Remote Sensing (Jul 2022)

Remote Sensing Application for Landslide Detection, Monitoring along Eastern Lake Michigan (Miami Park, MI)

  • Guzalay Sataer,
  • Mohamed Sultan,
  • Mustafa Kemal Emil,
  • John A. Yellich,
  • Monica Palaseanu-Lovejoy,
  • Richard Becker,
  • Esayas Gebremichael,
  • Karem Abdelmohsen

DOI
https://doi.org/10.3390/rs14143474
Journal volume & issue
Vol. 14, no. 14
p. 3474

Abstract

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We assessed the nature and spatial and temporal patterns of deformation over the Miami Park bluffs on the eastern margin of Lake Michigan and investigated the factors controlling its observed deformation. Our approach involved the following steps: (1) extracting bluff deformation rates (velocities along the line of sight of the satellite) using a stack of Sentinel-1A radar imagery in ascending acquisition geometry acquired between 2017 and 2021 and applying the Intermittent Small Baseline Subset (ISBAS) InSAR time series analysis method; (2) generating high-resolution (5 cm) elevation models and orthophotos from temporal unmanned aerial vehicle (UAV) surveys acquired in 2017, 2019, and 2021; and (3) comparing the temporal variations in mass wasting events to other relevant datasets including the ISBAS-based bluff deformation time series, lake level (LL) variations, and local glacial stratigraphy. We identified areas witnessing high line-of-sight (LOS) deformation rates (up to −21 mm/year) along the bluff from the ISBAS analysis and seasonal deformation patterns associated with freeze-thaw cycles, suggesting a causal effect. The acceleration of slope failures detected from field and UAV acquisitions correlated with high LLs and intensified onshore wave energy in 2020. The adopted methodology successfully predicts landslides caused by freezes and thaws of the slope face by identifying prolonged slow deformation preceding slope failures, but it does not predict the catastrophic landslides preceded by short-lived LOS deformation related to LL rise.

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