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Slope Hazard Monitoring Using High-Resolution Satellite Remote Sensing

Lessons Learned from a Case Study

Bibliographic Data

ID22031573
AuthorsYuxiao Qin (0000-0001-6169-0795, Purdue University West Lafayette), Edward Hoppe (0000-0002-4281-3080, West Virginia Department of Transportation, corresponding author), Daniele Perissin (0000-0003-4086-8608, Purdue University West Lafayette)
Year2020
Volume9
Issue2
Pages131
Publication date2020-02-23
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9020131
OpenAlexW3007856704
LanguageEN
References cited22

In this study, a highway slope monitoring project for a section of US highway I-77 in Virginia was carried out with the InSAR technique. This paper attempts to provide insights into the complete and practical solution for the monitoring project, including two parts: what to consider for selecting the optimal satellites and configurations for the given area of interest (AoI) and budget; and how to best process the selected data for the monitoring purposes. To answer the first question, the simulated geometric distortion map, cumulative change detection map, intensity map, interferograms and coherence maps from all available historical datasets were generated. The satellite configuration that gives the best coherence and least geometric distortion with the given budget was selected for the monitoring project. For this project, it was the X-band COSMO stripmap with 3 m resolution and eight-days revisit time. To answer the second question, a multi-temporal InSAR (MTInSAR) was applied to retrieve the settlement time series of the slopes along the highway. Several special techniques were applied to increase the level of confidence, i.e., dividing AoI into smaller and independent areas, using a non-linear approach, etc. Finally, fieldwork was carried out for the interpretation and validation of the results. The AoI was overall stable, though some local changes were detected by the SAR signal which were validated by the fieldwork

Change detection · Computer vision · Data mining · Geography · Interferometric synthetic aperture radar · Remote sensing · Satellite · Statistics · Synthetic aperture radar · Telecommunications · Computer Science · Cryospheric studies and observations · Engineering · Landslides and related hazards · Mathematics · Synthetic Aperture Radar (SAR) Applications and Techniques

  • A Threshold Selection Method from Gray-Level Histograms

    Nobuyuki Otsu•IEEE Transactions on Systems,…•1979

Highly citedNo

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