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

Abstract

<jats:p>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. </jats:p>

Characterization of Morphological Surface Activities Derived From Near-Continuous Terrestrial Lidar Time Series

“…They are characterized by a large maximum height change and shorter duration. These aspects indicate that cluster 5 shows anthropogenic deposition by bulldozers, as it has already been identified that such activities occur in this part of the beach (Kuschnerus et al, 2021).…”

“…Previous research on morphological change extraction from such four-dimensional (4D) datasets, was focused on full time series clustering, and as such, identifying areas of homogeneous surface change over the full time series (Kuschnerus et al, 2021;Winiwarter et al, 2022). A drawback of these methods is that individual occurrences of surface activity are not separated.…”

Detecting Anthropogenic Volume Changes in Cross Sections of a Sandy Beach With Permanent Laser Scanning

“…Several methods have been explored to detect and localise changes in permanent laser scanning data, mostly focused on analysing elevation changes (see for example (Anders et al, 2021), (Kuschnerus et al, 2021a), (Brand et al, 2019)). General analysis of time series from remote sensing data for sudden changes has been presented for example by (Verbesselt et al, 2010) and (Kuenzer et al, 2015).…”

Surface Approximation of Coastal Regions: Lr B-Spline for Detection of Deformation Pattern

“…A surface to surface approximation comparison is shown in Figure 12 but not for other examples: it could indicate misleading differences in areas with missing information, i.e., with sparse and varying coverage. Kuschnerus et al (2021) stated that on a small area of the beach at the bottom of a footpath, sand accumulated after a storm. Afterwards, the beach was cleared, and the superfluous sand forms a pile.…”