Surface velocity fields of active rock glaciers and ice‐debris complexes in the Central Andes of Argentina

Schmidt-hammer rebound values ( R-values) enable relative-age dating of landforms, with R-values relating to degree of weathering and therefore length of exposure. This method – recently termed as Schmidt-hammer exposure-age dating (SHD) – was applied to date five rock glaciers (size range, 0.01–0.12 km2) and one recent rockfall deposit at the study area Schöderkogel-Eisenhut, in the Schladminger Tauern Range (14°03′E, 47°15′N), Austria. The rock glaciers consist of gneiss or high metamorphic series of mica-schist that are comparable in their R-values. Four of them are relict (permafrost absent) and one is intact (containing patches of permafrost). On each of the five rock glaciers, SHD was carried out at 4–6 sites (50 measurements per site) along a longitudinal transect from the frontal ridge to the root zone. Results at all five rock glaciers are generally consistent with each other sharing statistically significant R-values along transects. The range between the highest and the lowest mean R-value at each of the five rock glaciers is 9.9–5.2. Using rock glacier length and surface velocity data from nearby sites, the rock glacier development must have lasted for several thousand years. Furthermore, by using SHD results from rock glaciers of known age from other sites in the region with comparable geology, approximate surface ages of 6.7–11.4 ka were estimated. This indicates long formation periods for all five rock glaciers. Our results suggest that many of the 1300 relict rock glaciers in central and eastern Austria were formed over a long period during the Lateglacial and Holocene period.

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Surface velocity fields from tidal interactions

10.1063/1.3250080 ◽

2009 ◽

Author(s):

Gloria Koenigsberger ◽

Edmundo Moreno ◽

David Harrington ◽

Ivan Hubeny ◽

James M. Stone ◽

...

Keyword(s):

Velocity Fields ◽

Surface Velocity ◽

Tidal Interactions

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Glacier Image Velocimetry: an open-source toolbox for easy and rapid calculation of high-resolution glacier velocity fields

The Cryosphere ◽

10.5194/tc-15-2115-2021 ◽

2021 ◽

Vol 15 (4) ◽

pp. 2115-2132

Author(s):

Maximillian Van Wyk de Vries ◽

Andrew D. Wickert

Keyword(s):

High Resolution ◽

Open Source ◽

Velocity Fields ◽

Surface Velocity ◽

Displacement Fields ◽

Desktop Computer ◽

Ice Cap ◽

Image Velocimetry ◽

Glacier Ice ◽

Glacier Velocity

Abstract. We present Glacier Image Velocimetry (GIV), an open-source and easy-to-use software toolkit for rapidly calculating high-spatial-resolution glacier velocity fields. Glacier ice velocity fields reveal flow dynamics, ice-flux changes, and (with additional data and modelling) ice thickness. Obtaining glacier velocity measurements over wide areas with field techniques is labour intensive and often associated with safety risks. The recent increased availability of high-resolution, short-repeat-time optical imagery allows us to obtain ice displacement fields using “feature tracking” based on matching persistent irregularities on the ice surface between images and hence, surface velocity over time. GIV is fully parallelized and automatically detects, filters, and extracts velocities from large datasets of images. Through this coupled toolchain and an easy-to-use GUI, GIV can rapidly analyse hundreds to thousands of image pairs on a laptop or desktop computer. We present four example applications of the GIV toolkit in which we complement a glaciology field campaign (Glaciar Perito Moreno, Argentina) and calculate the velocity fields of small mid-latitude (Glacier d'Argentière, France) and tropical glaciers (Volcán Chimborazo, Ecuador), as well as very large glaciers (Vavilov Ice Cap, Russia). Fully commented MATLAB code and a stand-alone app for GIV are available from GitHub and Zenodo (see https://doi.org/10.5281/zenodo.4624831, Van Wyk de Vries, 2021a).

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Trace projection transformation: a new method for measurement of debris flow surface velocity fields

Frontiers of Earth Science ◽

10.1007/s11707-015-0576-6 ◽

2016 ◽

Vol 10 (4) ◽

pp. 761-771 ◽

Cited By ~ 5

Author(s):

Yan Yan ◽

Peng Cui ◽

Xiaojun Guo ◽

Yonggang Ge

Keyword(s):

Debris Flow ◽

Velocity Fields ◽

New Method ◽

Surface Velocity ◽

Flow Surface ◽

Projection Transformation

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Reconstruction of the dynamics and origin of rock glaciers in an Alpine environment

10.5194/egusphere-egu21-2627 ◽

2021 ◽

Author(s):

Benjamin Lehmann ◽

Robert S. Anderson ◽

Xavier Bodin ◽

Pierre G. Valla ◽

Julien Carcaillet

Keyword(s):

Remote Sensing ◽

Time Scales ◽

Surface Velocity ◽

Rock Glacier ◽

Alpine Environment ◽

Mountain Ranges ◽

Long Time ◽

Rock Glaciers ◽

Alpine Environments ◽

Alpine Valleys

Rock glaciers are one of the most frequent cryospheric landform in mid-latitude mountain ranges. They influence the evolution of alpine environments on short (years to decades) and long (centuries to millennia) time scales. As a visible expression of mountain permafrost [1] as well as an important water reserve in the form of ground ice [2], rock glaciers are seen as increasingly important in the evolution of geomorphology and hydrology of mountain systems in the context of climate change and deglaciation [3, 4]. On longer time scales, rock glaciers transport boulders produced by the erosion of the headwall upstream and downstream and therefore participate in shaping mountain slopes [5]. Despite their importance, the dynamics and origin of rock glaciers are poorly understood.In this study, we propose to address two questions:1) How does the dynamics of rock glaciers change over time?2) What is the origin of rock glaciers and what is their influence on the evolution of alpine environments?These two questions require an evaluation of the surface velocity field of rock glaciers by relating short and long time scales. To solve this problem, we combine complementary methods including remote sensing, geochronology with a mechanical model of rock glacier dynamics. We apply this approach to the rock glacier complex of the Vallon de la Route in the Massif du Combeynot (French alps).In order to reconstruct the displacement field of the rock glacier on modern time scales, we used remote sensing methods (i.e., image correlation and InSAR). Over longer periods (103 to 104 years), we used cosmogenic terrestrial nuclides (TCN) dating. By applying this methodology to boulder surfaces at different positions along the central flow line of the rock glacier, from the headwall to its terminus, we will be able to convert the exposure ages into surface displacement. The use of dynamic modelling of rock glaciers [6] will allow us to relate the surface kinematics to short to long time scales. It will then be possible to discuss the age, origin of rock glaciers and how topo-climatic and geomorphological processes control their evolution in Alpine environment.&#160;[1] Barsch, D.: Rockglaciers. Indicators for the Present and Former Geoecology in High Mountain Environments, Springer series in physical environment vol. 16, Springer, Berlin, Heidelberg, 1996.[2] Jones, D. B., Harrison, S., Anderson, K., and Whalley, W. B.: Rock glaciers and mountain hydrology: A review, Earth-Sci Rev, 193, 66&#8211;90, 2019.[3] Haeberli, W., Schaub, Y., and Huggel, C.: Increasing risks related to landslides from degrading permafrost into new lakes in deglaciating mountain ranges, Geomorphology, 293, 405&#8211;417, 2017.[4] Knight, J., Harrison, S., and Jones, D. B.: Rock glaciers and the geomorphological evolution of deglacierizing mountains, Geomorphology, 324, 14&#8211;24, 2019.[5] MacGregor, K.R., Anderson, R.S., Waddington, E.D.: Numerical modeling of glacial erosion and headwall processes in alpine valleys. Geomorphology 103 (2):189&#8211;204, 2009.[6] Anderson, R. S., Anderson, L. S., Armstrong, W. H., Rossi, M. W., & Crump, S. E.: Glaciation of alpine valleys: The glacier&#8211;debris-covered glacier&#8211;rock glacier continuum. Geomorphology, 311, 127-142, 2018.

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A Combined Approach for Filtering Ice Surface Velocity Fields Derived from Remote Sensing Methods

Remote Sensing ◽

10.3390/rs9101062 ◽

2017 ◽

Vol 9 (10) ◽

pp. 1062 ◽

Cited By ~ 9

Author(s):

Christine Lüttig ◽

Niklas Neckel ◽

Angelika Humbert

Keyword(s):

Remote Sensing ◽

Velocity Fields ◽

Surface Velocity ◽

Combined Approach ◽

Ice Surface ◽

Remote Sensing Methods

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On the characterisation of open-flow seeding conditions for image-velocimetry techniques using UASs

10.5194/egusphere-egu2020-16011 ◽

2020 ◽

Author(s):

Silvano Fortunato Dal Sasso ◽

Alonso Pizarro ◽

Salvatore Manfreda

Keyword(s):

River Discharge ◽

Velocity Fields ◽

Velocity Estimation ◽

Surface Velocity ◽

Seeding Density ◽

Acquisition Time ◽

Flow Conditions ◽

Open Flow ◽

Image Velocimetry

In the last years, new technologies have been developed to monitor rivers in a real-time framework opening new opportunities and challenges for the research community and practitioners. Acquiring data in open flow conditions can be performed through the use of Unmanned Aerial System (UAS) to derive surface velocity fields and in consequence, river discharge. Significant work has been done to investigate the reliability of image-velocimetry techniques using numerical simulations and laboratory flume experiments, but, to date, the effects of environmental factors on velocity estimates are not addressed adequately. In this context, a critical variable is represented by the number of particles transiting on the water surface (defined as seeding density) during field surveys and their challenging dynamics along the cross-section, on both time and space. Seeding density has a significant effect on surface velocity estimation and river discharge accuracy. The goal of this study was, therefore, to evaluate the accuracy and feasibility of LSPIV and PTV techniques under different seeding and flow conditions using several footages acquired employing UASs. To this purpose, the seeding behaviour during the whole acquisition time was examined for each case study focusing on the quantification of essential variables such as seeding density, average tracers&#8217; dimension, coefficient of variation of tracers&#8217; area, and spatial dispersion of them in the field of view. For each case study, both image-velocimetry techniques have been applied considering several different sets of images to locally measure the accuracy of velocity estimations in challenging seeding conditions. Results show that the local seeding density, tracers&#8217; dimension and their spatial distribution can strongly influence the reconstruction of velocity fields in natural stream reaches. Therefore, prior knowledge of seeding characteristics in the field can deal with the choice of the optimal image-velocimetry technique to use and the related setting parameters.

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