Document Type
Article
Publication Date
6-1-2023
Department
Michigan Tech Research Institute; Department of Geological and Mining Engineering and Sciences
Abstract
Rapid slope instabilities (i.e., rockfalls) involving highway networks in mountainous areas pose a threat to facilities, settlements and life, thus representing a challenge for asset management plans. To identify different morphological expressions of degradation processes that lead to rock mass destabilization, we combined satellite and uncrewed aircraft system (UAS)-based products over two study sites along the State Highway 133 sector near Paonia Reservoir, Colorado (USA). Along with a PS-InSAR analysis covering the 2017–2021 interval, a high-resolution dataset composed of optical, thermal and multi-spectral imagery was systematically acquired during two UAS surveys in September 2021 and June 2022. After a pre-processing step including georeferencing and orthorectification, the final products were processed through object-based multispectral classification and change detection analysis for highlighting moisture or lithological variations and for identifying areas more susceptible to deterioration and detachments at the small and micro-scale. The PS-InSAR analysis, on the other hand, provided multi-temporal information at the catchment scale and assisted in understanding the large-scale morpho-evolution of the displacements. This synergic combination offered a multiscale perspective of the superimposed imprints of denudation and mass-wasting processes occurring on the study site, leading to the detection of evidence and/or early precursors of rock collapses, and effectively supporting asset management maintenance practices.
Publication Title
Remote Sensing
Recommended Citation
Zocchi, M.,
Kasaragod, A.,
Jenkins, A.,
Cook, C.,
Dobson, R.,
Oommen, T.,
Van Huis, D.,
Taylor, B.,
Brooks, C.,
Marini, R.,
Troiani, F.,
&
Mazzanti, P.
(2023).
Multi-Sensor and Multi-Scale Remote Sensing Approach for Assessing Slope Instability along Transportation Corridors Using Satellites and Uncrewed Aircraft Systems.
Remote Sensing,
15(12).
http://doi.org/10.3390/rs15123016
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17390
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Publisher's Statement
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. Publisher’s version of record: https://doi.org/10.3390/rs15123016