Articles | Volume 11, issue 2
https://doi.org/10.5194/gi-11-247-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-11-247-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
02 Aug 2022
Research article | Highlight paper |
| 02 Aug 2022
| 02 Aug 2022
MOLISENS: MObile LIdar SENsor System to exploit the potential of small industrial lidar devices for geoscientific applications
Thomas Goelles
CORRESPONDING AUTHOR
Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria
Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria
Tobias Hammer
CORRESPONDING AUTHOR
Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria
Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria
Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, 8010 Graz, Austria
Stefan Muckenhuber
CORRESPONDING AUTHOR
Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria
Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria
Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria
Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, 8010 Graz, Austria
Jakob Abermann
Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria
Christian Bauer
Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria
Víctor J. Expósito Jiménez
Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria
Wolfgang Schöner
Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria
Markus Schratter
Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria
Benjamin Schrei
Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria
Kim Senger
Arctic Geology Department, University Centre in Svalbard, P.O. Box 156, 9171 Longyearbyen, Norway
Related authors
No articles found.
Matthew B. Switanek, Jakob Abermann, Wolfgang Schöner, and Michael L. Anderson
EGUsphere, https://doi.org/10.5194/egusphere-2025-3881, https://doi.org/10.5194/egusphere-2025-3881, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Extreme precipitation is expected to increase in a warming climate. Measurements of precipitation and dew point temperature are often used to estimate observed precipitation-temperature scaling rates. In this study, we use three different approaches which rely on either raw or normalized data to estimate scaling rates and produce predictions of extreme precipitation. Our findings highlight the importance of using normalized data to obtain accurate observation-based scaling estimates.
Jonathan Fipper, Jakob Abermann, Ingo Sasgen, Henrik Skov, Lise Lotte Sørensen, and Wolfgang Schöner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3381, https://doi.org/10.5194/egusphere-2025-3381, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We use measurements conducted with uncrewed aerial vehicles (UAVs) and reanalysis data to study the drivers of vertical air temperature structures and their link to the surface mass balance of Flade Isblink, a large ice cap in Northeast Greenland. Surface properties control temperature structures up to 100 m above ground, while large-scale circulation dominates above. Mass loss has increased since 2015, with record loss in 2023 associated with frequent synoptic conditions favoring melt.
Jakob Steiner, Jakob Abermann, and Rainer Prinz
EGUsphere, https://doi.org/10.5194/egusphere-2025-2424, https://doi.org/10.5194/egusphere-2025-2424, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Ice in Greenland either ends in the ocean or on land and in lakes. We show that more than 95% of the margin ends on land. Ice ending in lakes is much rarer, but with 1.4% quite similar to the 2.2% ending in oceans. We also see that more than 20% of the margin ends in extremely steep, often vertical cliffs. We will now be able to compare these maps against local ice velocities, mass loss and climate to understand whether the margin shape teaches us something about the health of ice in the region.
Rafael Kenji Horota, Christian Haug Eide, Kim Senger, Marius Opsanger Jonassen, and Marie Annette Vander Kloet
EGUsphere, https://doi.org/10.5194/egusphere-2025-2248, https://doi.org/10.5194/egusphere-2025-2248, 2025
Short summary
Short summary
We explored how virtual tools can help students prepare for and reflect on outdoor learning in the Arctic. Using surveys from university courses in Svalbard, we found that digital field visits helped students feel more confident, better understand the landscape, and learn more effectively. These tools do not replace real fieldwork but make it more accessible and inclusive. Our research shows how technology can support hands-on learning in remote environments.
Lea Hartl, Patrick Schmitt, Lilian Schuster, Kay Helfricht, Jakob Abermann, and Fabien Maussion
The Cryosphere, 19, 1431–1452, https://doi.org/10.5194/tc-19-1431-2025, https://doi.org/10.5194/tc-19-1431-2025, 2025
Short summary
Short summary
We use regional observations of glacier area and volume change to inform glacier evolution modeling in the Ötztal and Stubai range (Austrian Alps) until 2100 in different climate scenarios. Glaciers in the region lost 23 % of their volume between 2006 and 2017. Under current warming trajectories, glacier loss in the region is expected to be near-total by 2075. We show that integrating regional calibration and validation data in glacier models is important to improve confidence in projections.
Florina Roana Schalamon, Sebastian Scher, Andreas Trügler, Lea Hartl, Wolfgang Schöner, and Jakob Abermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-4060, https://doi.org/10.5194/egusphere-2024-4060, 2025
Short summary
Short summary
Atmospheric patterns influence the air temperature in Greenland. We investigate two warming periods, from 1922–1932 and 1993–2007, both showing similar temperature increases. Using a neural network-based clustering method, we defined predominant atmospheric patterns for further analysis. Our findings reveal that while the connection between these patterns and local air temperature remains stable, the distribution of patterns changes between the warming periods and the full period (1900–2015).
Aleksandra Smyrak-Sikora, Peter Betlem, Victoria S. Engelschiøn, William J. Foster, Sten-Andreas Grundvåg, Mads E. Jelby, Morgan T. Jones, Grace E. Shephard, Kasia K. Śliwińska, Madeleine L Vickers, Valentin Zuchuat, Lars Eivind Augland, Jan Inge Faleide, Jennifer M. Galloway, William Helland-Hansen, Maria A. Jensen, Erik P. Johannessen, Maayke Koevoets, Denise Kulhanek, Gareth S. Lord, Tereza Mosociova, Snorre Olaussen, Sverre Planke, Gregory D. Price, Lars Stemmerik, and Kim Senger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3912, https://doi.org/10.5194/egusphere-2024-3912, 2025
Short summary
Short summary
In this review article we present Svalbard’s unique geological archive, revealing its climate history over the last 540 million years. We uncover how this Arctic region recorded key global events, including end Permian mass extinction, and climate crises like the Paleocene-Eocene Thermal Maximum. The overall climate trend recorded in sedimentary successions in Svalbard is discussed in context of global climate fluctuations and continuous drift of Svalbard from near equator to Arctic latitudes.
Matthew Switanek, Gernot Resch, Andreas Gobiet, Daniel Günther, Christoph Marty, and Wolfgang Schöner
The Cryosphere, 18, 6005–6026, https://doi.org/10.5194/tc-18-6005-2024, https://doi.org/10.5194/tc-18-6005-2024, 2024
Short summary
Short summary
Snow depth plays an important role in water resources, mountain tourism, and hazard management across the European Alps. Our study uses station-based historical observations to quantify how changes in temperature and precipitation affect average seasonal snow depth. We find that the relationship between these variables has been surprisingly robust over the last 120 years. This allows us to more accurately estimate how future climate will affect seasonal snow depth in different elevation zones.
Kim Senger, Grace Shephard, Fenna Ammerlaan, Owen Anfinson, Pascal Audet, Bernard Coakley, Victoria Ershova, Jan Inge Faleide, Sten-Andreas Grundvåg, Rafael Kenji Horota, Karthik Iyer, Julian Janocha, Morgan Jones, Alexander Minakov, Margaret Odlum, Anna Sartell, Andrew Schaeffer, Daniel Stockli, Marie Annette Vander Kloet, and Carmen Gaina
Geosci. Commun., 7, 267–295, https://doi.org/10.5194/gc-7-267-2024, https://doi.org/10.5194/gc-7-267-2024, 2024
Short summary
Short summary
The article describes a course that we have developed at the University Centre in Svalbard that covers many aspects of Arctic geology. The students experience this course through a wide range of lecturers, focussing both on the small and larger scales and covering many geoscientific disciplines.
Jorrit van der Schot, Jakob Abermann, Tiago Silva, Kerstin Rasmussen, Michael Winkler, Kirsty Langley, and Wolfgang Schöner
The Cryosphere, 18, 5803–5823, https://doi.org/10.5194/tc-18-5803-2024, https://doi.org/10.5194/tc-18-5803-2024, 2024
Short summary
Short summary
We present snow data from nine locations in coastal Greenland. We show that a reanalysis product (CARRA) simulates seasonal snow characteristics better than a regional climate model (RACMO). CARRA output matches particularly well with our reference dataset when we look at the maximum snow water equivalent and the snow cover end date. We show that seasonal snow in coastal Greenland has large spatial and temporal variability and find little evidence of trends in snow cover characteristics.
Bernhard Hynek, Daniel Binder, Michele Citterio, Signe Hillerup Larsen, Jakob Abermann, Geert Verhoeven, Elke Ludewig, and Wolfgang Schöner
The Cryosphere, 18, 5481–5494, https://doi.org/10.5194/tc-18-5481-2024, https://doi.org/10.5194/tc-18-5481-2024, 2024
Short summary
Short summary
An avalanche event in February 2018 caused thick snow deposits on Freya Glacier, a peripheral mountain glacier in northeastern Greenland. The avalanche deposits contributed significantly to the mass balance, leaving a strong imprint in the elevation changes in 2013–2021. The 8-year geodetic mass balance (2013–2021) of the glacier is positive, whereas previous estimates by direct measurements were negative and now turned out to have a negative bias.
Tiago Silva, Brandon Samuel Whitley, Elisabeth Machteld Biersma, Jakob Abermann, Katrine Raundrup, Natasha de Vere, Toke Thomas Høye, and Wolfgang Schöner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2571, https://doi.org/10.5194/egusphere-2024-2571, 2024
Short summary
Short summary
Ecosystems in Greenland have experienced significant changes over recent decades. Here, we show the consistency of a high-resolution polar-adapted reanalysis product to represent bio-climatic factors influencing ecological processes. Our results describe the interaction between snowmelt and soil water availability before the growing season onset, infer how changes in the growing season relate to changes in spectral greenness and identify regions of ongoing changes in vegetation distribution.
Christoph Posch, Jakob Abermann, and Tiago Silva
The Cryosphere, 18, 2035–2059, https://doi.org/10.5194/tc-18-2035-2024, https://doi.org/10.5194/tc-18-2035-2024, 2024
Short summary
Short summary
Radar beams from satellites exhibit reflection differences between water and ice. This condition, as well as the comprehensive coverage and high temporal resolution of the Sentinel-1 satellites, allows automatically detecting the timing of when ice cover of lakes in Greenland disappear. We found that lake ice breaks up 3 d later per 100 m elevation gain and that the average break-up timing varies by ±8 d in 2017–2021, which has major implications for the energy budget of the lakes.
Florian Lippl, Alexander Maringer, Margit Kurka, Jakob Abermann, Wolfgang Schöner, and Manuela Hirschmugl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-12, https://doi.org/10.5194/essd-2024-12, 2024
Preprint withdrawn
Short summary
Short summary
The aim of our work was to give an overview of data currently available for the National Park Gesäuse and Johnsbachtal relevant to the European long-term ecosystem monitoring. This data, further was made available on respective data repositories, where all data is downloadable free of charge. Data presented in our paper is from all compartments, the atmosphere, social & economic sphere, biosphere and geosphere. We consider our approach as an opportunity to function as a showcase for other sites.
Maral Habibi, Iman Babaeian, and Wolfgang Schöner
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-48, https://doi.org/10.5194/hess-2024-48, 2024
Publication in HESS not foreseen
Short summary
Short summary
Our study investigates how snow melting affects droughts in Iran's Urmia Lake Basin, revealing that future droughts will likely become more severe due to reduced snowmelt and increased evaporation. This is crucial for understanding water availability in the region, affecting millions. We used advanced climate models and drought indices to predict changes, aiming to inform water management strategies.
Peter Betlem, Thomas Birchall, Gareth Lord, Simon Oldfield, Lise Nakken, Kei Ogata, and Kim Senger
Earth Syst. Sci. Data, 16, 985–1006, https://doi.org/10.5194/essd-16-985-2024, https://doi.org/10.5194/essd-16-985-2024, 2024
Short summary
Short summary
We present the digitalisation (i.e. textured outcrop and terrain models) of the Agardhfjellet Fm. cliffs exposed in Konusdalen West, Svalbard, which forms the seal of a carbon capture site in Longyearbyen, where several boreholes cover the exposed interval. Outcrop data feature centimetre-scale accuracies and a maximum resolution of 8 mm and have been correlated with the boreholes through structural–stratigraphic annotations that form the basis of various numerical modelling scenarios.
Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Åsa K. Rennermalm, Achim Heilig, Jakob Abermann, Dirk van As, Elisa Bjerre, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel R. van den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, https://doi.org/10.5194/tc-18-609-2024, 2024
Short summary
Short summary
How fast is the Greenland ice sheet warming? In this study, we compiled 4500+ temperature measurements at 10 m below the ice sheet surface (T10m) from 1912 to 2022. We trained a machine learning model on these data and reconstructed T10m for the ice sheet during 1950–2022. After a slight cooling during 1950–1985, the ice sheet warmed at a rate of 0.7 °C per decade until 2022. Climate models showed mixed results compared to our observations and underestimated the warming in key regions.
Kim Senger, Denise Kulhanek, Morgan T. Jones, Aleksandra Smyrak-Sikora, Sverre Planke, Valentin Zuchuat, William J. Foster, Sten-Andreas Grundvåg, Henning Lorenz, Micha Ruhl, Kasia K. Sliwinska, Madeleine L. Vickers, and Weimu Xu
Sci. Dril., 32, 113–135, https://doi.org/10.5194/sd-32-113-2023, https://doi.org/10.5194/sd-32-113-2023, 2023
Short summary
Short summary
Geologists can decipher the past climates and thus better understand how future climate change may affect the Earth's complex systems. In this paper, we report on a workshop held in Longyearbyen, Svalbard, to better understand how rocks in Svalbard (an Arctic archipelago) can be used to quantify major climatic shifts recorded in the past.
Sonika Shahi, Jakob Abermann, Tiago Silva, Kirsty Langley, Signe Hillerup Larsen, Mikhail Mastepanov, and Wolfgang Schöner
Weather Clim. Dynam., 4, 747–771, https://doi.org/10.5194/wcd-4-747-2023, https://doi.org/10.5194/wcd-4-747-2023, 2023
Short summary
Short summary
This study highlights how the sea ice variability in the Greenland Sea affects the terrestrial climate and the surface mass changes of peripheral glaciers of the Zackenberg region (ZR), Northeast Greenland, combining model output and observations. Our results show that the temporal evolution of sea ice influences the climate anomaly magnitude in the ZR. We also found that the changing temperature and precipitation patterns due to sea ice variability can affect the surface mass of the ice cap.
Klaus Haslinger, Wolfgang Schöner, Jakob Abermann, Gregor Laaha, Konrad Andre, Marc Olefs, and Roland Koch
Nat. Hazards Earth Syst. Sci., 23, 2749–2768, https://doi.org/10.5194/nhess-23-2749-2023, https://doi.org/10.5194/nhess-23-2749-2023, 2023
Short summary
Short summary
Future changes of surface water availability in Austria are investigated. Alterations of the climatic water balance and its components are analysed along different levels of elevation. Results indicate in general wetter conditions with particular shifts in timing of the snow melt season. On the contrary, an increasing risk for summer droughts is apparent due to increasing year-to-year variability and decreasing snow melt under future climate conditions.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
Short summary
Short summary
Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Tiago Silva, Jakob Abermann, Brice Noël, Sonika Shahi, Willem Jan van de Berg, and Wolfgang Schöner
The Cryosphere, 16, 3375–3391, https://doi.org/10.5194/tc-16-3375-2022, https://doi.org/10.5194/tc-16-3375-2022, 2022
Short summary
Short summary
To overcome internal climate variability, this study uses k-means clustering to combine NAO, GBI and IWV over the Greenland Ice Sheet (GrIS) and names the approach as the North Atlantic influence on Greenland (NAG). With the support of a polar-adapted RCM, spatio-temporal changes on SEB components within NAG phases are investigated. We report atmospheric warming and moistening across all NAG phases as well as large-scale and regional-scale contributions to GrIS mass loss and their interactions.
Jonathan P. Conway, Jakob Abermann, Liss M. Andreassen, Mohd Farooq Azam, Nicolas J. Cullen, Noel Fitzpatrick, Rianne H. Giesen, Kirsty Langley, Shelley MacDonell, Thomas Mölg, Valentina Radić, Carleen H. Reijmer, and Jean-Emmanuel Sicart
The Cryosphere, 16, 3331–3356, https://doi.org/10.5194/tc-16-3331-2022, https://doi.org/10.5194/tc-16-3331-2022, 2022
Short summary
Short summary
We used data from automatic weather stations on 16 glaciers to show how clouds influence glacier melt in different climates around the world. We found surface melt was always more frequent when it was cloudy but was not universally faster or slower than under clear-sky conditions. Also, air temperature was related to clouds in opposite ways in different climates – warmer with clouds in cold climates and vice versa. These results will help us improve how we model past and future glacier melt.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
Short summary
Short summary
Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Kim Senger, Peter Betlem, Sten-Andreas Grundvåg, Rafael Kenji Horota, Simon John Buckley, Aleksandra Smyrak-Sikora, Malte Michel Jochmann, Thomas Birchall, Julian Janocha, Kei Ogata, Lilith Kuckero, Rakul Maria Johannessen, Isabelle Lecomte, Sara Mollie Cohen, and Snorre Olaussen
Geosci. Commun., 4, 399–420, https://doi.org/10.5194/gc-4-399-2021, https://doi.org/10.5194/gc-4-399-2021, 2021
Short summary
Short summary
At UNIS, located at 78° N in Longyearbyen in Arctic Norway, we use digital outcrop models (DOMs) actively in a new course (
AG222 Integrated Geological Methods: From Outcrop To Geomodel) to solve authentic geoscientific challenges. DOMs are shared through the open-access Svalbox geoscientific portal, along with 360° imagery, subsurface data and published geoscientific data from Svalbard. Here we share experiences from the AG222 course and Svalbox, both before and during the Covid-19 pandemic.
Thomas Birchall, Malte Jochmann, Peter Betlem, Kim Senger, Andrew Hodson, and Snorre Olaussen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-226, https://doi.org/10.5194/tc-2021-226, 2021
Preprint withdrawn
Short summary
Short summary
Svalbard has over a century of drilling history, though this historical data is largely overlooked nowadays. After inspecting this data, stored in local archives, we noticed the surprisingly common phenomenon of gas trapped below the permafrost. Methane is a potent greenhouse gas, and the Arctic is warming at unprecedented rates. The permafrost is the last barrier preventing this gas from escaping into the atmosphere and if it thaws it risks a feedback effect to the already warming climate.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
Short summary
Short summary
The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Mikkel Toft Hornum, Andrew Jonathan Hodson, Søren Jessen, Victor Bense, and Kim Senger
The Cryosphere, 14, 4627–4651, https://doi.org/10.5194/tc-14-4627-2020, https://doi.org/10.5194/tc-14-4627-2020, 2020
Short summary
Short summary
In Arctic fjord valleys, considerable amounts of methane may be stored below the permafrost and escape directly to the atmosphere through springs. A new conceptual model of how such springs form and persist is presented and confirmed by numerical modelling experiments: in uplifted Arctic valleys, freezing pressure induced at the permafrost base can drive the flow of groundwater to the surface through vents in frozen ground. This deserves attention as an emission pathway for greenhouse gasses.
Andrew J. Hodson, Aga Nowak, Mikkel T. Hornum, Kim Senger, Kelly Redeker, Hanne H. Christiansen, Søren Jessen, Peter Betlem, Steve F. Thornton, Alexandra V. Turchyn, Snorre Olaussen, and Alina Marca
The Cryosphere, 14, 3829–3842, https://doi.org/10.5194/tc-14-3829-2020, https://doi.org/10.5194/tc-14-3829-2020, 2020
Short summary
Short summary
Methane stored below permafrost is an unknown quantity in the Arctic greenhouse gas budget. In coastal areas with rising sea levels, much of the methane seeps into the sea and is removed before it reaches the atmosphere. However, where land uplift outpaces rising sea levels, the former seabed freezes, pressurising methane-rich groundwater beneath, which then escapes via permafrost seepages called pingos. We describe this mechanism and the origins of the methane discharging from Svalbard pingos.
Cited articles
AccuPower Research, Development and Distribution Company (Ltd.): AccuPower
AkkuPacks,
https://www.accupower.at/produkt-kategorie/akkus/lithium/akkupacks/
(last access: 1 February 2022), 2022. a
Alexander, A., Obu, J., Schuler, T. V., Kääb, A., and Christiansen, H. H.: Subglacial permafrost dynamics and erosion inside subglacial channels driven by surface events in Svalbard, The Cryosphere, 14, 4217–4231, https://doi.org/10.5194/tc-14-4217-2020, 2020. a
Bălaşa, R. I., Olaru, G., Constantin, D., Ștefan, A., Bîlu, C. M.,
and Bălăceanu, M. B.: LIDAR based distance estimation for emergency
use terrestrial autonomous robot, 14th International Conference on
Electronics, Comp. Artif. Intell., 13, 1–4,
https://doi.org/10.1109/ECAI52376.2021.9515047, 2021. a, b
Behley, J. and Stachniss, C.: Efficient Surfel-Based SLAM using 3D Laser Range
Data in Urban Environments, in: Conference: Robotics: Science and Systems
2018, Pittsburgh, Pennsylvania, USA, https://doi.org/10.15607/RSS.2018.XIV.016, 2018.
a
Birkebak, M., Stearns, J., Durell, C., and Scharpf, D.: Radiometry 101
Calibrating with diffuse reflecting targets,
https://www.photonicsonline.com/doc/radiometry-calibrating-with-diffuse-reflecting-targets-0001 (last access: 28 July 2022),
2018. a
Bock, H. and Dolischka, A.: Plan der Lurgrotte Peggau – Semriach, Tech. rep.,
Graz, m = 1:2.500, 1953. a
Boehler, W., Vicent, M., and Marbs, A.: Investigating laser scanner accuracy,
in: Proc. Proceedings of the XIXth International Symposium, CIPA 2003, 34, 2003. a
Bosse, M., Zlot, R., and Flick, P.: Zebedee: Design of a Spring-Mounted 3-D
Range Sensor with Application to Mobile Mapping, IEEE T.
Robot., 28, 1104–1119, https://doi.org/10.1109/TRO.2012.2200990, 2012. a
Continental AG: Technical Documentation ARS 404-21 (Entry) and ARS 408-21
(Premium), Version 1.0, Tech. rep., Continental Engineering Services GmbH, https://www.continental-automotive.com/getattachment/8e4678e1-9358-48e1-8d5b-a0c2de942edb/ARS408-21_Datenblatt_de_170707_V07.pdf.pdf (last access: 1 August 2022),
2016. a
Continental AG: ARS 408-21 Premium Long Range Radar Sensor 77 GHz, ARS 408-21
datasheet, Tech. rep., Continental Engineering Services GmbH, 2017. a
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479,
https://doi.org/10.3189/2013JoG12J154, 2013. a
Druml, N., Maksymova, I., Thurner, T., van Lierop, D., Hennecke, M., and A.,
F.: 1d Mems Micro-Scanning LiDAR, Conference on Sensor Device Technologies
and Applications (SENSORDEVICES), Venice, Italy, 9, 2018. a
Gallay, M., Kaňuk, J., Hochmuth, Z., Meneely, J., Hofierka, J., and
Sedlák, V.: Large-scale and high-resolution 3-d cave mapping by
terrestrial laser scanning: A case study of the Domica cave, Slovakia,
Int. J. Speleol., 44, 277–291, https://doi.org/10.5038/1827-806X.44.3.6, 2015. a
Goelles, T., Hammer, T., Muckenhuber, S., and Schlager, B.: Pointcloud of Longyearbreen glacier surface and glacer cave, Graz University of Technology [data set], https://doi.org/10.3217/182j2-hdn17, 2021a. a
Goelles, T., Schlager, B., Muckenhuber, S., Haas, S., and Hammer, T.:
pointcloudset: Efficient Analysis of Large Datasets of Point Clouds Recorded
Over Time, Journal of Open Source Software, 6, 3471,
https://doi.org/10.21105/joss.03471, 2021b. a
Guégan, E. B. M. and Christiansen, H. H.: Seasonal Arctic Coastal Bluff
Dynamics in Adventfjorden, Svalbard, Permafrost Periglac. Process, 28,
18–31, https://doi.org/10.1002/ppp.1891, 2017. a
Guđmundsdóttir, A. S.: Morphology and Development of the Longyearbreen Ice
Cave, Central Spitsbergen, Svalbard, Bachelor Thesis BS, ritgerð,
Jarðvísin-dadeild, Háskóli Íslands, 2011. a
Hammer, T.: New applications of automotive lidar sensors in geosciences,
Master's Thesis, Graz University of Technology, 2021. a
Hasch, J.: Driving Towards Automotive Radar Technology Trends, IEEE MTT-S
International Conference on Microwaves for Intelligent Mobility, Heidelberg, Germany, 1–4, https://doi.org/10.1109/ICMIM.2015.7117956,
2015. a
Hecht, J.: Lidar for self-driving cars, Optics and Photonics News, 29, 26–33,
2018. a
Ibeo Automotive Systems GmbH: ibeo LUX 4L/ibeo LUX 8L/ibeo LUX HD
Datasheet,
https://hexagondownloads.blob.core.windows.net/public/AutonomouStuff/wp-content/uploads/2019/05/ibeo_LUX_datasheet_whitelabel.pdf
(last access: 4 October 2021), 2021. a
Janos, D. and Przemysław, K.: Evaluation of Low-Cost GNSS Receiver under
Demanding Conditions in RTK Network Mode, Sensors, 21, 5552,
https://doi.org/10.3390/s21165552, 2021. a
Kukko, A., Kaartinen, H., Hyyppä, J., and Chen, Y.: Multiplatform mobile
laser scanning: Usability and performance, Sensors, 12, 11712–11733,
2012. a
Lague, D.: Chapter 8 – Terrestrial laser scanner applied to fluvial
geomorphology, in: Developments in Earth Surface Processes, Elsevier, 23,
231–254, https://doi.org/10.1016/B978-0-444-64177-9.00008-4, 2020. a, b
LeddarTech Inc.: LeddarTech Launches PixSet, the Industry's First
Full-Waveform Flash LiDAR Dataset,
https://leddartech.com/leddartech-launches-pixset-industrys-first-full-waveform-flash-lidar-dataset/ (last access: 28 July 2022),
2021. a
Leica Geosystems AG: Leica ScanStation P30/P40: Leica P30/P40 data sheet,
https://leica-geosystems.com/-/media/files/leicageosystems/products/datasheets/scan/leica scanstation p50 ds 869145 0119 en lr.ashx?la=de-at&hash=9ABF78CC529268400306349359BE769A
(last access: 4 October 2021), 2021a. a
Leica Geosystems AG: Leica ScanStation P50: Leica P50 data sheet,
https://leica-geosystems.com/-/media/files/leicageosystems/products/datasheets/scan/leica scanstation p50 ds 869145 0119 en lr.ashx?la=de-at&hash=9ABF78CC529268400306349359BE769A
(last access: 4 October 2021), 2021b. a
Marti, E., Perez, J., Miguel, M. A., and Garcia, F.: A Review of Sensor
Technologies for Perception in Automated Driving, IEEE Intelligent
Transportation Systems Magazine, 11, 94–108,
https://doi.org/10.1109/MITS.2019.2907630, 2019. a
Moosmann, F. and Stiller, C.: Velodyne SLAM, in: Proceedings of the IEEE
Intelligent Vehicles Symposium, pp. 393–398, Baden-Baden, Germany,
https://doi.org/10.1109/IVS.2011.5940396,
2011. a
Muckenhuber, S., Holzer, H., and Bockaj, Z.: Automotive lidar modelling
approach based on material properties and lidar capabilities, Sensors
20, 3309, https://doi.org/10.3390/s20113309, 2020. a
Nahler, C., Steger, C., and Druml, N.: Quantitative and qualitative evaluation
methods of automotive time of flight based sensors, in:
Proceedings – Euromicro Conference on Digital System Design, DSD 2020, Kranj, Slovenia, 651–659, https://doi.org/10.1109/DSD51259.2020.00106, 2020. a
Neugirg, F., Stark, M., Kaiser, A., Vlacilova, M., Della Seta, M., Vergari,
F., Schmidt, J., Becht, M., and Haas, F.: Erosion processes in calanchi in
the Upper Orcia Valley, Southern Tuscany, Italy based on multitemporal
high-resolution terrestrial LiDAR and UAV surveys, Geomorphology, 269, 8–22,
https://doi.org/10.1016/j.geomorph.2016.06.027, 2016. a
Ocular Robotics Limited: RobotEye RE08 3D LIDAR,
https://www.ocularrobotics.com/products/lidar/re08/ (last access: 1 August 2022), 2018. a
Olsen, M. J. and Kayen, R.: Post-Earthquake and Tsunami 3D Laser Scanning
Forensic Investigations, pp. 477–486,
https://doi.org/10.1061/9780784412640.051, 2012. a
O'Neal, M. A. and Pizzuto, J. E.: The rates and spatial patterns of annual
riverbank erosion revealed through terrestrial laser-scanner surveys of the
South River, Virginia, Earth Surf. Proc. Land., 36, 695–701,
https://doi.org/10.1002/esp.2098, 2011. a
Ouster Inc.: Ouster Example Code, Github [code],
https://github.com/ouster-lidar/ouster_example/tree/20201209, 2020a. a
Patole, S. M., Torlak, M., Wang, D., and Murtaza, A.: Automotive radars: A
review of signal processing techniques, IEEE Signal Processing Magazine, 34,
22–35, https://doi.org/10.1109/MSP.2016.2628914, 2017. a
Perşoiu, A. and Lauritzen, S.-E.: Ice Caves, Elsevier,
https://doi.org/10.1016/C2016-0-01961-7, 2017. a
Plan, L. and Oberender, P.: Höhlen und Karst in Österreich, chap.
Höhlen in Österreich, 11–22, Oberösterreichisches
Landesmuseum, 2016. a
Prokop, A., Schirmer, M., Rub, M., Lehning, M., and Stocker, M.: A comparison
of measurement methods: terrestrial laser scanning, tachymetry and snow
probing for the determination of the spatial snow-depth distribution on
slopes, Ann. Glaciol., 49, 210–216, https://doi.org/10.3189/172756408787814726,
2008. a
Pukanská, K., Bartoš, K., Bella, P., Gašinec, J., Blistan, P., and
Kovanič, v.: Surveying and High-Resolution Topography of the Ochtiná
Aragonite Cave Based on TLS and Digital Photogrammetry, Appl. Sci., 10, 4633,
https://doi.org/10.3390/app10134633, 2020. a
Rabatel, A., Deline, P., Jaillet, S., and Ravanel, L.: Rock falls in
high-alpine rock walls quantified by terrestrial lidar measurements: A case
study in the Mont Blanc area, Geophys. Res. Lett., 35, L10502,
https://doi.org/10.1029/2008GL033424, 2008. a
Ramasubramanian, K. and Ramaiah, K.: Moving from Legacy 24 GHz to
State-of-the-Art 77-GHz Radar, ATZelektronik worldwide, 13, 46–49,
https://doi.org/10.1007/s38314-018-0029-6, 2018. a
Rapstine, T. D., Rengers, F. K., Allstadt, K. E., Iverson, R. M., Smith, J. B.,
Obryk, M., Logan, M., and Olsen, M. J.: Reconstructing the velocity and
deformation of a rapid landslide using multiview video, J.
Geophys. Res.-Ea. Surf., 125, https://doi.org/10.1029/2019JF005348, 2020. a
Rengers, F. K. and Tucker, G. E.: The evolution of gully headcut morphology: a
case study using terrestrial laser scanning and hydrological monitoring,
Earth Surf. Proc. Land., 40, 1304–1317,
https://doi.org/10.1002/esp.3721, 2015. a
Rengers, F. K., Rapstine, T. D., Olsen, M., Allstadt, K. E., Iverson, R. M.,
Leshchinsky, B., Obryk, M., and Smith, J. B.: Using High Sample Rate Lidar
to Measure Debris-Flow Velocity and Surface Geometry, Environ.
Eng. Geosci., 27, 113–126, https://doi.org/10.2113/EEG-D-20-00045, 2021. a, b, c
Senger, K., Betlem, P., Grundvåg, S.-A., Horota, R. K., Buckley, S. J., Smyrak-Sikora, A., Jochmann, M. M., Birchall, T., Janocha, J., Ogata, K., Kuckero, L., Johannessen, R. M., Lecomte, I., Cohen, S. M., and Olaussen, S.: Teaching with digital geology in the high Arctic: opportunities and challenges, Geosci. Commun., 4, 399–420, https://doi.org/10.5194/gc-4-399-2021, 2021. a
s.m.s, smart microwave sensors GmbH: SmartMicro Product information, traffic
management sensor, TRUGRD Stream,
https://www.smartmicro.com/fileadmin/media/Downloads/Traffic_Radar/Sensor_Data_Sheets__24_GHz_/Datasheet_TRUGRD_Stream.pdf
(last access: 4 October 2021), 2021. a
Šupinský, J., Kaňuk, J., Hochmuth, Z., and Gallay, M.: Detecting dynamics of cave floor ice with selective cloud-to-cloud approach, The Cryosphere, 13, 2835–2851, https://doi.org/10.5194/tc-13-2835-2019, 2019. a
Telling, J., Lyda, A., Hartzell, P., and Glennie, C.: Review of Earth science
research using terrestrial laser scanning, Earth-Sci. Rev., 169,
35–68, https://doi.org/10.1016/j.earscirev.2017.04.007, 2017. a
Thakur, R.: Scanning LIDAR in Advanced Driver Assistance Systems and Beyond:
Building a road map for next-generation LIDAR technology, IEEE Consumer
Electronics Magazine, 5, 48–54, https://doi.org/10.1109/MCE.2016.2556878, 2016. a
The pandas development team: pandas-dev/pandas: Pandas, Zenodo,
https://doi.org/10.5281/zenodo.3509134, 2020. a
TixiaoShan: LIO-SAM, GitHub [code], https://github.com/TixiaoShan/LIO-SAM, last access: 1 August 2022. a
Tucci, G., Visintini, D., Bonora, V., and Parisi, E. I.: Examination of Indoor
Mobile Mapping Systems in a Diversified Internal/External Test Field, Appl.
Sci., 8, 401, https://doi.org/10.3390/app8030401, 2018. a
Velodyne Lidar Inc.: Velodyne Lidar Alpha Prime: Velodyne Alpha Prime data
sheet, https://velodynelidar.com/products/alpha-prime/ (last access: 4
October 2021), 2021. a
virtual-vehicle: pointcloudset, GitHub [code], https://github.com/virtual-vehicle/pointcloudset, last access: 1 August 2022. a
Voordendag, A. B., Goger, B., Klug, C., Prinz, R., Rutzinger, M., and Kaser, G.: AUTOMATED AND PERMANENT LONG-RANGE TERRESTRIAL LASER SCANNING IN A HIGH MOUNTAIN ENVIRONMENT: SETUP AND FIRST RESULTS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 153–160, https://doi.org/10.5194/isprs-annals-V-2-2021-153-2021, 2021. a
Wang, Y., Liang, X., Flener, C., Kukko, A., Kaartinen, H., Kurkela, M., Vaaja,
M., Hyyppä, H., and Alho, P.: 3d modeling of coarse fluvial sediments
based on mobile laser scanning data, Remote Sens., 5, 4571–4592, https://doi.org/10.3390/rs5094571, 2013. a
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet
to the microwave: A revised compilation, J. Geophys. Res.-Atmos., 114, D14220, https://doi.org/10.1029/2007JD009744, 2008. a
Watzenig, D. and Horn, M.: Automated Driving – Safer and More Efficient Future
Driving, Springer, Cham, https://doi.org/10.1007/978-3-319-31895-0, 2017. a
Wilkinson, M., Jones, R., Woods, C., Gilment, S., McCaffrey, K., Kokkalas, S.,
and Long, J.: A comparison of terrestrial laser scanning and
structure-from-motion photogrammetry as methods for digital outcrop
acquisition, Geosphere, 12, 1865–1880, https://doi.org/10.1130/GES01342.1, 2016.
a
Williams, J. G., Rosser, N. J., Hardy, R. J., Brain, M. J., and Afana, A. A.: Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency, Earth Surf. Dynam., 6, 101–119, https://doi.org/10.5194/esurf-6-101-2018, 2018. a
Williams, J. G., Rosser, N. J., Hardy, R. J., and Brain, M. J.: The Importance
of Monitoring Interval for Rockfall Magnitude-Frequency Estimation, J. Geophys. Res.-Ea. Surf., 124, 2841–2853,
https://doi.org/10.1029/2019JF005225, 2019. a
Xsens: Download MT Software Suite 2021.4,
https://content.xsens.com/mt-software-suite-download (last access: 1
February 2022), 2021. a
Zhang, J. and Singh, S.: Low-drift and Real-time Lidar Odometry and Mapping,
Autonomous Robots, 41, 401–416, https://doi.org/10.1007/s10514-016-9548-2, 2017. a, b, c
Zhou, Q.-Y., Park, J., and Koltun, V.: Open3D: A Modern Library for 3D
Data Processing, arXiv [preprint], https://doi.org/10.48550/arXiv.1801.09847, 2018. a
Executive editor
Mobile lidar system are innovative sensors that will significantly enhance monitoring of a variety of geoscience phenomena. This manuscript provides promising results for a scientific and operational standpoint.
Mobile lidar system are innovative sensors that will significantly enhance monitoring of a...
Short summary
We propose a newly developed modular MObile LIdar SENsor System (MOLISENS) to enable new applications for small industrial light detection and ranging (lidar) sensors. MOLISENS supports both monitoring of dynamic processes and mobile mapping applications. The mobile mapping application of MOLISENS has been tested under various conditions, and results are shown from two surveys in the Lurgrotte cave system in Austria and a glacier cave in Longyearbreen on Svalbard.
We propose a newly developed modular MObile LIdar SENsor System (MOLISENS) to enable new...
