Detection and analysis of mass wasting events in chalk sea cliffs using UAV photogrammetry

Coastal cliff recession represents a significant risk to both people and infrastructure, it is therefore important that we are able to efficiently monitor these environments to inform future management decisions. Through the use of UAV digital photogrammetry, we obtain point clouds to develop monthly models of the sea cliffs at Telscombe, East Sussex, UK between August 2016 and July 2017. The models captured were accurate to 0.05 m and had an average point density of 351 pts./m(2). Using the methodology presented we were able to automatically detect rockfalls by undertaking a 2.5D surface change detection which populated monthly inventories through volumetric estimations. A total of 10,085 failures were observed with an estimated volumetric flux of 3889.4 m(3) over the 12 month period of data collection. Due to the high frequency of data capture, successive block failures in the Newhaven Chalk formation were observed. The largest failure within the 12 month period was estimated at 2546.8 m(3) and followed significant toe erosion due to wave action. The steepening of the cliff face was modelled through limit equilibrium analysis to determine the reduction in factor of safety for the months preceding failure. We then present a magnitude-frequency analysis using negative power laws from the monthly rockfall inventories for the entire study area. The negative power law models produced a strong correlation across all months with r(2) values ranging from 0.97 to 0.99. The normalised power law scaling parameters ranged from 1.421 to 1.955 for beta and from 33.79 to 904.14 for s. The observed rollover in power laws presented in previous research matches the resolution of the data presented in this study. Our results show that this method of data capture is comparable to existing methods whilst offering significant benefits in field surveying time and cost.