New Paper: Correspondence-driven plane-based M3C2 for lower uncertainty in 3D topographic change quantification
Dec 15th, 2021 by Vivien Zahs
Check out our new paper presenting a novel approach (correspondence-driven plane-based M3C2) to lower the uncertainty in 3D topographic change quantification (get free access here until 2 February 2022)
Zahs, V., Winiwarter, L., Anders, K., Williams, J.G., Rutzinger, M. & Höfle, B. (2022): Correspondence-driven plane-based M3C2 for lower uncertainty in 3D topographic change quantification. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 183, pp. 541-559.
Key features of the method 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 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).
Overview of the correspondence-driven plane-based M3C2 approach to quantify change between homologous planar areas and the associated uncertainty based on point clouds of two epochs (Zahs et al., 2022).
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USE CASE: CHANGE MONITORING AT AN ALPINE ROCK GLACIER - We applied the method 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). With the 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; around 500,000 corresponding planar surfaces).
Overview map of the study site in the lower tongue area of the rock glacier Äußeres Hochebenkar, Austria (Zahs et al., 2022).
Differences in the significance of surface change derived from the correspondence-driven plane-based M3C2 and standard M3C2 for a 2-week, 4-week, and 10-week timespan (Zahs et al., 2022).
Winiwarter, L., Anders, K., Höfle, B. (2021): M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation. ISPRS Journal of Photogrammetry and Remote Sensing, 178, pp. 240–258. DOI: 10.1016/j.isprsjprs.2021.06.011.
Ulrich, V., Williams, J.G., Zahs, V., Anders, K., Hecht, S., Höfle, B. (2021): Measurement of rock glacier surface change over different timescales using terrestrial laser scanning point clouds. Earth Surface Dynamics. Vol. 9, pp. 19-28. DOI: 10.5194/esurf-9-19-2021.
Williams, J.G., Anders, K., Winiwarter, L., Zahs, V., Höfle, B. (2021): Multi-directional change detection between point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 172, pp. 95-113. DOI: 10.1016/j.isprsjprs.2020.12.002.
Ulrich, V., Williams, J.G., Zahs, V., Anders, K., Hecht, S., Höfle, B. (2020): Disaggregating surface change mechanisms of a rock glacier using terrestrial laser scanning point clouds acquired at different time scales. Earth Surface Dynamics Discussion. DOI: 10.5194/esurf-2020-55.
Zahs, V., Hämmerle, M., Anders, K., Hecht, S., Rutzinger, M., Sailer, R., Williams, J.G., Höfle, B. (2019): Multi-temporal 3D point cloud-based quantification and analysis of geomorphological activity at an alpine rock glacier using airborne and terrestrial LiDAR. Permafrost and Periglacial Processes. Vol. 30 (3), pp. 222-238. DOI: 10.1002/ppp.2004.
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