Characterization of morphological surface activities derived from near-continuous terrestrial lidar time series

Characterization of morphological surface activities derived from near-continuous terrestrial lidar time series

Hulskemper, D. (University of Heidelberg; Student TU Delft)
Anders, K. (University of Heidelberg)
Antolínez, José A. Á. (TU Delft Coastal Engineering)
Kuschnerus, M. (TU Delft Optical and Laser Remote Sensing)
Höfle, B. (University of Heidelberg)
Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing)

2022

The Earth's landscapes are shaped by processes eroding, transporting and depositing material over various timespans and spatial scales. To understand these surface activities and mitigate potential hazards they inflict (e.g., the landward movement of a shoreline), knowledge is needed on the occurrences and impact of these activities. Near-continuous terrestrial laser scanning enables the acquisition of large datasets of surface morphology, represented as three-dimensional point cloud time series. Exploiting the full potential of this large amount of data, by extracting and characterizing different types of surface activities, is challenging. In this research we use a time series of 2,942 point clouds obtained over a sandy beach in The Netherlands. We investigate automated methods to extract individual surface activities present in this dataset and cluster them into groups to characterize different types of surface activities. We show that, first extracting 2,021 spatiotemporal segments of surface activity using an object detection algorithm, and second, clustering these segments with a Self-organizing Map (SOM) in combination with hierarchical clustering, allows for the unsupervised identification and characterization of different types of surface activities present on a sandy beach. The SOM enables us to find events displaying certain type of surface activity, while it also enables the identification of subtle differences between different events belonging to one specific surface activity. Hierarchical clustering then allows us to find and characterize broader groups of surface activity, even if the same type of activity occurs at different points in space or time.

4D objects-by-change
Coastal monitoring
Self-organizing Map
Surface Activity
Terrestrial laser scanning

http://resolver.tudelft.nl/uuid:17866929-38b0-4df8-a78f-5133b915937b

https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022

1682-1750

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48 (2/W2-2022), 53-60

2022 Optical 3D Metrology, O3DM 2022, 2022-12-15 → 2022-12-16, Wurzburg, Germany

Institutional Repository

journal article

© 2022 D. Hulskemper, K. Anders, José A. Á. Antolínez, M. Kuschnerus, B. Höfle, R.C. Lindenbergh