GSA Annual Meeting in Phoenix, Arizona, USA - 2019

Paper No. 72-11
Presentation Time: 4:15 PM


HARTSHORN, Evan J.1, MCDONALD, Eric V.2, PAGE, David3, SABOL, Donald Edwin2 and MINOR, Tim2, (1)Desert Research Institute, Division of Earth and Ecosystem Sciences, 2215 Raggion Pkwy, Reno, NV 89512, (2)Division of Earth & Ecosystem Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, (3)Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512

New techniques utilizing high resolution point-cloud data created with photogrammetry from UAS (Unmanned Aircraft Systems) imagery has broad applications towards modeling and evaluation of terrain roughness elements critical to vehicle mobility testing and evaluation. For vehicle mobility testing in extreme environments, it is critical that testing conditions are representative of their respective analogs and excessive use has not degraded testing environments to become unrepresentative of these extreme conditions. Photogrammetry with UAS imagery combined with geospatial modeling techniques were used to create several types of topographic datasets for automotive engineers, allowing for better understanding of natural conditions of terrain roughness prior to testing activity and to allow for evaluation of roughness lost through extensive vehicle use.

Previous techniques used topographic profile data collected using a vehicle mounted laser based system which were processed using existing techniques of roughness evaluation used by automotive engineers such as RMS (root mean square) and grade. Our UAS dataset results are compatible with these techniques, and analysis of these provides similar results. Additionally, modeled datasets can be produced using a “cut and fill” model which removes disturbed areas of the digital elevation model (DEM) and replaces the surface with elevation values from a best fit line or 3rd order polynomial curve perpendicular to the test course surface. This technique was iterated over entire regions of test courses in order to create a 3D pre-test course surface to measure RMS and grade from. This high resolution dataset and RMS combination allows for quantification of different sized roughness features ranging from washboards to hills. The purpose is to directly estimate the change that has occurred in vehicle test course conditions due to excessive use, and has the capability to be applied to other erosion based landform models.