Multi-Directional 3D topographic change

News

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Objective

Processes of topographic change in natural landscapes feature different magnitudes, directions and timescales of occurrence. Our point cloud-based methods derive change in multiple directions and reduce the uncertainty associated to measured change.

This allows to quantify multi-directional 3D topographic change with low uncertainties and accounts for the complexity of surface change processes in natural landscapes.

Methods
Dominant direction of movement (DMD)

Key features of the method by Williams et al. (2021) are that

  • the approach automatically ascertains the DMD unique to each point, and the magnitude of movement along this direction
  • the approach is versatile where different types of change occur within the same scene. It differs from change along the surface normal by ensuring that the magnitudes of different styles of movement can be accurately recorded irrespective of their direction
  • the DMD adapts to movement rather than to the local surface and can provide more relevant and accurate measures of change where the movement is not orthogonal to the surface

The approach provides an alternative view of movement where change processes operate in a direction that is not surface-normal, the underlying process(es) may not be known, movement(s) across the point cloud scene is not oriented along a single axis or where overall displacement of a surface or feature is needed.

MultiDirChange

Multi-directional change quantified along the dominant movement direction (Williams et al.,2021).

Correspondence-driven plane-based M3C2 (CD-PB-M3C2)

Key features of the method by Zahs et al. (2021; in review) are that

  • change is quantified between homologous planar surfaces of successive 3D point clouds
  • the method uses a larger neighborhood and a better plane fit for the quantification of uncertainty, compared to the standard M3C2 (Lague et al. 2013)
  • measured change is not affected by multiple surfaces in the local neighborhood and can be related directly to the moving rigid object
  • by tracking simple planar segments of rigid objects, the geometric complexity of the objects that need to be identified to compute change between two point clouds is greatly reduced compared to object tracking approaches

The correspondence-driven plane-based M3C2 approach quantifies small-scale topographic change in photogrammetric or laser scanning point clouds with low uncertainties in natural landscape settings that are characterised by generally rough surface morphology and by single rigid objects with planar faces (e.g. rock glaciers, landslides, debris covered glaciers).

MultiDirChange2

Change quantification between corresponding planar surfaces (Zahs et al., 2021; forthcoming).

Example use case: Rock glacier monitoring

We applied both methods for the use case of topographic change monitoring at an alpine rock glacier where different processes of surface change (e.g. frost heave, rock glacier creep, individual boulder movement) have shown to be dominant at different times of a year and their disaggregation requires monitoring at high frequency (Ulrich et al., 2021).

Our main findings:

  • Dominant direction of movement: Estimating change between successive terrestrial laser scanning point clouds yielded an increase in the number of points with a recorded change value compared to change quantification along the surface normal vector.
  • Correspondence-driven plane-based M3C2: The uncertainty of measured surface change between successive terrestrial laser scanning point clouds was reduced to around 1 cm. By this, significant change was detected for large parts of the rock glacier (75 % of the area and for around 500,000 corresponding planar surfaces).

The studies are presented with all methodological details in Ulrich et al. (2021), Williams et al. (2021).

Research

Multi-directional change analysis is featured in the following scientific publications:

Related Projects
  • AHK-4D: High-resolution and high-frequency monitoring of the rock glacier Äußeres Hochebenkar (AHK) in Austria.
  • Auto3DScapes: Autonomous 3D Earth observation of dynamic landscapes
  • GEODYNAMO4D: Tracing geographic dynamics on 4D Point Clouds.
  • Geomorph4D: Characterising multi-process geomorphic change.
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Latest Revision: 2021-06-02
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