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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.
Original languageEnglish
Pages (from-to)1039-1046
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
VolumeXLII
Issue number2/W13
DOIs
Publication statusPublished - 2019
Event4th ISPRS Geospatial Week 2019 - Enschede, Netherlands
Duration: 10 Jun 201914 Jun 2019
https://www.gsw2019.org

    Research areas

  • change detection, environmental monitoring, Permanent laser scanning, time series

ID: 54917721