Rockfall hazard rating system: benefits of utilizing remote sensing
Transportation corridor slopes have the potential to be hazardous to adjacent assets. The Rockfall Hazard Rating System (RHRS) is a stepwise process designed to identify potentially hazardous slopes by assigning a hazard rating that determines the order by which to mitigate and remediate slopes. The traditional RHRS approach is field-based: observations are made by a field crew who convert observations into slope ratings (preliminary and detailed). The purpose of this study is to examine the benefits of utilizing remote sensing techniques on 14 slopes within a 24-km railroad corridor in southeastern Nevada. Remote sensing allows for data acquisition in difficult-to-reach locations from various view angles. Images and data from three remote sensing technique-platform combinations are examined: optical imagery acquired via satellite, unmanned aerial vehicle, and LiDAR data acquired from a stationary sensor. Detailed RHRS slope ratings from both sets of optical images are compared to two types of field-based ratings: (1) initial field observations performed using the traditional RHRS approach and (2) average detailed rating scores from six participants (geologists and geotechnical engineers) given field notes of the 10 rating criteria for the 14 slopes. Terrestrial LiDAR is capable of monitoring slow slope deformation, with an accuracy of approximately 2 cm/yr, and identifying areas of rapid deformation. Remote sensing techniques should not entirely replace traditional field methods. Instead, developing an approach that combines the advantages of field- and remote sensing–based methodologies will enable transportation agencies to ensure a more robust, efficient, and time-effective RHRS approach.
Environmental and Engineering Geoscience
Bouali, E. H.,
Escobar Wolf, R. P.,
Brooks, C. N.
Rockfall hazard rating system: benefits of utilizing remote sensing.
Environmental and Engineering Geoscience,
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© ASSOCIATION OF ENVIRONMENTAL & ENGINEERING GEOLOGISTS. Publisher's version of record: https://doi.org/10.2113/gseegeosci.23.3.165