Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

Paweł Terefenko, Dominik Paprotny, Andrzej Giza, Oswaldo Morales Napoles, Adam Kubicki, Szymon Walczakiewicz

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)
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Cliff coasts are dynamic environments that can retreat very quickly. However, the
short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The
results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.
Original languageEnglish
Article number843
Number of pages16
JournalRemote Sensing
Issue number7
Publication statusPublished - 2019


  • cliff coastlines
  • Non-parametric Bayesian network
  • Southern Baltic Sea
  • Terrestrial laser scanner
  • Time-series analysis


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