Next Article in Journal
Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015
Previous Article in Journal
Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results
Previous Article in Special Issue
Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
Article Menu
Issue 7 (April-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(7), 843; https://doi.org/10.3390/rs11070843

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

1
Institute of Marine and Coastal Sciences, Faculty of Geosciences, University of Szczecin, 70-453 Szczecin, Poland
2
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
3
Section Hydrology, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
4
GEO Ingenieurservice Nord-West, 26386 Wilhelmshaven, Germany
*
Author to whom correspondence should be addressed.
Received: 15 March 2019 / Revised: 4 April 2019 / Accepted: 4 April 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
  |  
PDF [2254 KB, uploaded 8 April 2019]
  |  

Abstract

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. View Full-Text
Keywords: cliff coastlines; time-series analysis; terrestrial laser scanner; southern Baltic Sea; non-parametric Bayesian network cliff coastlines; time-series analysis; terrestrial laser scanner; southern Baltic Sea; non-parametric Bayesian network
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Terefenko, P.; Paprotny, D.; Giza, A.; Morales-Nápoles, O.; Kubicki, A.; Walczakiewicz, S. Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis. Remote Sens. 2019, 11, 843.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top