Decoding Complex Erosion Responses for the Mitigation of Coastal Rockfall Hazards Using Repeat Terrestrial LiDAR
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ID: 270632
2020
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Abstract
A key factor limiting our understanding of rock slope behavior and associated geohazards is the interaction between internal and external system controls on the nature, rates, and timing of rockfall activity. We use high-resolution, monthly terrestrial light detection and ranging (LiDAR) surveys over a 2 year monitoring period to quantify rockfall patterns across a 0.6 km-long (15.3 × 103 m2) section of a limestone rock cliff on the northeast coast of England, where uncertainty in rates of change threaten the effective planning and operational management of a key coastal cliff top road. Internal system controls, such as cliff material characteristics and foreshore geometry, dictate rockfall characteristics and background patterns of activity and demonstrate that layer-specific analyses of rockfall inventories and sequencing patterns are essential to better understand the timing and nature of rockfall risks. The influence of external environmental controls, notably storm activity, is also evaluated, and increased storminess corresponds to detectable rises in both total and mean rockfall volume and the volumetric contribution of large (>10 m3) rockfalls at the cliff top during these periods. Transient convergence of the cumulative magnitude–frequency power law scaling exponent (ɑ) during high magnitude events signals a uniform erosion response across the wider cliff system that applies to all lithologies. The tracking of rockfall distribution metrics from repeat terrestrial LiDAR in this way demonstrably improves the ability to identify, monitor, and forecast short-term variations in rockfall hazards, and, as such, provides a powerful new approach for mitigating the threats and impacts of coastal erosion.
| Reference Key |
westoby2020remotedecoding
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| Authors | Matthew Westoby;Michael Lim;Michelle Hogg;Lesley Dunlop;Matthew Pound;Mateusz Strzelecki;John Woodward;Westoby, Matthew;Lim, Michael;Hogg, Michelle;Dunlop, Lesley;Pound, Matthew;Strzelecki, Mateusz;Woodward, John; |
| Journal | remote sensing |
| Year | 2020 |
| DOI |
10.3390/rs12162620
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| URL | |
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