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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -