Articles | Volume 8, issue 4
https://doi.org/10.5194/esurf-8-995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-8-995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Nov 2020
Research article |
| 24 Nov 2020
| 24 Nov 2020
A photogrammetry-based approach for soil bulk density measurements with an emphasis on applications to cosmogenic nuclide analysis
Institute of Geology and Mineralogy, University of Cologne,
Zülpicher Str. 49b, 50674 Cologne, Germany
Steven A. Binnie
Institute of Geology and Mineralogy, University of Cologne,
Zülpicher Str. 49b, 50674 Cologne, Germany
Gregor M. Rink
Institute of Geology and Mineralogy, University of Cologne,
Zülpicher Str. 49b, 50674 Cologne, Germany
present address: Department of Geosciences, University of
Tübingen, Hölderlinstr. 12, 72074 Tübingen, Germany
Katharina Knödgen
Institute of Geology and Mineralogy, University of Cologne,
Zülpicher Str. 49b, 50674 Cologne, Germany
Carlos Miranda
Departemento de Ciencias Geólogicas, Universidad Católica del Norte, Avenida Angamos 0610, Antofagasta, Chile
Nora Tilly
Institute of Geography, University of Cologne, Otto-Fischer-Str. 4,
50674 Cologne, Germany
Tibor J. Dunai
Institute of Geology and Mineralogy, University of Cologne,
Zülpicher Str. 49b, 50674 Cologne, Germany
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Michael Dietze
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Earth Surf. Dynam., 5, 331–345, https://doi.org/10.5194/esurf-5-331-2017, https://doi.org/10.5194/esurf-5-331-2017, 2017
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Laurent Fouinat, Pierre Sabatier, Jérôme Poulenard, Jean-Louis Reyss, Xavier Montet, and Fabien Arnaud
Earth Surf. Dynam., 5, 199–209, https://doi.org/10.5194/esurf-5-199-2017, https://doi.org/10.5194/esurf-5-199-2017, 2017
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Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
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Andrew Valentine and Lara Kalnins
Earth Surf. Dynam., 4, 445–460, https://doi.org/10.5194/esurf-4-445-2016, https://doi.org/10.5194/esurf-4-445-2016, 2016
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Learning algorithms are powerful tools for understanding and working with large data sets, particularly in situations where any underlying physical models may be complex and poorly understood. Such situations are common in geomorphology. We provide an accessible overview of the various approaches that fall under the umbrella of "learning algorithms", discuss some potential applications within geomorphometry and/or geomorphology, and offer advice on practical considerations.
E. Pohl, M. Knoche, R. Gloaguen, C. Andermann, and P. Krause
Earth Surf. Dynam., 3, 333–362, https://doi.org/10.5194/esurf-3-333-2015, https://doi.org/10.5194/esurf-3-333-2015, 2015
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A semi-distributed hydrological model is used to analyse the hydrological cycle of a glaciated high-mountain catchment in the Pamirs.
We overcome data scarcity by utilising various raster data sets as meteorological input. Temperature in combination with the amount of snow provided in winter play the key role in the annual cycle.
This implies that expected Earth surface processes along precipitation and altitude gradients differ substantially.
T. Croissant and J. Braun
Earth Surf. Dynam., 2, 155–166, https://doi.org/10.5194/esurf-2-155-2014, https://doi.org/10.5194/esurf-2-155-2014, 2014
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Short summary
In this study, we comprehensively test a method to derive soil densities under fieldwork conditions. The method is mainly based on images taken from consumer-grade cameras. The obtained soil/sediment densities reflect
truevalues by generally > 95 %, even if a smartphone is used for imaging. All computing steps can be conducted using freeware programs. Soil density is an important variable in the analysis of terrestrial cosmogenic nuclides, for example to infer long-term soil production rates.
In this study, we comprehensively test a method to derive soil densities under fieldwork...
