4D Objects-By-Change: Change Analysis of Natural Surfaces using 3D Time Series

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News

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Objective

Geographic observation benefits from the increasing availability of time series of 3D geospatial data, which allow analysis of change processes at high temporal detail and over extensive periods. Time series-based change analysis can integrate the history of surface change by performing spatiotemporal segmentation with the concept of 4D objects-by-change (4D-OBC) (Anders et al., 2020). The method identifies areas in the scene where surfaces change similarly over time, within sub-periods in the time series at neighboring locations.

Key features of time series-based change analysis are:

• Removing the requirement to select and predefine periods for the analysis of changes
• Extracting change forms at different (unknown) timing, change rates, durations of change processes, and persistence of (temporary) change forms
• Separating spatially overlapping changes, which might be aggregated in bitemporal change information of a scene

The time series-based approach thereby enables a generic extraction of surface changes in their varying spatial and temporal extents from large and dense 4D geospatial data (Anders et al., 2021).

Example use case: Coastal Monitoring

We applied fully automatic spatiotemporal segmentation for the use case of coastal monitoring at a sandy beach in The Netherlands (CoastScan project by TU Delft). A time series of hourly 3D point clouds was acquired by permanent terrestrial laser scanning (TLS) over a period of five months (~4,000 epochs). The analysis of around 15 billion laser points in the entire observation period revealed more than 2,000 4D objects-by-change (4D-OBCs). These 4D-OBCs represent temporary accumulation or erosion of sand that occurred in different locations at varying magnitudes and across various time periods. The study is presented with all methodological details in Anders et al. (2021).

Research

Time series-based change analysis is featured in the following scientific publications:

• Anders, K., Eberlein, S., Höfle, B. (2022): Hourly Terrestrial Laser Scanning Point Clouds of Snow Cover in the Area of the Schneeferner, Zugspitze, Germany. PANGAEA. DOI: 10.1594/PANGAEA.941550.
• Anders, K., Winiwarter, L., Höfle, B. (2022): Automatic Extraction and Characterization of Natural Surface Changes from Near-Continuous 3D Time Series using 4D Objects-By-Change and Kalman Filtering. EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022. DOI: 10.5194/egusphere-egu22-4225.
• Anders, K., Winiwarter, L., Schröder, D. & Höfle, B. (2022): Integration of Kalman Filtering of Near-Continuous Surface Change Time Series into the Extraction of 4D Objects-By-Change. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2022, 973--980. DOI: 10.5194/isprs-archives-XLIII-B2-2022-973-2022.
• Anders, K., Winiwarter, L., Höfle, B. (2022): Improving change analysis from near-continuous 3D time series by considering full temporal information. IEEE Geoscience and Remote Sensing Letters, 19. DOI: 10.1109/LGRS.2022.3148920.
• Anders, K., Winiwarter, L., Höfle, B. (2022): Improving change analysis from near-continuous 3D time series by considering full temporal information. IEEE Geoscience and Remote Sensing Letters, 19. DOI: 10.1109/LGRS.2022.3148920
• Hulskemper, D., Anders, K., Antolínez, J.A.Á., Kuschnerus, M., Höfle, B., Lindenbergh, R. (2022): Characterization of Morphological Surface Activities derived from Near-Continuous Terrestrial LiDAR Time Series. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2/W2-2022, 53-60. DOI: 10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022.
• Vos, S., Anders, K., Kuschnerus, M., Lindenbergh, R., Höfle, B., Aarninkhof, S., de Vries, S. (2022): A high-resolution 4D terrestrial laser scan dataset of the Kijkduin beach-dune system, The Netherlands. Scientific Data, 9 (191). DOI: 10.1038/s41597-022-01291-9.
• Anders, K., Winiwarter, L., Höfle, B. (2021): Improving change analysis from near-continuous 3D time series by considering full temporal information [Data and Source Code]. heiDATA, V1. DOI: 10.11588/data/1L11SQ.
• Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R. C., Vos, S. E. & Höfle, B. (2021): Influence of Spatial and Temporal Resolution on Time Series-Based Coastal Surface Change Analysis using Hourly Terrestrial Laser Scans. ISPRS Annals of Photogrammy, Remote Sensing and Spatial Information Science, V-2-2021, 137–144. DOI: 10.5194/isprs-annals-V-2-2021-137-2021.
• Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R., Vos, S.E. & Höfle, B. (2021): Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 173, pp. 297-308. DOI: 10.1016/j.isprsjprs.2021.01.015.
• Anders, K., Winiwarter, L., Lindenbergh, R., Williams, J.G., Vos, S.E. & Höfle, B. (2020): 4D objects-by-change: Spatiotemporal segmentation of geomorphic surface change from LiDAR time series. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 159, pp. 352-363. DOI: 10.1016/j.isprsjprs.2019.11.025.
• Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R.C., Vos, S.E. & Höfle, B. (2020): Einfluss der zeitlichen Auflösung auf die raumzeitliche Segmentierung geomorphologischer Änderungsprozesse in 3D-Punktwolken. In: 40. Wissenschaftlich-Technische Jahrestagung der DGPF. Vol. 29, pp. 312-316.
• Anders, K., Lindenbergh, R.C., Vos, S.E., Mara, H., de Vries, S. & Höfle, B. (2019): High-frequency 3D Geomorphic Observation using Hourly Terrestrial Laser Scanning Data of a Sandy Beach. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. IV-2/W5, pp. 317-324. DOI: 10.5194/isprs-annals-IV-2-W5-317-2019.
• Eberlein, S., Anders, K., Höfle, B. (2019): Kontinuierliches Schneedecken-Monitoring mittels Zeitserien von 3D-Punktwolken aus automatischem terrestrischem Laserscanning. Umweltforschungsstation Schneefernerhaus GmbH: UFS Wissenschaftliche Resultate 2017/2018, pp. 66-69.
• Höfle, B., Anders, K. (2019): Auto3Dscapes - Autonomous 3D Earth Observation of Dynamic Landscapes. In: Zipf, A., Growe, A., Schmidt, S., Klonner, C.: Heidelberger Geographische Gesellschaft (HGG) Journal, 33, pp. 54-56.

Related Projects

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• Geomorph4D: Characterising multi-process geomorphic change.