Cost-effective erosion monitoring of coastal cliffs

Westoby, Matt, Lim, Michael, Hogg, Michelle, Pound, Matthew, Dunlop, Lesley and Woodward, John (2018) Cost-effective erosion monitoring of coastal cliffs. Coastal Engineering, 138. pp. 152-164. ISSN 0378-3839

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Abstract

Structure-from-motion with multi-view stereo methods (SfM-MVS) hold the potential for monitoring and quantifying cliff erosion to levels of accuracy and precision which rival terrestrial laser scanning (TLS) and at a fraction of the cost. We benchmark repeat SfM-MVS against TLS for quantifying rock fall frequency, volume, and cliff face erosion rates for a ∼1km section of coastal cliffs where cliff top infrastructure is threatened by erosion. First, we address a major unknown in these techniques, the number and configuration of control points. Surveys demonstrate that a sparse configuration along the cliff base and top, at spacing equivalent to the cliff height, provides suitable accuracy at acceptable logistic time and expense. Second, we show that SfM-MVS models match equivalent TLS data to within 0.04m, and that the correlation between intersecting TLS- and SfM-derived rock fall volumes improves markedly above a detection threshold of 0.07m3. Rock falls below this size threshold account for ∼77.7% of detected rock falls but only 1.9% of the calculated annual eroded volume. Annual erosion rates for the 1km cliff face as calculated by repeat TLS and SfM differencing are 0.6×10−2m a−1 and 0.7×10−2m a−1, respectively. Kilometre-scale patterns of cliff erosion are dominated by localised zones of high-magnitude, episodic failure that are over an order of magnitude greater than background rates. The ability of non-specialist engineers, geologists, geomorphologists and managers to rapidly capture high quality, accurate erosion data in a cost-effective manner through repeat SfM-MVS has significant potential to inform coastal managers and decision makers. To further empower coastal authorities and communities, policy frameworks must be developed to incorporate and interpret these data.

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