Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/33370
Title: Assessment of LiDAR- and photogrammetry-based airfield roughness profiling techniques
Authors: Ward, Andrew B.
Newman, J. Kent.
Herring, G. Bryan.
Keywords: Roughness
Surface roughness
Friction--Testing
Airfield roughness
Optical radar
Photogrammetry
Surface roughness--Testing
Runways (Aeronautics)
Unmanned Aerial System (UAS)
Issue Date: Jun-2019
Publisher: Geotechnical and Structures Laboratory (U.S.)
Coastal and Hydraulics Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC TR-19-11
Abstract: The measurement of surface roughness is one of the factors necessary for determining stable landing and take-off conditions on airfields. The U.S. Army identified a need for rapid and precise assessment of airfield surface roughness without grossly inhibiting aircraft traffic. The present standard of roughness measurement includes the use of cumbersome ground vehicle-based inertial profilers or slow-speed profilographs. The use of Unmanned Aerial System (UAS) based photogrammetry for remote assessment has been highly successful and the data products derived from these systems are useful to surface roughness measurements. Roughness parameters like the Boeing Bump Index and International Roughness Index can be calculated using 3-D reconstructed surface data derived from photogrammetric techniques. Roughness parameters can also be calculated using the surface obtained by Light Detection and Ranging (LiDAR) systems. While LiDAR techniques are highly accurate and robust, they are often cost-prohibitive. The assessment of photogrammetry-based alternatives to LiDAR systems is required to satisfy airfield roughness measurement needs. Findings herein show that photogrammetric techniques can provide sufficient surface profiles for use in roughness measurement. This report compares multiple photogrammetric software packages for best correlation to actual surface profiles and concludes with a preferred method of surface roughness measurement using UAS-based photogrammetry.
Description: Technical Report
Gov't Doc #: ERDC TR-19-11
URI: https://hdl.handle.net/11681/33370
http://dx.doi.org/10.21079/11681/33370
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