrial laser scanning, Theoretical research, Surface roughness",
author = "Lei Fan and P.M. Atkinson",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, 144, 2018 DOI: 10.1016/j.isprsjprs.2018.08.003",
year = "2018",
month = oct,
doi = "10.1016/j.isprsjprs.2018.08.003",
language = "English",
volume = "144",
pages = "369--378",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
issn = "0924-2716",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - A new multi-resolution based method for estimating local surface roughness from point clouds

AU - Fan, Lei

AU - Atkinson, P.M.

N1 - This is the author’s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, 144, 2018 DOI: 10.1016/j.isprsjprs.2018.08.003

PY - 2018/10

Y1 - 2018/10

N2 - From some empirical and theoretical research on the digital elevation model (DEM) accuracy obtained for different source data densities, it can be observed that when the same degree of data reduction is applied to a whole area, the rate of change in the DEM error is statistically greater in local areas where the surface is rougher. Based on this observation, it is possible to characterize surface roughness or complexity from the differences between two digital elevation models (DEMs) built using point clouds that represent the same terrain surface but are of different spatial resolutions (or data spacings). Following this logic, a new approach for estimating surface roughness is proposed in this article. Numerical experiments are used to test the effectiveness of the approach. The study datasets considered in this article consist of four elevation point clouds obtained from terrestrial laser scanning (TLS) and airborne light detection and ranging (LiDAR). These types of topographical data are now used widely in Earth science and related disciplines. The method proposed was found to be an effective means of quantifying local terrain surface roughness. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

AB - From some empirical and theoretical research on the digital elevation model (DEM) accuracy obtained for different source data densities, it can be observed that when the same degree of data reduction is applied to a whole area, the rate of change in the DEM error is statistically greater in local areas where the surface is rougher. Based on this observation, it is possible to characterize surface roughness or complexity from the differences between two digital elevation models (DEMs) built using point clouds that represent the same terrain surface but are of different spatial resolutions (or data spacings). Following this logic, a new approach for estimating surface roughness is proposed in this article. Numerical experiments are used to test the effectiveness of the approach. The study datasets considered in this article consist of four elevation point clouds obtained from terrestrial laser scanning (TLS) and airborne light detection and ranging (LiDAR). These types of topographical data are now used widely in Earth science and related disciplines. The method proposed was found to be an effective means of quantifying local terrain surface roughness. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

KW - DEM error

KW - Laser scanning

KW - Point cloud

KW - Terrain surface roughness

KW - Digital instruments

KW - Forestry

KW - Geomorphology

KW - Landforms

KW - Laser applications

KW - Numerical methods

KW - Optical radar

KW - Surveying

KW - Surveying instruments

KW - Three dimensional computer graphics

KW - Digital elevation model

KW - Light detection and ranging

KW - Numerical experiments

KW - Spatial resolution

KW - Terrestrial laser scanning

KW - Theoretical research

KW - Surface roughness

U2 - 10.1016/j.isprsjprs.2018.08.003

DO - 10.1016/j.isprsjprs.2018.08.003

M3 - Journal article

VL - 144

SP - 369

EP - 378

JO - ISPRS Journal of Photogrammetry and Remote Sensing

JF - ISPRS Journal of Photogrammetry and Remote Sensing

SN - 0924-2716

ER -