The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1039–1046, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1039-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1039–1046, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1039-2019

  05 Jun 2019

05 Jun 2019

CLUSTERING TIME SERIES OF REPEATED SCAN DATA OF SANDY BEACHES

R. Lindenbergh1, S. van der Kleij1, M. Kuschnerus1, S. Vos2, and S. de Vries2 R. Lindenbergh et al.
  • 1Dept. Geoscience & Remote Sensing, Delft University of Technology, Delft, The Netherlands
  • 2Dept. of Hydraulic Engineering, Delft University of Technology, Delft, The Netherlands

Keywords: permanent laser scanning, environmental monitoring, time series, change detection

Abstract. Sandy beaches are highly dynamic areas affected by different natural and anthropogenic effects. Large changes, caused by a storm for example, are in general well-understood and easy to measure. Most times, only small changes, at the centimeter scale, are occurring, but these changes accumulate significantly over periods from weeks to months. Laser scanning is a suitable technique to measure such small signals, as it is able to obtain dense 3D terrain data at centimeter level in a time span of minutes. In this work we consider two repeated laser scan data sets of two different beaches in The Netherlands. The first data set is from around the year 2000 and consists of six consecutive yearly airborne laser scan data sets of a beach on Texel. The second data set is from 2017 and consists of 30 consecutive daily terrestrial scans of a beach near The Hague. So far, little work has been done on time series analysis of repeated scan data. To obtain a first grouping of morphologic processes, we propose to use a simple un-supervised clustering approach, k-means clustering, on de-leveled, cumulative point-wise time series. The results for both regions of interest, obtained using k=5 and k=10 clusters, indicate that such clustering gives a meaningful decomposition of the morphological laser scan data into clusters that exhibit similar change patterns. At the same time, we realize that the chosen approach is just a first step in a wide open topic of clustering spatially correlated long time series of morphological laser scan data as are now obtained by permanent laser scanning.