Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-4063-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-4063-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2023
Research article |
| 19 Sep 2023
| 19 Sep 2023
GLAcier Feature Tracking testkit (GLAFT): a statistically and physically based framework for evaluating glacier velocity products derived from optical satellite image feature tracking
Department of Statistics, University of California Berkeley, Berkeley, CA 94720, USA
Center for Space and Remote Sensing Research, National Central University, Zhongli, Taoyuan 320317, Taiwan
Shashank Bhushan
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
Maximillian Van Wyk De Vries
Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55414, USA
School of Environmental Sciences, University of Liverpool, Liverpool, L69 7ZT, UK
School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK
William Kochtitzky
Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa K1N 6N5, Canada
School of Marine and Environmental Programs, University of New England, Biddeford, ME 04005, USA
David Shean
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
Luke Copland
Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa K1N 6N5, Canada
Christine Dow
Department of Geography and Environmental Management, University of Waterloo, Waterloo N2L 3G1, Canada
Renette Jones-Ivey
Institute for Artificial Intelligence and Data Science, University at Buffalo, Buffalo, NY 14260, USA
Fernando Pérez
Department of Statistics, University of California Berkeley, Berkeley, CA 94720, USA
Related authors
Jakob Steiner, William Armstrong, Will Kochtitzky, Robert McNabb, Rodrigo Aguayo, Tobias Bolch, Fabien Maussion, Vibhor Agarwal, Iestyn Barr, Nathaniel R. Baurley, Mike Cloutier, Katelyn DeWater, Frank Donachie, Yoann Drocourt, Siddhi Garg, Gunjan Joshi, Byron Guzman, Stanislav Kutuzov, Thomas Loriaux, Caleb Mathias, Biran Menounos, Evan Miles, Aleksandra Osika, Kaleigh Potter, Adina Racoviteanu, Brianna Rick, Miles Sterner, Guy D. Tallentire, Levan Tielidze, Rebecca White, Kunpeng Wu, and Whyjay Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-315, https://doi.org/10.5194/essd-2025-315, 2025
Preprint under review for ESSD
Short summary
Short summary
Many mountain glaciers around the world flow into lakes – exactly how many however, has never been mapped. Across a large team of experts we have now identified all glaciers that end in lakes. Only about 1% do so, but they are generally larger than those which end on land. This is important to understand, as lakes can influence the behaviour of glacier ice, including how fast it disappears. This new dataset allows us to better model glaciers at a global scale, accounting for the effect of lakes.
Whyjay Zheng, Wesley Van Wychen, Tian Li, and Tsutomu Yamanokuchi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2707, https://doi.org/10.5194/egusphere-2025-2707, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We identify lakes beneath the glaciers in the Canadian Arctic using satellite measurements over a decade, increasing the number of known subglacial lakes in this area from 2 to 37. These lakes are recharged by billions of cubic meters of water, and the draining of these lakes can lower the ice elevation by more than 100 meters. We find three types of subglacial lakes, two of which are primarily located in the Canadian Arctic. When glaciers lose their ice quickly, these lakes become active.
Whyjay Zheng
The Cryosphere, 16, 1431–1445, https://doi.org/10.5194/tc-16-1431-2022, https://doi.org/10.5194/tc-16-1431-2022, 2022
Short summary
Short summary
A glacier can speed up when surface water reaches the glacier's bottom via crevasses and reduces sliding friction. This paper builds up a physical model and finds that thick and fast-flowing glaciers are sensitive to this friction disruption. The data from Greenland and Austfonna (Svalbard) glaciers over 20 years support the model prediction. To estimate the projected sea-level rise better, these sensitive glaciers should be frequently monitored for potential future instabilities.
Jakob Steiner, William Armstrong, Will Kochtitzky, Robert McNabb, Rodrigo Aguayo, Tobias Bolch, Fabien Maussion, Vibhor Agarwal, Iestyn Barr, Nathaniel R. Baurley, Mike Cloutier, Katelyn DeWater, Frank Donachie, Yoann Drocourt, Siddhi Garg, Gunjan Joshi, Byron Guzman, Stanislav Kutuzov, Thomas Loriaux, Caleb Mathias, Biran Menounos, Evan Miles, Aleksandra Osika, Kaleigh Potter, Adina Racoviteanu, Brianna Rick, Miles Sterner, Guy D. Tallentire, Levan Tielidze, Rebecca White, Kunpeng Wu, and Whyjay Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-315, https://doi.org/10.5194/essd-2025-315, 2025
Preprint under review for ESSD
Short summary
Short summary
Many mountain glaciers around the world flow into lakes – exactly how many however, has never been mapped. Across a large team of experts we have now identified all glaciers that end in lakes. Only about 1% do so, but they are generally larger than those which end on land. This is important to understand, as lakes can influence the behaviour of glacier ice, including how fast it disappears. This new dataset allows us to better model glaciers at a global scale, accounting for the effect of lakes.
Whyjay Zheng, Wesley Van Wychen, Tian Li, and Tsutomu Yamanokuchi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2707, https://doi.org/10.5194/egusphere-2025-2707, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We identify lakes beneath the glaciers in the Canadian Arctic using satellite measurements over a decade, increasing the number of known subglacial lakes in this area from 2 to 37. These lakes are recharged by billions of cubic meters of water, and the draining of these lakes can lower the ice elevation by more than 100 meters. We find three types of subglacial lakes, two of which are primarily located in the Canadian Arctic. When glaciers lose their ice quickly, these lakes become active.
Shfaqat A. Khan, Helene Seroussi, Mathieu Morlighem, William Colgan, Veit Helm, Gong Cheng, Danjal Berg, Valentina R. Barletta, Nicolaj K. Larsen, William Kochtitzky, Michiel van den Broeke, Kurt H. Kjær, Andy Aschwanden, Brice Noël, Jason E. Box, Joseph A. MacGregor, Robert S. Fausto, Kenneth D. Mankoff, Ian M. Howat, Kuba Oniszk, Dominik Fahrner, Anja Løkkegaard, Eigil Y. H. Lippert, Alicia Bråtner, and Javed Hassan
Earth Syst. Sci. Data, 17, 3047–3071, https://doi.org/10.5194/essd-17-3047-2025, https://doi.org/10.5194/essd-17-3047-2025, 2025
Short summary
Short summary
The surface elevation of the Greenland Ice Sheet is changing due to surface mass balance processes and ice dynamics, each exhibiting distinct spatiotemporal patterns. Here, we employ satellite and airborne altimetry data with fine spatial (1 km) and temporal (monthly) resolutions to document this spatiotemporal evolution from 2003 to 2023. This dataset of fine-resolution altimetry data in both space and time will support studies of ice mass loss and be useful for GIS ice sheet modeling.
Lorenzo Nava, Maximilian Van Wyk de Vries, and Louie Elliot Bell
EGUsphere, https://doi.org/10.5194/egusphere-2025-2795, https://doi.org/10.5194/egusphere-2025-2795, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We introduce TerraTrack, an open-source tool for detecting and monitoring slow-moving landslides using Sentinel-2 data. It automates image acquisition, landslide identification, and time-series generation in an accessible and cloud-based workflow. TerraTrack supports early warning, complements InSAR, and offers a scalable solution for landslide hazard identification and monitoring.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci., 25, 1937–1942, https://doi.org/10.5194/nhess-25-1937-2025, https://doi.org/10.5194/nhess-25-1937-2025, 2025
Short summary
Short summary
Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides, such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management of landslide risk.
Dorota Medrzycka, Luke Copland, Laura Thomson, William Kochtitzky, and Braden Smeda
Geosci. Instrum. Method. Data Syst., 14, 69–90, https://doi.org/10.5194/gi-14-69-2025, https://doi.org/10.5194/gi-14-69-2025, 2025
Short summary
Short summary
This work explores the use of aerial photography surveys for mapping glaciers, specifically in challenging environments. Using examples from two glaciers in Arctic Canada, we discuss the main factors which can affect data collection and review methods for capturing and processing images to create accurate topographic maps. Key recommendations include choosing the right camera and positioning equipment and adapting survey design to maximise data quality, even under less-than-ideal conditions.
Joseph P. Tulenko, Sophie A. Goliber, Renette Jones-Ivey, Justin Quinn, Abani Patra, Kristin Poinar, Sophie Nowicki, Beata M. Csatho, and Jason P. Briner
EGUsphere, https://doi.org/10.5194/egusphere-2025-894, https://doi.org/10.5194/egusphere-2025-894, 2025
Short summary
Short summary
Ghub is an online platform that hosts tools, datasets and educational resources related to ice sheet science. These resources are provided by ice sheet researchers and allow other researchers, students, educators, and interested members of the general public to analyze, visualize and download datasets that researchers use to study past and present ice sheet behavior. We describe how users can interact with Ghub, showcase some available resources, and describe the future of the Ghub Project.
Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3992, https://doi.org/10.5194/egusphere-2024-3992, 2025
Short summary
Short summary
Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
Alexandre Dunant, Tom R. Robinson, Alexander L. Densmore, Nick J. Rosser, Ragindra Man Rajbhandari, Mark Kincey, Sihan Li, Prem Raj Awasthi, Max Van Wyk de Vries, Ramesh Guragain, Erin Harvey, and Simon Dadson
Nat. Hazards Earth Syst. Sci., 25, 267–285, https://doi.org/10.5194/nhess-25-267-2025, https://doi.org/10.5194/nhess-25-267-2025, 2025
Short summary
Short summary
Natural hazards like earthquakes often trigger other disasters, such as landslides, creating complex chains of impacts. We developed a risk model using a mathematical approach called hypergraphs to efficiently measure the impact of interconnected hazards. We showed that it can predict broad patterns of damage to buildings and roads from the 2015 Nepal earthquake. The model's efficiency allows it to generate multiple disaster scenarios, even at a national scale, to support preparedness plans.
Laurane Charrier, Amaury Dehecq, Lei Guo, Fanny Brun, Romain Millan, Nathan Lioret, Luke Copland, Nathan Maier, Christine Dow, and Paul Halas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3409, https://doi.org/10.5194/egusphere-2024-3409, 2025
Short summary
Short summary
While global annual glacier velocities are openly accessible, sub-annual velocity time series are still lacking. This hinders our ability to understand flow processes and the integration of these observations in numerical models. We introduce an open source Python package called TICOI to fuses multi-temporal and multi-sensor image-pair velocities produced by different processing chains to produce standardized sub-annual velocity products.
Ingalise Kindstedt, Dominic Winski, C. Max Stevens, Emma Skelton, Luke Copland, Karl Kreutz, Mikaila Mannello, Renée Clavette, Jacob Holmes, Mary Albert, and Scott N. Williamson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3807, https://doi.org/10.5194/egusphere-2024-3807, 2025
Short summary
Short summary
Atmospheric warming over mountain glaciers is leading to increased warming and melting of snow as it compresses into glacier ice. This affects both regional hydrology and climate records contained in the ice. Here we use field observations and modeling to show that surface melting and percolation at Eclipse Icefield (Yukon, Canada) is increasing with an increase in extreme melt events, and that compressing snow at Eclipse is likely to continue warming even if air temperatures remain stable.
George Brencher, Scott Henderson, and David Shean
EGUsphere, https://doi.org/10.5194/egusphere-2024-3196, https://doi.org/10.5194/egusphere-2024-3196, 2024
Short summary
Short summary
Glacial lakes are often dammed by moraines, which can fail, causing floods. Traditional methods of measuring moraine dam structure are not feasible for thousands of lakes. We instead developed a method to measure moraine dam movement with satellite radar data and applied this approach to the Imja Lake moraine dam in Nepal. We found that the moraine dam moved ~90 cm from 2017–2024, providing information about its internal structure. These data can help guide limited hazard remediation resources.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Adam J. Hepburn, Christine F. Dow, Antti Ojala, Joni Mäkinen, Elina Ahokangas, Jussi Hovikoski, Jukka-Pekka Palmu, and Kari Kajuutti
The Cryosphere, 18, 4873–4916, https://doi.org/10.5194/tc-18-4873-2024, https://doi.org/10.5194/tc-18-4873-2024, 2024
Short summary
Short summary
Terrain formerly occupied by ice sheets in the last ice age allows us to parameterize models of basal water flow using terrain and data unavailable beneath current ice sheets. Using GlaDS, a 2D basal hydrology model, we explore the origin of murtoos, a specific landform found throughout Finland that is thought to mark the upper limit of channels beneath the ice. Our results validate many of the predictions of murtoo origins and demonstrate that such models can be used to explore past ice sheets.
Robert G. Bingham, Julien A. Bodart, Marie G. P. Cavitte, Ailsa Chung, Rebecca J. Sanderson, Johannes C. R. Sutter, Olaf Eisen, Nanna B. Karlsson, Joseph A. MacGregor, Neil Ross, Duncan A. Young, David W. Ashmore, Andreas Born, Winnie Chu, Xiangbin Cui, Reinhard Drews, Steven Franke, Vikram Goel, John W. Goodge, A. Clara J. Henry, Antoine Hermant, Benjamin H. Hills, Nicholas Holschuh, Michelle R. Koutnik, Gwendolyn J.-M. C. Leysinger Vieli, Emma J. Mackie, Elisa Mantelli, Carlos Martín, Felix S. L. Ng, Falk M. Oraschewski, Felipe Napoleoni, Frédéric Parrenin, Sergey V. Popov, Therese Rieckh, Rebecca Schlegel, Dustin M. Schroeder, Martin J. Siegert, Xueyuan Tang, Thomas O. Teisberg, Kate Winter, Shuai Yan, Harry Davis, Christine F. Dow, Tyler J. Fudge, Tom A. Jordan, Bernd Kulessa, Kenichi Matsuoka, Clara J. Nyqvist, Maryam Rahnemoonfar, Matthew R. Siegfried, Shivangini Singh, Verjan Višnjević, Rodrigo Zamora, and Alexandra Zuhr
EGUsphere, https://doi.org/10.5194/egusphere-2024-2593, https://doi.org/10.5194/egusphere-2024-2593, 2024
Short summary
Short summary
The ice sheets covering Antarctica have built up over millenia through successive snowfall events which become buried and preserved as internal surfaces of equal age detectable with ice-penetrating radar. This paper describes an international initiative to work together on this archival data to build a comprehensive 3-D picture of how old the ice is everywhere across Antarctica, and how this will be used to reconstruct past and predict future ice and climate behaviour.
Matias Romero, Shanti B. Penprase, Maximillian S. Van Wyk de Vries, Andrew D. Wickert, Andrew G. Jones, Shaun A. Marcott, Jorge A. Strelin, Mateo A. Martini, Tammy M. Rittenour, Guido Brignone, Mark D. Shapley, Emi Ito, Kelly R. MacGregor, and Marc W. Caffee
Clim. Past, 20, 1861–1883, https://doi.org/10.5194/cp-20-1861-2024, https://doi.org/10.5194/cp-20-1861-2024, 2024
Short summary
Short summary
Investigating past glaciated regions is crucial for understanding how ice sheets responded to climate forcings and how they might respond in the future. We use two independent dating techniques to document the timing and extent of the Lago Argentino glacier lobe, a former lobe of the Patagonian Ice Sheet, during the late Quaternary. Our findings highlight feedbacks in the Earth’s system responsible for modulating glacier growth in the Southern Hemisphere prior to the global Last Glacial Maximum.
Siobhan F. Killingbeck, Anja Rutishauser, Martyn J. Unsworth, Ashley Dubnick, Alison S. Criscitiello, James Killingbeck, Christine F. Dow, Tim Hill, Adam D. Booth, Brittany Main, and Eric Brossier
The Cryosphere, 18, 3699–3722, https://doi.org/10.5194/tc-18-3699-2024, https://doi.org/10.5194/tc-18-3699-2024, 2024
Short summary
Short summary
A subglacial lake was proposed to exist beneath Devon Ice Cap in the Canadian Arctic based on the analysis of airborne data. Our study presents a new interpretation of the subglacial material beneath the Devon Ice Cap from surface-based geophysical data. We show that there is no evidence of subglacial water, and the subglacial lake has likely been misidentified. Re-evaluation of the airborne data shows that overestimation of a critical processing parameter has likely occurred in prior studies.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
Short summary
Short summary
Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Maximillian Van Wyk de Vries, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Alexander L. Densmore, Tek Bahadur Dong, Alexandre Dunant, Erin L. Harvey, Ganesh K. Jimee, Mark E. Kincey, Katie Oven, Sarmila Paudyal, Dammar Singh Pujara, Anuradha Puri, Ram Shrestha, Nick J. Rosser, and Simon J. Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2024-397, https://doi.org/10.5194/egusphere-2024-397, 2024
Preprint archived
Short summary
Short summary
This study focuses on understanding soil moisture, a key factor for evaluating hillslope stability and landsliding. In Nepal, where landslides are common, we used a computer model to better understand how rapidly soil dries out after the monsoon season. We calibrated the model using field data and found that, by adjusting soil properties, we could predict moisture levels more accurately. This helps understand where landslides might occur, even where direct measurements are not possible.
Christine F. Dow, Derek Mueller, Peter Wray, Drew Friedrichs, Alexander L. Forrest, Jasmin B. McInerney, Jamin Greenbaum, Donald D. Blankenship, Choon Ki Lee, and Won Sang Lee
The Cryosphere, 18, 1105–1123, https://doi.org/10.5194/tc-18-1105-2024, https://doi.org/10.5194/tc-18-1105-2024, 2024
Short summary
Short summary
Ice shelves are a key control on Antarctic contribution to sea level rise. We examine the Nansen Ice Shelf in East Antarctica using a combination of field-based and satellite data. We find the basal topography of the ice shelf is highly variable, only partially visible in satellite datasets. We also find that the thinnest region of the ice shelf is altered over time by ice flow rates and ocean melting. These processes can cause fractures to form that eventually result in large calving events.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
Short summary
Short summary
We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Koi McArthur, Felicity S. McCormack, and Christine F. Dow
The Cryosphere, 17, 4705–4727, https://doi.org/10.5194/tc-17-4705-2023, https://doi.org/10.5194/tc-17-4705-2023, 2023
Short summary
Short summary
Using subglacial hydrology model outputs for Denman Glacier, East Antarctica, we investigated the effects of various friction laws and effective pressure inputs on ice dynamics modeling over the same glacier. The Schoof friction law outperformed the Budd friction law, and effective pressure outputs from the hydrology model outperformed a typically prescribed effective pressure. We propose an empirical prescription of effective pressure to be used in the absence of hydrology model outputs.
Felicity S. McCormack, Jason L. Roberts, Bernd Kulessa, Alan Aitken, Christine F. Dow, Lawrence Bird, Benjamin K. Galton-Fenzi, Katharina Hochmuth, Richard S. Jones, Andrew N. Mackintosh, and Koi McArthur
The Cryosphere, 17, 4549–4569, https://doi.org/10.5194/tc-17-4549-2023, https://doi.org/10.5194/tc-17-4549-2023, 2023
Short summary
Short summary
Changes in Antarctic surface elevation can cause changes in ice and basal water flow, impacting how much ice enters the ocean. We find that ice and basal water flow could divert from the Totten to the Vanderford Glacier, East Antarctica, under only small changes in the surface elevation, with implications for estimates of ice loss from this region. Further studies are needed to determine when this could occur and if similar diversions could occur elsewhere in Antarctica due to climate change.
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
Short summary
Short summary
The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Tim Hill and Christine F. Dow
The Cryosphere, 17, 2607–2624, https://doi.org/10.5194/tc-17-2607-2023, https://doi.org/10.5194/tc-17-2607-2023, 2023
Short summary
Short summary
Water flow across the surface of the Greenland Ice Sheet controls the rate of water flow to the glacier bed. Here, we simulate surface water flow for a small catchment on the southwestern Greenland Ice Sheet. Our simulations predict significant differences in the form of surface water flow in high and low melt years depending on the rate and intensity of surface melt. These model outputs will be important in future work assessing the impact of surface water flow on subglacial water pressure.
Maximillian Van Wyk de Vries, Shashank Bhushan, Mylène Jacquemart, César Deschamps-Berger, Etienne Berthier, Simon Gascoin, David E. Shean, Dan H. Shugar, and Andreas Kääb
Nat. Hazards Earth Syst. Sci., 22, 3309–3327, https://doi.org/10.5194/nhess-22-3309-2022, https://doi.org/10.5194/nhess-22-3309-2022, 2022
Short summary
Short summary
On 7 February 2021, a large rock–ice avalanche occurred in Chamoli, Indian Himalaya. The resulting debris flow swept down the nearby valley, leaving over 200 people dead or missing. We use a range of satellite datasets to investigate how the collapse area changed prior to collapse. We show that signs of instability were visible as early 5 years prior to collapse. However, it would likely not have been possible to predict the timing of the event from current satellite datasets.
Ingalise Kindstedt, Kristin M. Schild, Dominic Winski, Karl Kreutz, Luke Copland, Seth Campbell, and Erin McConnell
The Cryosphere, 16, 3051–3070, https://doi.org/10.5194/tc-16-3051-2022, https://doi.org/10.5194/tc-16-3051-2022, 2022
Short summary
Short summary
We show that neither the large spatial footprint of the MODIS sensor nor poorly constrained snow emissivity values explain the observed cold offset in MODIS land surface temperatures (LSTs) in the St. Elias. Instead, the offset is most prominent under conditions associated with near-surface temperature inversions. This work represents an advance in the application of MODIS LSTs to glaciated alpine regions, where we often depend solely on remote sensing products for temperature information.
Whyjay Zheng
The Cryosphere, 16, 1431–1445, https://doi.org/10.5194/tc-16-1431-2022, https://doi.org/10.5194/tc-16-1431-2022, 2022
Short summary
Short summary
A glacier can speed up when surface water reaches the glacier's bottom via crevasses and reduces sliding friction. This paper builds up a physical model and finds that thick and fast-flowing glaciers are sensitive to this friction disruption. The data from Greenland and Austfonna (Svalbard) glaciers over 20 years support the model prediction. To estimate the projected sea-level rise better, these sensitive glaciers should be frequently monitored for potential future instabilities.
Maximillian Van Wyk de Vries, Emi Ito, Mark Shapley, Matias Romero, and Guido Brignone
Clim. Past Discuss., https://doi.org/10.5194/cp-2022-29, https://doi.org/10.5194/cp-2022-29, 2022
Manuscript not accepted for further review
Short summary
Short summary
In some situations, the color of sediment records information about the climatic conditions under which it was deposited. We show that sediment color and climate are linked at Lago Argentino, the world's largest ice-contact lake, but that this relationship is too complex to be used for reconstructing past climate. We instead use this sediment color-climate relationship to show that temperature and wind speed affect sediment deposition in the summer, but not in the winter.
Joachim Meyer, McKenzie Skiles, Jeffrey Deems, Kat Boremann, and David Shean
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-281, https://doi.org/10.5194/hess-2021-281, 2021
Revised manuscript not accepted
Short summary
Short summary
Seasonally accumulated snow in the mountains forms a natural water reservoir which is challenging to measure in the rugged and remote terrain. Here, we use overlapping aerial images that model surface elevations using software to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the utility of aerial images to improve our ability to capture the amount of water held as snow in remote and inaccessible locations.
Maximillian Van Wyk de Vries and Andrew D. Wickert
The Cryosphere, 15, 2115–2132, https://doi.org/10.5194/tc-15-2115-2021, https://doi.org/10.5194/tc-15-2115-2021, 2021
Short summary
Short summary
We can measure glacier flow and sliding velocity by tracking patterns on the ice surface in satellite images. The surface velocity of glaciers provides important information to support assessments of glacier response to climate change, to improve regional assessments of ice thickness, and to assist with glacier fieldwork. Our paper describes Glacier Image Velocimetry (GIV), a new, easy-to-use, and open-source toolbox for calculating high-resolution velocity time series for any glacier on earth.
Naomi E. Ochwat, Shawn J. Marshall, Brian J. Moorman, Alison S. Criscitiello, and Luke Copland
The Cryosphere, 15, 2021–2040, https://doi.org/10.5194/tc-15-2021-2021, https://doi.org/10.5194/tc-15-2021-2021, 2021
Short summary
Short summary
In May 2018 we drilled into Kaskawulsh Glacier to study how it is being affected by climate warming and used models to investigate the evolution of the firn since the 1960s. We found that the accumulation zone has experienced increased melting that has refrozen as ice layers and has formed a perennial firn aquifer. These results better inform climate-induced changes on northern glaciers and variables to take into account when estimating glacier mass change using remote-sensing methods.
Joachim Meyer, S. McKenzie Skiles, Jeffrey Deems, Kat Bormann, and David Shean
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-34, https://doi.org/10.5194/tc-2021-34, 2021
Manuscript not accepted for further review
Short summary
Short summary
Snow that accumulates seasonally in mountains forms a natural water reservoir and is difficult to measure in the rugged and remote landscapes. Here, we use modern software that models surface elevations from overlapping aerial images to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the potential value of aerial images for understanding the amount of water held as snow in remote and inaccessible locations.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
Short summary
Short summary
We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Cited articles
Abe, T. and Furuya, M.: Winter speed-up of quiescent surge-type glaciers in Yukon, Canada, The Cryosphere, 9, 1183–1190, https://doi.org/10.5194/tc-9-1183-2015, 2015. a
Ahn, Y. and Howat, I. M.: Efficient Automated Glacier Surface Velocity
Measurement From Repeat Images Using Multi-Image/Multichip and Null Exclusion
Feature Tracking, IEEE T. Geosci. Remote, 49,
2838–2846, https://doi.org/10.1109/TGRS.2011.2114891, 2011. a
Altena, B. and Kääb, A.: Ensemble matching of repeat satellite
images applied to measure fast-changing ice flow, verified with mountain
climber trajectories on Khumbu icefall, Mount Everest, J.
Glaciol., 66, 905–915, https://doi.org/10.1017/jog.2020.66, 2020. a, b
Altena, B., Scambos, T., Fahnestock, M., and Kääb, A.: Extracting recent short-term glacier velocity evolution over southern Alaska and the Yukon from a large collection of Landsat data, The Cryosphere, 13, 795–814, https://doi.org/10.5194/tc-13-795-2019, 2019. a, b
Altena, B., Kääb, A., and Wouters, B.: Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements, The Cryosphere, 16, 2285–2300, https://doi.org/10.5194/tc-16-2285-2022, 2022. a
Argyriou, V. and Vlachos, T.: Performance study of gradient correlation for
sub-pixel motion estimation in the frequency domain, IEE Proc.-F, 152, 107,
https://doi.org/10.1049/ip-vis:20051073, 2005. a
Armstrong, W. H., Anderson, R. S., Allen, J., and Rajaram, H.: Modeling the
WorldView-derived seasonal velocity evolution of Kennicott Glacier, Alaska,
J. Glaciol., 62, 763–777, https://doi.org/10.1017/jog.2016.66, 2016. a
Baker, S. and Matthews, I.: Lucas-Kanade 20 Years On: A Unifying
Framework, Int. J. Comput. Vision, 56, 221–255,
https://doi.org/10.1023/B:VISI.0000011205.11775.fd, 2004. a
Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline:
NASA's Open Source Software for Deriving and Processing Terrain Data, Earth
Space Sci., 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018. a
Bindschadler, R. A. and Scambos, T. A.: Satellite-Image-Derived Velocity Field
of an Antarctic Ice Stream, Science, 252, 242–246,
https://doi.org/10.1126/science.252.5003.242, 1991. a
Boncori, J. P. M., Andersen, M. L., Dall, J., Kusk, A., Kamstra, M., Andersen,
S. B., Bechor, N., Bevan, S., Bignami, C., Gourmelen, N., Joughin, I., Jung,
H.-S., Luckman, A., Mouginot, J., Neelmeijer, J., Rignot, E., Scharrer, K.,
Nagler, T., Scheuchl, B., and Strozzi, T.: Intercomparison and Validation of
SAR-Based Ice Velocity Measurement Techniques within the Greenland Ice Sheet
CCI Project, Remote Sens.-Basel, 10, 929, https://doi.org/10.3390/rs10060929, 2018. a
Bradski, G.: The OpenCV Library, Dr. Dobb’s Journal of Software Tools, Vol. 25, p. 120-5, ISSN:1044-789X, 2000. a
Brencher, G., Handwerger, A. L., and Munroe, J. S.: InSAR-based characterization of rock glacier movement in the Uinta Mountains, Utah, USA, The Cryosphere, 15, 4823–4844, https://doi.org/10.5194/tc-15-4823-2021, 2021. a
Broxton, M. J., Nefian, A. V., Moratto, Z., Kim, T., Lundy, M., and Segal,
A. V.: 3D Lunar Terrain Reconstruction from Apollo Images, in: Advances in
Visual Computing, edited by: Bebis, G., Boyle, R., Parvin, B., Koracin, D.,
Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P.,
Hinkenjann, A., Encarnação, M. L., Silva, C. T., and Coming, D.,
Springer Berlin Heidelberg, Berlin, Heidelberg, 710–719, https://doi.org/10.1007/978-3-642-10331-5_66, 2009. a
Burgess, E. W., Larsen, C. F., and Forster, R. R.: Summer melt regulates
winter glacier flow speeds throughout Alaska, Geophys. Res. Lett.,
40, 6160–6164, https://doi.org/10.1002/2013GL058228, 2013. a, b, c
Clarke, G. K.: A Short and Somewhat Personal History of Yukon Glacier Studies
in the Twentieth Century, Arctic, 67, 1, https://doi.org/10.14430/arctic4355, 2014. a
Clarke, G. K. C., Schmok, J. P., Ommanney, C. S. L., and Collins, S. G.:
Characteristics of surge-type glaciers, J. Geophys. Res.-Sol. Ea., 91, 7165–7180, https://doi.org/10.1029/JB091iB07p07165, 1986. a
Dehecq, A., Gourmelen, N., and Trouve, E.: Deriving large-scale glacier
velocities from a complete satellite archive: Application to the
Pamir-Karakoram-Himalaya, Remote Sens. Environ., 162, 55–66,
https://doi.org/10.1016/j.rse.2015.01.031, 2015. a, b, c, d
Evans, S. G., Tutubalina, O. V., Drobyshev, V. N., Chernomorets, S. S.,
McDougall, S., Petrakov, D. A., and Hungr, O.: Catastrophic detachment and
high-velocity long-runout flow of Kolka Glacier, Caucasus Mountains, Russia
in 2002, Geomorphology, 105, 314–321, https://doi.org/10.1016/j.geomorph.2008.10.008,
2009. a
Executable Books Community: Jupyter Book (v0.10), Zenodo [code],
https://doi.org/10.5281/zenodo.4539666, 2020. a
Fahnestock, M., Scambos, T., Moon, T., Gardner, A., Haran, T., and Klinger, M.:
Rapid large-area mapping of ice flow using Landsat 8, Remote Sens.
Environ., 185, 84–94, https://doi.org/10.1016/j.rse.2015.11.023, 2016. a, b, c
Foy, N., Copland, L., Zdanowicz, C., Demuth, M., and Hopkinson, C.: Recent
volume and area changes of Kaskawulsh Glacier, Yukon, Canada, J.
Glaciol., 57, 515–525, https://doi.org/10.3189/002214311796905596, 2011. a
Friedl, P., Seehaus, T., and Braun, M.: Global time series and temporal mosaics of glacier surface velocities derived from Sentinel-1 data, Earth Syst. Sci. Data, 13, 4653–4675, https://doi.org/10.5194/essd-13-4653-2021, 2021. a, b
Henderson, D. J. and Parmeter, C. F.: Normal reference bandwidths for the
general order, multivariate kernel density derivative estimator, Stat.
Probabil. Letters, 82, 2198–2205, https://doi.org/10.1016/j.spl.2012.07.020,
2012. a
Holdsworth, G.: Primary Transverse Crevasses, J. Glaciol., 8,
107–129, https://doi.org/10.3189/S0022143000020797, 1969. a
Holland, D. M., Thomas, R. H., de Young, B., Ribergaard, M. H., and Lyberth,
B.: Acceleration of Jakobshavn Isbræ triggered by warm subsurface ocean
waters, Nat. Geosci., 1, 659–664, https://doi.org/10.1038/ngeo316, 2008. a
Kääb, A., Winsvold, S., Altena, B., Nuth, C., Nagler, T., and
Wuite, J.: Glacier Remote Sensing Using Sentinel-2. Part I: Radiometric and
Geometric Performance, and Application to Ice Velocity, Remote Sens.-Basel, 8,
598, https://doi.org/10.3390/rs8070598, 2016. a
Lei, Y., Gardner, A., and Agram, P.: Autonomous Repeat Image Feature Tracking
(autoRIFT) and Its Application for Tracking Ice Displacement, Remote
Sens.-Basel, 13, 749, https://doi.org/10.3390/rs13040749, 2021. a, b
Lei, Y., Gardner, A. S., and Agram, P.: Processing methodology for the ITS_LIVE Sentinel-1 ice velocity products, Earth Syst. Sci. Data, 14, 5111–5137, https://doi.org/10.5194/essd-14-5111-2022, 2022. a
Main, B., Copland, L., Smeda, B., Kochtitzky, W., Samsonov, S., Dudley, J.,
Skidmore, M., Dow, C., Van Wychen, W., Medrzycka, D., Higgs, E., and Mingo,
L.: Terminus change of Kaskawulsh Glacier, Yukon, under a warming climate:
retreat, thinning, slowdown and modified proglacial lake geometry,J. Glaciol., 69, 936–952, https://doi.org/10.1017/jog.2022.114, 2023. a
Millan, R., Mouginot, J., Rabatel, A., Jeong, S., Cusicanqui, D., Derkacheva,
A., and Chekki, M.: Mapping surface flow velocity of glaciers at regional
scale using a multiple sensors approach, Remote Sens.-Basel, 11, 1–21,
https://doi.org/10.3390/rs11212498, 2019. a, b
Millan, R., Mouginot, J., Rabatel, A., and Morlighem, M.: Ice velocity and
thickness of the world's glaciers, Nat. Geosci., 15, 124–129,
https://doi.org/10.1038/s41561-021-00885-z, 2022. a, b, c, d
Moon, T., Joughin, I., Smith, B., and Howat, I.: 21st-century evolution of
Greenland outlet glacier velocities, Science, 336, 576–578,
https://doi.org/10.1126/science.1219985, 2012. a, b
Mouginot, J., Rignot, E., Bjørk, A. A., van den Broeke, M., Millan, R.,
Morlighem, M., Noël, B., Scheuchl, B., and Wood, M.: Forty-six years
of Greenland Ice Sheet mass balance from 1972 to 2018, P.
Natl. Acad. Sci. USA, 116, 9239–9244, https://doi.org/10.1073/pnas.1904242116,
2019. a
Nefian, A. V., Husmann, K., Broxton, M., To, V., Lundy, M., and Hancher, M. D.:
A bayesian formulation for sub-pixel refinement in stereo orbital imagery,
in: 2009 16th IEEE International Conference on Image Processing (ICIP),
2361–2364, https://doi.org/10.1109/ICIP.2009.5413749, 2009. a
Paul, F., Bolch, T., Briggs, K., Kääb, A., McMillan, M., McNabb,
R., Nagler, T., Nuth, C., Rastner, P., Strozzi, T., and Wuite, J.: Error
sources and guidelines for quality assessment of glacier area, elevation
change, and velocity products derived from satellite data in the
Glaciers_cci project, Remote Sens. Environ., 203, 256–275,
https://doi.org/10.1016/j.rse.2017.08.038, 2017. a, b, c
Project Jupyter, Bussonnier, M., Forde, J., Freeman, J., Granger, B., Head,
T., Holdgraf, C., Kelley, K., Nalvarte, G., Osheroff, A., Pacer, M., Panda,
Y., Perez, F., Ragan-Kelley, B., and Willing, C.: Binder 2.0 – Reproducible,
Interactive, Sharable Environments for Science at Scale, in: The 17th Python
in Science Conference, https://doi.org/10.25080/Majora-4af1f417-011, 2018. a
Redpath, T., Sirguey, P., Fitzsimons, S., and Kääb, A.: Accuracy
assessment for mapping glacier flow velocity and detecting flow dynamics from
ASTER satellite imagery: Tasman Glacier, New Zealand, Remote Sens.
Environ., 133, 90–101, https://doi.org/10.1016/j.rse.2013.02.008, 2013. a
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier
Outlines, Version 6, National Snow and Ice Data Center [data set], https://doi.org/10.7265/4m1f-gd79, 2017. a
Rosen, P. A., Hensley, S., Peltzer, G., and Simons, M.: Updated repeat orbit
interferometry package released, EOS T. Am. Geophys.
Un., 85, p. 47, https://doi.org/10.1029/2004EO050004, 2004. a
Rosen, P. A., Gurrola, E. M., Franco Sacco, G., and Zebker, H. A.: The InSAR
Scientific Computing Environment, Proceedings of the 9th European Conference
on Synthetic Aperture Radar, 730–733, ISBN 978-3-8007-3404-7,
2012. a
Sciacchitano, A.: Uncertainty quantification in particle image velocimetry,
Measure. Sci. Technol., 30, 092001,
https://doi.org/10.1088/1361-6501/ab1db8, 2019. a, b, c, d
Shangguan, D., Li, D., Ding, Y., Liu, J., Anjum, M. N., Li, Y., and Guo, W.:
Determining the Events in a Glacial Disaster Chain at Badswat Glacier in the
Karakoram Range Using Remote Sensing, Remote Sens.-Basel, 13, 1165,
https://doi.org/10.3390/rs13061165, 2021. a
Shean, D. and Bhushan, S.: vmap: Velocity map generation using the NASA Ames
Stereo Pipeline (ASP) image correlator, Zenodo [code], https://doi.org/10.5281/zenodo.7730146, 2023. a
Shepherd, A., Ivins, E., Rignot, E., Smith, B., van den Broeke, M., Velicogna,
I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne,
T., Scambos, T., Schlegel, N., A, G., Agosta, C., Ahlstrøm, A., Babonis,
G., Barletta, V. R., Bjørk, A. A., Blazquez, A., Bonin, J., Colgan, W.,
Csatho, B., Cullather, R., Engdahl, M. E., Felikson, D., Fettweis, X.,
Forsberg, R., Hogg, A. E., Gallee, H., Gardner, A., Gilbert, L., Gourmelen,
N., Groh, A., Gunter, B., Hanna, E., Harig, C., Helm, V., Horvath, A.,
Horwath, M., Khan, S., Kjeldsen, K. K., Konrad, H., Langen, P. L.,
Lecavalier, B., Loomis, B., Luthcke, S., McMillan, M., Melini, D., Mernild,
S., Mohajerani, Y., Moore, P., Mottram, R., Mouginot, J., Moyano, G., Muir,
A., Nagler, T., Nield, G., Nilsson, J., Noël, B., Otosaka, I., Pattle,
M. E., Peltier, W. R., Pie, N., Rietbroek, R., Rott, H.,
Sandberg Sørensen, L., Sasgen, I., Save, H., Scheuchl, B., Schrama, E.,
Schröder, L., Seo, K. W., Simonsen, S. B., Slater, T., Spada, G.,
Sutterley, T., Talpe, M., Tarasov, L., van de Berg, W. J., van der Wal, W.,
van Wessem, M., Vishwakarma, B. D., Wiese, D., Wilton, D., Wagner, T.,
Wouters, B., and Wuite, J.: Mass balance of the Greenland Ice Sheet from
1992 to 2018, Nature, 579, 233–239, https://doi.org/10.1038/s41586-019-1855-2, 2020. a
Shimizu, M. and Okutomi, M.: Precise sub-pixel estimation on area-based
matching, in: Proceedings Eighth IEEE International Conference on Computer
Vision. ICCV 2001, IEEE Comput. Soc., 1, 90–97,
https://doi.org/10.1109/ICCV.2001.937503, 2001. a
Shugar, D. H., Clague, J. J., Best, J. L., Schoof, C., Willis, M. J., Copland,
L., and Roe, G. H.: River piracy and drainage basin reorganization led by
climate-driven glacier retreat, Nat. Geosci., 10, 370–375,
https://doi.org/10.1038/ngeo2932, 2017. a
Shugar, D. H., Jacquemart, M., Shean, D., Bhushan, S., Upadhyay, K., Sattar,
A., Schwanghart, W., McBride, S., de Vries, M. V. W., Mergili, M., Emmer, A.,
Deschamps-Berger, C., McDonnell, M., Bhambri, R., Allen, S., Berthier, E.,
Carrivick, J. L., Clague, J. J., Dokukin, M., Dunning, S. A., Frey, H.,
Gascoin, S., Haritashya, U. K., Huggel, C., Kääb, A., Kargel,
J. S., Kavanaugh, J. L., Lacroix, P., Petley, D., Rupper, S., Azam, M. F.,
Cook, S. J., Dimri, A. P., Eriksson, M., Farinotti, D., Fiddes, J., Gnyawali,
K. R., Harrison, S., Jha, M., Koppes, M., Kumar, A., Leinss, S., Majeed, U.,
Mal, S., Muhuri, A., Noetzli, J., Paul, F., Rashid, I., Sain, K., Steiner,
J., Ugalde, F., Watson, C. S., and Westoby, M. J.: A massive rock and ice
avalanche caused the 2021 disaster at Chamoli, Indian Himalaya, Science,
373, 300–306, https://doi.org/10.1126/science.abh4455, 2021. a
Sperhac, J. M., Poinar, K., Jones‐Ivey, R., Briner, J., Csatho, B., Nowicki,
S., Simon, E., Larour, E., Quinn, J., and Patra, A.: GHub: Building a
glaciology gateway to unify a community, Concurr. Comput.-Pract. E.,
33, 1–14, https://doi.org/10.1002/cpe.6130, 2021. a
Stein, A., Huertas, A., and Matthies, L.: Attenuating stereo pixel-locking via
affine window adaptation, in: Proceedings 2006 IEEE International Conference
on Robotics and Automation, ICRA 2006, May 2006, IEEE, 914–921,
https://doi.org/10.1109/ROBOT.2006.1641826, 2006. a
Strozzi, T., Luckman, A., Murray, T., Wegmüller, U., and Werner, C. L.:
Glacier motion estimation using SAR offset-tracking procedures, IEEE
T. Geosci. Remote, 40, 2384–2391,
https://doi.org/10.1109/TGRS.2002.805079, 2002. a
Strozzi, T., Paul, F., Wiesmann, A., Schellenberger, T., and Kääb,
A.: Circum-arctic changes in the flow of glaciers and ice caps from
satellite SAR data between the 1990s and 2017, Remote Sens.-Basel, 9, 947,
https://doi.org/10.3390/rs9090947, 2017. a, b
Sundal, A., Shepherd, A., van den Broeke, M., Van Angelen, J., Gourmelen, N.,
and Park, J.: Controls on short-term variations in Greenland glacier
dynamics, J. Glaciol., 59, 883–892, https://doi.org/10.3189/2013JoG13J019,
2013. a
Thomson, L. I. and Copland, L.: Multi-decadal reduction in glacier velocities
and mechanisms driving deceleration at polythermal White Glacier, Arctic
Canada, J. Glaciol., 63, 450–463, https://doi.org/10.1017/jog.2017.3, 2017. a
USGS: Earth Explorer, USGS [data set], https://earthexplorer.usgs.gov, last access: 21 August 2020. a
Van Wychen, W., Copland, L., Jiskoot, H., Gray, L., Sharp, M., and Burgess,
D.: Surface Velocities of Glaciers in Western Canada from Speckle-Tracking
of ALOS PALSAR and RADARSAT-2 data, Can. J. Remote Sens., 44,
57–66, https://doi.org/10.1080/07038992.2018.1433529, 2018. a, b
Van Wyk de Vries, M. and Wickert, A. D.: Glacier Image Velocimetry: an open-source toolbox for easy and rapid calculation of high-resolution glacier velocity fields, The Cryosphere, 15, 2115–2132, https://doi.org/10.5194/tc-15-2115-2021, 2021. a, b, c
Van Wyk de Vries, M., Bhushan, S., Jacquemart, M., Deschamps-Berger, C., Berthier, E., Gascoin, S., Shean, D. E., Shugar, D. H., and Kääb, A.: Pre-collapse motion of the February 2021 Chamoli rock–ice avalanche, Indian Himalaya, Nat. Hazards Earth Syst. Sci., 22, 3309–3327, https://doi.org/10.5194/nhess-22-3309-2022, 2022. a
Van Wyk de Vries, M., Carchipulla-Morales, D., Wickert, A. D., and Minaya,
V. G.: Glacier thickness and ice volume of the Northern Andes, Sci.
Data, 9, 342, https://doi.org/10.1038/s41597-022-01446-8, 2022. a
Van Wyk de Vries, M., Wickert, A. D., MacGregor, K. R., Rada, C., and Willis,
M. J.: Atypical landslide induces speedup, advance, and long-term slowdown
of a tidewater glacier, Geology, 50, 806–811, https://doi.org/10.1130/G49854.1, 2022. a, b
Waechter, A., Copland, L., and Herdes, E.: Modern glacier velocities across
the Icefield Ranges, St Elias Mountains, and variability at selected glaciers
from 1959 to 2012, J. Glaciol., 61, 624–634,
https://doi.org/10.3189/2015JoG14J147, 2015.
a, b, c
Willis, M. J., Melkonian, A. K., Pritchard, M. E., and Ramage, J. M.: Ice loss
rates at the Northern Patagonian Icefield derived using a decade of satellite
remote sensing, Remote Sens. Environ., 117, 184–198,
https://doi.org/10.1016/j.rse.2011.09.017, 2012. a, b
Zheng, W.: Glacier geometry and flow speed determine how Arctic marine-terminating glaciers respond to lubricated beds, The Cryosphere, 16, 1431–1445, https://doi.org/10.5194/tc-16-1431-2022, 2022. a
Zheng, W., Pritchard, M. E., Willis, M. J., and Stearns, L. A.: The possible
transition from glacial surge to ice stream on Vavilov Ice Cap, Geophys.
Res. Lett., 46, 13892–13902, https://doi.org/10.1029/2019GL084948, 2019. a, b
Zheng, W., Durkin, W. J., Melkonian, A. K., and Pritchard, M. E.: Cryosphere
And Remote Sensing Toolkit (CARST) v2.0.0a1, Zenodo [code], https://doi.org/10.5281/zenodo.4592619,
2021. a, b
Zheng, W., Bhushan, S., Van Wyk De Vries, M., Kochtitzky, W., Shean, D., Copland, L., Dow, C., Jones-Ivey, R., and Pérez, F.: GLAFT data repository, OSF [data set], https://doi.org/10.17605/OSF.IO/HE7YR, 2023b. a
Short summary
We design and propose a method that can evaluate the quality of glacier velocity maps. The method includes two numbers that we can calculate for each velocity map. Based on statistics and ice flow physics, velocity maps with numbers close to the recommended values are considered to have good quality. We test the method using the data from Kaskawulsh Glacier, Canada, and release an open-sourced software tool called GLAcier Feature Tracking testkit (GLAFT) to help users assess their velocity maps.
We design and propose a method that can evaluate the quality of glacier velocity maps. The...
