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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2/W10-2025
https://doi.org/10.5194/isprs-archives-XLVIII-2-W10-2025-185-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-2-W10-2025-185-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
07 Jul 2025
| 07 Jul 2025
New Approaches in Photo-Bathymetry
Hans-Gerd Maas
Institute of Photogrammetry and Remote Sensing, TUD, Dresden University of Technology, Germany
Hannes Sardemann
Institute of Photogrammetry and Remote Sensing, TUD, Dresden University of Technology, Germany
Christian Mulsow
Institute of Photogrammetry and Remote Sensing, TUD, Dresden University of Technology, Germany
Laure-Anne Gueguen
Department of Geodesy and Geoinformation, TU Wien, Austria
Gottfried Mandlburger
Department of Geodesy and Geoinformation, TU Wien, Austria
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Short summary
Short summary
The simple use of time-lapse cameras as a visual observation tool may already be a great help for environmental investigations. However, beyond that, they have the potential to also deliver precise measurements with high temporal and spatial resolution when applying appropriate processing techniques. In this paper we introduce a method for the determination of glacier motion fields from time-lapse images, but it might also be adapted for other environmental motion analysis tasks.
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Lucas Dammert, Jan Rhomberg-Kauert, Tomas Thalmann, Laure-Anne Gueguen, David Monetti, Hans-Berndt Neuner, and Gottfried Mandlburger
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Michael Grömer, Benjamin Wild, Camillo Ressl, Johannes Otepka, and Gottfried Mandlburger
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 61–68, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-61-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-61-2022, 2022
S. Isfort, F. Maiwald, C. Mulsow, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 113–119, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-113-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-113-2022, 2022
M. Jarzabek-Rychard and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 395–400, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-395-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-395-2022, 2022
F. Maiwald and H. Sardemann
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 823–829, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-823-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-823-2022, 2022
K. Richter, D. Mader, P. Westfeld, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 945–951, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-945-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-945-2022, 2022
M. Jarząbek-Rychard and H-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-5-W1-2022, 1–7, https://doi.org/10.5194/isprs-annals-VIII-5-W1-2022-1-2022, https://doi.org/10.5194/isprs-annals-VIII-5-W1-2022-1-2022, 2022
F. Liebold, R. Lorenzoni, I. Curosu, F. Léonard, V. Mechtcherine, S. Paciornik, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 533–539, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-533-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-533-2021, 2021
K. Richter, D. Mader, P. Westfeld, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 681–688, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-681-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-681-2021, 2021
H. Sardemann, C. Mulsow, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 689–692, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-689-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-689-2021, 2021
J. Otepka, G. Mandlburger, W. Karel, B. Wöhrer, C. Ressl, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 35–42, https://doi.org/10.5194/isprs-annals-V-2-2021-35-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-35-2021, 2021
E. Johnson, D. Floricioiu, E. Schwalbe, R. Koschitzki, H.-G. Maas, C. Cardenas, and G. Casassa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 125–127, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-125-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-125-2020, 2020
E. Schwalbe, R. Koschitzki, E. Johnson, D. F. Mojica Moncada, B. Schröter, C. Cardenas, G. Casassa, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 129–131, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-129-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-129-2020, 2020
J. Otepka, G. Mandlburger, M. Schütz, N. Pfeifer, and M. Wimmer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 293–300, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-293-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-293-2020, 2020
G. Mandlburger, R. Weiß, and T. Artz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 57–64, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-57-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-57-2020, 2020
M. Elias and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 181–187, https://doi.org/10.5194/isprs-annals-V-1-2020-181-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-181-2020, 2020
C. Mulsow, G. Mandlburger, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 671–677, https://doi.org/10.5194/isprs-annals-V-2-2020-671-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-671-2020, 2020
N. Haala, M. Kölle, M. Cramer, D. Laupheimer, G. Mandlburger, and P. Glira
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 727–734, https://doi.org/10.5194/isprs-annals-V-2-2020-727-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-727-2020, 2020
Anette Eltner, Hannes Sardemann, and Jens Grundmann
Hydrol. Earth Syst. Sci., 24, 1429–1445, https://doi.org/10.5194/hess-24-1429-2020, https://doi.org/10.5194/hess-24-1429-2020, 2020
Short summary
Short summary
An automatic workflow is introduced to measure surface flow velocities in rivers. The provided tool enables the measurement of spatially distributed surface flow velocities independently of the image acquisition perspective. Furthermore, the study illustrates how river discharge in previously ungauged and unmeasured regions can be retrieved, considering the image-based flow velocities and digital elevation models of the studied river reach reconstructed with UAV photogrammetry.
F. Liebold, H.-G. Maas, and A. A. Heravi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W18, 107–113, https://doi.org/10.5194/isprs-archives-XLII-2-W18-107-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W18-107-2019, 2019
T. Kaiser, C. Clemen, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W2, 35–39, https://doi.org/10.5194/isprs-archives-XLII-5-W2-35-2019, https://doi.org/10.5194/isprs-archives-XLII-5-W2-35-2019, 2019
D. Mader, K. Richter, P. Westfeld, R. Weiß, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1053–1059, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1053-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1053-2019, 2019
D. Lin, Z. Dong, X. Zhang, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 45–51, https://doi.org/10.5194/isprs-annals-IV-2-W5-45-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-45-2019, 2019
G. Mandlburger, H. Lehner, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 397–404, https://doi.org/10.5194/isprs-annals-IV-2-W5-397-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-397-2019, 2019
S. C. L. Ribeiro, M. Jarzabek-Rychard, J. P. Cintra, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 437–444, https://doi.org/10.5194/isprs-annals-IV-2-W5-437-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-437-2019, 2019
P. Glira, N. Pfeifer, and G. Mandlburger
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 567–574, https://doi.org/10.5194/isprs-annals-IV-2-W5-567-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-567-2019, 2019
H.-G. Maas, D. Mader, K. Richter, and P. Westfeld
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W10, 113–117, https://doi.org/10.5194/isprs-archives-XLII-2-W10-113-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W10-113-2019, 2019
G. Mandlburger and B. Jutzi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 109–116, https://doi.org/10.5194/isprs-annals-IV-1-109-2018, https://doi.org/10.5194/isprs-annals-IV-1-109-2018, 2018
M. Cramer, N. Haala, D. Laupheimer, G. Mandlburger, and P. Havel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 115–120, https://doi.org/10.5194/isprs-archives-XLII-1-115-2018, https://doi.org/10.5194/isprs-archives-XLII-1-115-2018, 2018
G. Mandlburger, J. Kremer, F. Steinbacher, and R. Baran
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 275–282, https://doi.org/10.5194/isprs-archives-XLII-1-275-2018, https://doi.org/10.5194/isprs-archives-XLII-1-275-2018, 2018
M. Kröhnert, R. Anderson, J. Bumberger, P. Dietrich, W. S. Harpole, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 539–542, https://doi.org/10.5194/isprs-archives-XLII-2-539-2018, https://doi.org/10.5194/isprs-archives-XLII-2-539-2018, 2018
D. Lin, M. Jarzabek-Rychard, D. Schneider, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 585–591, https://doi.org/10.5194/isprs-archives-XLII-2-585-2018, https://doi.org/10.5194/isprs-archives-XLII-2-585-2018, 2018
G. Mandlburger
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 659–666, https://doi.org/10.5194/isprs-archives-XLII-2-659-2018, https://doi.org/10.5194/isprs-archives-XLII-2-659-2018, 2018
C. Mulsow, R. Kenner, Y. Bühler, A. Stoffel, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 739–744, https://doi.org/10.5194/isprs-archives-XLII-2-739-2018, https://doi.org/10.5194/isprs-archives-XLII-2-739-2018, 2018
K. Richter, D. Mader, P. Westfeld, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 961–967, https://doi.org/10.5194/isprs-archives-XLII-2-961-2018, https://doi.org/10.5194/isprs-archives-XLII-2-961-2018, 2018
H. Sardemann, A. Eltner, and H.-G. Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1023–1027, https://doi.org/10.5194/isprs-archives-XLII-2-1023-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1023-2018, 2018
E. Watson, H.-G. Maas, F. Schäfer, and S. Hiermaier
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1175–1181, https://doi.org/10.5194/isprs-archives-XLII-2-1175-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1175-2018, 2018
F. Liebold and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 193–200, https://doi.org/10.5194/isprs-annals-IV-2-193-2018, https://doi.org/10.5194/isprs-annals-IV-2-193-2018, 2018
D. Lin, A. Eltner, H. Sardemann, and H.-G. Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 201–208, https://doi.org/10.5194/isprs-annals-IV-2-201-2018, https://doi.org/10.5194/isprs-annals-IV-2-201-2018, 2018
Ellen Schwalbe and Hans-Gerd Maas
Earth Surf. Dynam., 5, 861–879, https://doi.org/10.5194/esurf-5-861-2017, https://doi.org/10.5194/esurf-5-861-2017, 2017
Short summary
Short summary
The simple use of time-lapse cameras as a visual observation tool may already be a great help for environmental investigations. However, beyond that, they have the potential to also deliver precise measurements with high temporal and spatial resolution when applying appropriate processing techniques. In this paper we introduce a method for the determination of glacier motion fields from time-lapse images, but it might also be adapted for other environmental motion analysis tasks.
G. Mandlburger, N. Pfeifer, and U. Soergel
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 123–130, https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017, 2017
G. Mandlburger, K. Wenzel, A. Spitzer, N. Haala, P. Glira, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 259–266, https://doi.org/10.5194/isprs-annals-IV-2-W4-259-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-259-2017, 2017
C. Mulsow, H.-G. Maas, and B. Hentschel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 85–91, https://doi.org/10.5194/isprs-archives-XLI-B5-85-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-85-2016, 2016
G. Mandlburger, M. Pfennigbauer, M. Wieser, U. Riegl, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 933–939, https://doi.org/10.5194/isprs-archives-XLI-B1-933-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-933-2016, 2016
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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