Monitoring Coastal Erosion Using Remote Images: Comparison between Physically and Remotely Acquired Data on a Limestone Coast

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Abstract

Boulder-sized clasts on rocky coasts are considered as erosional signatures of extreme wave events and boulder attributes are often used in numerical models to estimate wave characteristics. The use of unmanned aerial vehicle (UAV) technology and related software has facilitated the monitoring of coastal areas, by generating models from which 2D and 3D measurements can be derived. However, the reliability and preciseness of such measurements is still to be determined. This study seeks to analyse the accuracy of boulder measurements by comparing the dimension data obtained through in-situ measurements with ex situ data generated from digital models, based on UAV images. The study area is a bouldered sloping coast located on the southeast coast of Malta (Central Mediterranean) that has developed into multiple limestone dipped strata with a fractured and heavily jointed morphology. The dimensions of c. 200 boulders in different morphological settings, such as clusters or ridges, have been statistically compared. The results show a very strong correlation between the two datasets, both in 2D and 3D; however some notable differences were observed at the individual boulder level. For the majority of boulders analysed, the A and B axes dimensions varied by ±10% to 20%. The C axis proved to be harder to measure accurately and showed a wider range of difference. Boulder volume results in the majority of cases varied from 0% to ±40%. Some tested methods of volume calculation may be more accurate and realistic than others depending on the boulder position in relation to other clasts and shore morphology. An automated digital analysis of the terrain surface to identify the boulder extents may offer possibilities for a more accurate estimation of boulder attributes.

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Causon Deguara, J., Gauci, R., & Inkpen, R. (2023). Monitoring Coastal Erosion Using Remote Images: Comparison between Physically and Remotely Acquired Data on a Limestone Coast. Remote Sensing, 15(1). https://doi.org/10.3390/rs15010036

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