Print Email Facebook Twitter Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis Title Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis Author Terefenko, Paweł (University of Szczecin) Paprotny, D. (TU Delft Hydraulic Structures and Flood Risk; Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences) Giza, Andrzej (University of Szczecin) Morales Napoles, O. (TU Delft Hydraulic Structures and Flood Risk) Kubicki, Adam (GEO Ingenieurservice Nord-West) Walczakiewicz, Szymon (University of Szczecin) Date 2019 Abstract Cliff coasts are dynamic environments that can retreat very quickly. However, theshort-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. Theresults 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. Subject cliff coastlinesNon-parametric Bayesian networkSouthern Baltic SeaTerrestrial laser scannerTime-series analysis To reference this document use: http://resolver.tudelft.nl/uuid:4a4710d9-fea2-46dd-9894-aed973204520 DOI https://doi.org/10.3390/rs11070843 ISSN 2072-4292 Source Remote Sensing, 11 (7) Part of collection Institutional Repository Document type journal article Rights © 2019 Paweł Terefenko, D. Paprotny, Andrzej Giza, O. Morales Napoles, Adam Kubicki, Szymon Walczakiewicz Files PDF remotesensing_11_00843.pdf 2.34 MB Close viewer /islandora/object/uuid:4a4710d9-fea2-46dd-9894-aed973204520/datastream/OBJ/view