Project

AHK-4D - High-resolution and high-frequency monitoring of the rock glacier Äußeres Hochebenkar (AHK) in Austria

Goal: Project website: https://uni-heidelberg.de/ahk-4d

Related blog posts: http://k1z.blog.uni-heidelberg.de/tag/AHK4D/

The aim of this project is to develop a methodology to quantify the magnitudes and frequencies of individual surface change processes of a rock glacier over several years. We do this by analyzing three dimensional (3D) surface change based on high-resolution, high-frequency and multisource LiDAR data. The derived information will enable us to develop methods to automatically characterize and disaggregate multiple processes and mechanisms that contribute to surface change signals derived from less frequent monitoring (e.g. yearly). Such methods can enhance our general understanding of the spatial and temporal variability of rock glacier deformation and the interaction of rock glaciers with connected environmental systems.

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Project log

Vivien Zahs
added 2 research items
Point clouds derived from UAV-borne laser scanning and UAV-borne photogrammetry provide new opportunities for 3D topographic monitoring in geographic research. The airborne acquisition strategy overcomes common challenges of ground-based techniques, such as limited spatial coverage or heterogeneous measurement distribution, and allows flexible repeated acquisitions at high temporal and spatial resolution. While UAV-borne 3D sensing techniques are expected to thereby enhance geographic monitoring, their specific potential for methods and algorithms of 3D change analysis is yet to be investigated. In our study, we assess point clouds originating from UAV-borne photogrammetry using dense image matching (DIM) and UAV-borne laser scanning (ULS) as input for 3D topographic change analysis at an active rock glacier in the Austrian Alps. We analyse surface change by using ULS and DIM point clouds of 2019 and 2021 as input for two state-of-the-art methods for pairwise surface change analysis: (1) The Multiscale Model to Model Cloud Comparison (M3C2) algorithm and (2) a recent M3C2-based approach (CD-PB M3C2) using plane correspondences to reduce the uncertainty of quantified change. We evaluate ULS-based and DIM-based change analysis regarding their performance in (1) achieving high spatial coverage of derived changes, (2) accurately quantifying magnitudes and uncertainty of change, and (3) detecting significant change (change magnitudes > associated uncertainty). As reference we use change quantified between two terrestrial laser scanning (TLS) surveys undertaken simultaneously with the ULS and DIM data acquisitions. Our study shows the improved spatial coverage of M3C2 achieved with point clouds acquired with UAVs (+ 60% of core points used for change analysis). For CD-PB M3C2, ULS and DIM point clouds enabled a spatially more uniform distribution of plane pairs used for change quantification and a slightly higher spatial coverage (+6% – +7% of core points used for change analysis) compared to the TLS reference. Magnitudes of M3C2 change were closer to the TLS reference for ULS-ULS (mean difference: 0.04 m; std. dev.: 0.05 m) compared to ULS-DIM (mean difference: 0.12 m; std. dev.: 0.08 m). Similar results were obtained for CD-PB M3C2 using ULS-ULS (mean difference: 0.02 m; std. dev.: 0.01 m) and ULS-DIM (mean difference: 0.06 m; std. dev.: 0.01 m). Moreover, magnitudes of change were above the associated uncertainty in 82% – 89% (M3C2) and 89% – 90% (CD-PB M3C2) of the area of change analysis. Our findings demonstrate the potential of ULS and DIM point clouds as input for accurate 3D topographic change analysis for the study at hand and can support the design and setup of 3D/4D Earth observation systems for rock glaciers and natural scenes with complex topography, such as landslides or debris covered glaciers.
Vivien Zahs
added a research item
We analyse surface change by using ULS and DIM point clouds of 2019 and 2021 as input for two state-of-the-art methods for pairwise surface change analysis: (1) The Multiscale Model to Model Cloud Comparison (M3C2) algorithm and (2) a recent M3C2-based approach (CD-PB M3C2) using plane correspondences to reduce the uncertainty of quantified change. We evaluate ULS-based and DIM-based change analysis regarding their performance in (1) achieving high spatial coverage of derived changes, (2) accurately quantifying magnitudes and uncertainty of change, and (3) detecting significant change (change magnitudes > associated uncertainty). Our findings demonstrate the potential of ULS and DIM point clouds as input for accurate 3D topographic change analysis for the study at hand and can support the design and setup of 3D/4D Earth observation systems for rock glaciers and natural scenes with complex topography, such as landslides or debris covered glaciers.
Bernhard Höfle
added a research item
The analysis and interpretation of 3D topographic change requires methods that achieve low uncertainties in change quantification. Many recent geoscientific studies that perform point cloud-based topographic change analysis have used the Multiscale Model to Model Cloud Comparison (M3C2) algorithm to consider the associated uncertainty. Change measured with the M3C2 approach, however, is difficult to interpret where (1) change occurs in directions different to the direction of change computation or (2) the quantified magnitudes of change are exceeded by the associated uncertainty due to a rough surface morphology. We present a correspondence-driven plane-based M3C2 approach that is tailored to quantifying small-magnitude ( 0.1 m) 3D topographic change of rough surfaces by reducing the uncertainty of quantified change. The approach (1) extracts planar surfaces in point clouds of successive epochs, (2) identifies corresponding planar surfaces between two point clouds using a binary random forest classification, and (3) calculates M3C2 distances and the associated uncertainty between the corresponding planar surfaces. This correspondence-driven plane-based M3C2 does not require recognition or reconstruction of geometrically complex objects but instead quantifies change between less complex, homologous planar surfaces. The approach further allows to relate change directly to a moving object. We apply our approach to a bi-weekly time series of terrestrial laser scanning point clouds acquired at a rock glacier in the Austrian Alps. The approach enables a sevenfold reduction in the uncertainty associated with topographic change compared to standard M3C2. Significant change is therefore detected in 72.62% to 76.41% of the area of change analysis, whereas standard M3C2 detects significant change in only 16.21% (2-week timespan) to 59.96% (10-week timespan) of the same area. The correspondence-driven plane-based M3C2 complements 3D change analysis in applications that aim to quantify small-magnitude topographic change in photogrammetric or laser scanning point clouds with low uncertainties in natural scenes which are characterised by overall rough surface morphology and by individual rigid objects with planar surfaces (e.g., rock glaciers, landslides, debris covered glaciers).
Lukas Winiwarter
added a research item
The analysis of topographic time series is often based on bitemporal change detection and quantification. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the minimum detectable change. To analyse geomorphic change in point cloud data, the multiscale model-to-model cloud comparison (M3C2) approach is commonly applied, which provides a statistical significance test. This test assumes planar surfaces and a uniform registration error. For natural surfaces, the planarity assumption does not necessarily apply, in which cases the value of minimal detectable change (Level of Detection) is overestimated. To overcome these limitations, we quantify an uncertainty information for each 3D point by propagating the uncertainty of the measurements themselves and of the alignment uncertainty to the 3D points. This allows the calculation of 3D covariance information for the point cloud, which we use in an extended statistical test for equality of multivariate means. Our method, called M3C2-EP, gives a less biased estimate of the Level of Detection, allowing a more appropriate significance threshold in typical cases. We verify our method in two simulated scenarios, and apply it to a time series of terrestrial laser scans of a rock glacier at two different timespans of three weeks and one year. Over the three-week period, we detect significant change at 12.5% fewer 3D locations, while quantifying additional 25.2% of change volume, when compared to the reference method of M3C2. Compared with manual assessment, M3C2-EP achieves a specificity of 0.97, where M3C2 reaches 0.86 for the one-year timespan, while sensitivity drops from 0.72 for M3C2 to 0.60 for M3C2-EP. Lower Levels of Detection enable the analysis of high-frequency monitoring data, where usually less change has occurred between successive scans, and where change is small compared to local roughness. Our method further allows the combination of data from multiple scan positions or data sources with different levels of uncertainty. The combination using error propagation ensures that every dataset is used to its full potential.
Bernhard Höfle
added a research item
Topographic change at a given location usually results from multiple processes operating over different timescales. However, interpretations of surface change are often based upon single values of movement, measured over a specified time period or in a single direction. This work presents a method to help separate surface change types that occur at different timescales related to the deformation of an active rock glacier, drawing on terrestrial lidar monitoring at sub-monthly intervals. To this end, we derive 3D topographic changes across the Äußeres Hochebenkar rock glacier in the Ötztal Alps. These changes are presented as the relative contribution of surface change during a 3-week period (2018) to the annual surface change (2017–2018). They are also separated according to the spatially variable direction perpendicular to the local rock glacier surface (using point cloud distance computation) and a single main direction of rock glacier flow, indicated by movement of individual boulders. In a 1500 m2 sample area in the lower tongue section of the rock glacier, the contribution of the 3-week period to the annual change perpendicular to the surface is 20 %, compared with 6 % in the direction of rock glacier flow. Viewing change in this way, our approach provides estimates of surface change in different directions that are dominant at different times of the year. Our results demonstrate the benefit of more frequent lidar monitoring and, critically, the requirement for novel approaches to quantifying and disaggregating surface change, as a step towards rock glacier observation networks focusing on the analysis of 3D surface change over time.
Bernhard Höfle
added a research item
Point clouds continue to be acquired with greater accuracy and less occlusion over complex scenes, characterised by high roughness and topographic variation in all three dimensions. The most widely adopted approach to change detection, M3C2, measures change along the local surface normal, which varies between points and bypasses the uncertainties involved in mesh or DEM generation. While adaptive, this direction of comparison is nevertheless user-defined and becomes less relevant where the movement direction deviates from the surface normal. Measured change therefore also becomes less meaningful, as it is a projection onto this direction. Sliding of a failing slope, for example, is predominantly surface parallel rather than along the surface normal. We present an approach that derives a dominant movement direction (DMD) at each point based on multi-scale, multi-directional change quantifications. The DMDs differ from the surface normals in three LiDAR-derived test cases; a rockfall, an avalanche, and rock glacier movement, providing more accurate measures of rockfall depth and boulder movement across the rock glacier. When the direction of change detection is orthogonal to local relief (i.e. across the surface), a variable length search cylinder that intersects only a single (corresponding) surface is necessary during change detection. Where movement results in new regions of occlusion in the second point cloud, we show that the proportion of points for which no valid change could be recorded decreases by up to 15% using the DMD rather than the surface normal. We emphasise the importance of examining the direction over which change is measured, and highlight that a comparison direction that adapts to movement rather than to the local surface can provide more relevant and accurate measures of change where the movement is not orthogonal to the surface. Our approach represents a supplementary tool for cloud-to-cloud comparison, where a choice of tool should be made based on the expected DMD deviation from the surface normal.
Katharina Anders
added a research item
Topographic change at a given location usually results from multiple processes operating over different timescales. However, interpretations of surface change are often based upon single values of movement, measured over a specified time period and in a single direction. This work presents a method to help separate surface change mechanisms related to the deformation of an active rock glacier, drawing on terrestrial lidar monitoring at sub-monthly intervals. We derive 3D topographic changes across the Äußeres Hochebenkar rock glacier in the Ötztal Alps. These are presented as the relative contribution of surface change during a three-week period of snow-free conditions (2018) to the annual surface change (2017–2018). They are also separated according to the direction perpendicular to the local rock glacier surface (using point cloud distance computation) and the direction of rock glacier flow, indicated by movement of individual boulders. In a 1500 m2 sample area in the lower tongue section of the rock glacier, the contribution of the three-week period to the annual change perpendicular to the surface is 20 %, as compared to 6 % in the direction of rock glacier flow. This shows that different directions of surface change are dominant at different times of the year. Our results demonstrate the benefit of more frequent lidar monitoring and, critically, the requirement of novel approaches to detecting change, as a step towards interpreting the mechanisms that underlie the surface change of rock glaciers.
Vivien Zahs
added a research item
Change analysis of rock glaciers is crucial to analyzing the adaptation of surface and subsurface processes to changing environmental conditions at different timescales because rock glaciers are considered as potentially unstable slopes and solid water reservoirs. To quantify surface change in complex surface topographies with varying surface orientation and roughness, a full three‐dimensional (3D) change analysis is required. This study therefore proposes a novel approach for accurate 3D point cloud‐based quantification and analysis of geomorphological activity on rock glaciers. It is applied to the lower tongue area of the Äußeres Hochebenkar rock glacier, Ötztal Alps, Austria. Multi‐temporal and multi‐source topographic LiDAR data are used to quantify surface changes and to reveal their spatial and temporal characteristics at different timescales within the period 2006–2018. LiDAR‐based examinations are complemented with subsurface characteristics obtained from electrical resistivity tomography. This combined approach reveals active and variable spatial and temporal surface dynamics in the investigated area, with minimum detectable change between 0.09 and 0.65 m at 95% confidence. Given that this approach overcomes current uncertainties in established methods of differentiating complex rock glacier surfaces, we consider it a valuable addition that can be applied to objects of similar properties such as landslides or glaciers.
Vivien Zahs
added a project goal
The aim of this project is to develop a methodology to quantify the magnitudes and frequencies of individual surface change processes of a rock glacier over several years. We do this by analyzing three dimensional (3D) surface change based on high-resolution, high-frequency and multisource LiDAR data. The derived information will enable us to develop methods to automatically characterize and disaggregate multiple processes and mechanisms that contribute to surface change signals derived from less frequent monitoring (e.g. yearly). Such methods can enhance our general understanding of the spatial and temporal variability of rock glacier deformation and the interaction of rock glaciers with connected environmental systems.