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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/esurf-2020-34
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-2020-34
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 May 2020

19 May 2020

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This preprint is currently under review for the journal ESurf.

Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

Mieke Kuschnerus1, Roderik Lindenbergh1, and Sander Vos2 Mieke Kuschnerus et al.
  • 1Department of Geoscience and Remote Sensing, Delft University of Technology
  • 2Department of Hydraulic Engineering, Deflt University of Technology

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

Mieke Kuschnerus et al.

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Mieke Kuschnerus et al.

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CoastScan: Data of daily scans at low tide Kijkduin January 2017 S. Vos, M. Kuschnerus, S. de Vries, and R. Lindenbergh https://doi.org/10.4121/uuid:409d3634-0f52-49ea-8047-aeb0fefe78af

Mieke Kuschnerus et al.

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Short summary
Sandy coasts are areas that undergo a lot of changes, which are caused by different influences, such as tides, wind or human activity. Permanent laser scanning is used to generate a 3-dimensional representations of a part of the coast continuously over an extended period. By comparing three unsupervised learning algorithms, we develop a methodology to analyse the resulting data set and derive which processes are dominating changes in the beach and dunes.
Sandy coasts are areas that undergo a lot of changes, which are caused by different influences,...
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