International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 1225–1229, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-1225-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 1225–1229, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-1225-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  08 Feb 2020

08 Feb 2020

A COMPARISON BETWEEN STRUCTURE-FROM-MOTION AND TERRESTRIAL LASER SCANNING FOR DERIVING SURFACE ROUGHNESS: A CASE STUDY ON A SANDY TERRAIN SURFACE

L. Fan L. Fan
  • Department of Civil Engineering, Xi’an Jiaotong – Liverpool University, Suzhou, China

Keywords: Point Clouds, Surface Roughness, Structure-From-Motion, Terrestrial Laser Scanning

Abstract. Structure-from-motion (SfM) is a useful technique for acquiring the topographic information of terrain surfaces for a wide range of geoscience applications. Due to its easy mobilization and cost-effective implementation, the SfM technique may be considered as a favourable alternative to the laser scanning technique in some applications. To this end, it is essential to understand how point cloud data derived using these two different surveying techniques affect the geographic information system (GIS) outputs such as local surface roughness of a terrain surface. In this case study, a small sandy terrain surface was surveyed using a terrestrial laser scanner and the digital camera of a mobile phone, respectively. Analyses were carried out to check the measurement quality of the SfM-derived point cloud and to explore the differences in local surface roughness calculated using the SfM-derived point cloud and that from the scanner, respectively. In addition, it looked into how those differences were affected by different surface roughness descriptors and the associated input parameters (mainly window sizes). Two commonly used methods for describing local surface roughness were considered, consisting of root mean square height and standard deviation of slope.