- Article
(1358 KB) - Metadata XML
- December 03, 2024
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-1/W2-2023
https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-989-2023
© 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/isprs-archives-XLVIII-1-W2-2023-989-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
13 Dec 2023
| 13 Dec 2023
E-TRAINEE: OPEN E-LEARNING COURSE ON TIME SERIES ANALYSIS IN REMOTE SENSING
M. Potůčková
Charles University, Faculty of Science, Czech Republic
J. Albrechtová
Charles University, Faculty of Science, Czech Republic
K. Anders
Heidelberg University, Institute of Geography, 3DGeo Research Group, Germany
L. Červená
Charles University, Faculty of Science, Czech Republic
J. Dvořák
Charles University, Faculty of Science, Czech Republic
K. Gryguc
University of Warsaw, Chair of Geomatics and Information Systems, Department of Geoinformatics, Cartography and Remote Sensing, Poland
Heidelberg University, Institute of Geography, 3DGeo Research Group, Germany
L. Hunt
Charles University, Faculty of Science, Czech Republic
Z. Lhotáková
Charles University, Faculty of Science, Czech Republic
A. Marcinkowska-Ochtyra
University of Warsaw, Chair of Geomatics and Information Systems, Department of Geoinformatics, Cartography and Remote Sensing, Poland
A. Mayr
University of Innsbruck, Department of Geography, Remote Sensing & Topographic LiDAR Research Group, Austria
E. Neuwirthová
Charles University, Faculty of Science, Czech Republic
A. Ochtyra
University of Warsaw, Chair of Geomatics and Information Systems, Department of Geoinformatics, Cartography and Remote Sensing, Poland
M. Rutzinger
University of Innsbruck, Department of Geography, Remote Sensing & Topographic LiDAR Research Group, Austria
A. Šedová
Charles University, Faculty of Science, Czech Republic
A. Šrollerů
Charles University, Faculty of Science, Czech Republic
L. Kupková
Charles University, Faculty of Science, Czech Republic
Related authors
S. Bijou, L. Kupková, M. Potůčková, L. Červená, and J. Lysák
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 1033–1040, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1033-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1033-2023, 2023
V. Zahs, L. Winiwarter, K. Anders, M. Bremer, M. Rutzinger, M. Potůčková, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1109–1116, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, 2022
L. Červená, G. Pinlová, Z. Lhotáková, E. Neuwirthová, L. Kupková, M. Potůčková, J. Lysák, P. Campbell, and J. Albrechtová
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 381–388, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-381-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-381-2022, 2022
J. Dvořák, M. Potůčková, and V. Treml
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 33–38, https://doi.org/10.5194/isprs-annals-V-3-2022-33-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-33-2022, 2022
L. Červená, L. Kupková, M. Potůčková, and J. Lysák
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 371–376, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-371-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-371-2020, 2020
M. Tomková, J. Lysák, and M. Potůčková
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 333–338, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-333-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-333-2020, 2020
Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, and Lucie Homolová
Earth Syst. Sci. Data, 16, 5069–5087, https://doi.org/10.5194/essd-16-5069-2024, https://doi.org/10.5194/essd-16-5069-2024, 2024
Short summary
Short summary
Radiative transfer models play a key role in monitoring vegetation using remote sensing data such as satellite or airborne images. The development of these models has been hindered by a lack of comprehensive ground reference data on structural and spectral characteristics of forests. Here, we reported datasets on the structural and spectral properties of temperate, hemiboreal, and boreal European forest stands. We anticipate that these data will have wide use in remote sensing applications.
S. Bijou, L. Kupková, M. Potůčková, L. Červená, and J. Lysák
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 1033–1040, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1033-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1033-2023, 2023
Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle
Earth Surf. Dynam., 11, 593–613, https://doi.org/10.5194/esurf-11-593-2023, https://doi.org/10.5194/esurf-11-593-2023, 2023
Short summary
Short summary
We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
Lea Hartl, Thomas Zieher, Magnus Bremer, Martin Stocker-Waldhuber, Vivien Zahs, Bernhard Höfle, Christoph Klug, and Alessandro Cicoira
Earth Surf. Dynam., 11, 117–147, https://doi.org/10.5194/esurf-11-117-2023, https://doi.org/10.5194/esurf-11-117-2023, 2023
Short summary
Short summary
The rock glacier in Äußeres Hochebenkar (Austria) moved faster in 2021–2022 than it has in about 70 years of monitoring. It is currently destabilizing. Using a combination of different data types and methods, we show that there have been two cycles of destabilization at Hochebenkar and provide a detailed analysis of velocity and surface changes. Because our time series are very long and show repeated destabilization, this helps us better understand the processes of rock glacier destabilization.
D. Hulskemper, K. Anders, J. A. Á. Antolínez, M. Kuschnerus, B. Höfle, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 53–60, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, 2022
Jan Pfeiffer, Thomas Zieher, Jan Schmieder, Thom Bogaard, Martin Rutzinger, and Christoph Spötl
Nat. Hazards Earth Syst. Sci., 22, 2219–2237, https://doi.org/10.5194/nhess-22-2219-2022, https://doi.org/10.5194/nhess-22-2219-2022, 2022
Short summary
Short summary
The activity of slow-moving deep-seated landslides is commonly governed by pore pressure variations within the shear zone. Groundwater recharge as a consequence of precipitation therefore is a process regulating the activity of landslides. In this context, we present a highly automated geo-statistical approach to spatially assess groundwater recharge controlling the velocity of a deep-seated landslide in Tyrol, Austria.
Hannah Weiser, Jannika Schäfer, Lukas Winiwarter, Nina Krašovec, Fabian E. Fassnacht, and Bernhard Höfle
Earth Syst. Sci. Data, 14, 2989–3012, https://doi.org/10.5194/essd-14-2989-2022, https://doi.org/10.5194/essd-14-2989-2022, 2022
Short summary
Short summary
3D point clouds, acquired by laser scanning, allow us to retrieve information about forest structure and individual tree properties. We conducted airborne, UAV-borne and terrestrial laser scanning in German mixed forests, resulting in overlapping point clouds with different characteristics. From these, we generated a comprehensive database of individual tree point clouds and corresponding tree metrics. Our dataset may serve as a benchmark dataset for algorithms in forestry research.
K. Anders, L. Winiwarter, D. Schröder, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 973–980, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-973-2022, 2022
A. B. Voordendag, B. Goger, C. Klug, R. Prinz, M. Rutzinger, and G. Kaser
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1093–1099, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1093-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1093-2022, 2022
V. Zahs, L. Winiwarter, K. Anders, M. Bremer, M. Rutzinger, M. Potůčková, and B. Höfle
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1109–1116, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1109-2022, 2022
L. Červená, G. Pinlová, Z. Lhotáková, E. Neuwirthová, L. Kupková, M. Potůčková, J. Lysák, P. Campbell, and J. Albrechtová
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 381–388, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-381-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-381-2022, 2022
L. Winiwarter, K. Anders, D. Schröder, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 79–86, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, 2022
B. Hiebl, A. Mayr, A. Kollert, M. Rutzinger, M. Bremer, N. Helm, and K. Chytry
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 367–374, https://doi.org/10.5194/isprs-annals-V-2-2022-367-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-367-2022, 2022
L. Müller, M. Rutzinger, A. Mayr, and A. Kollert
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 391–398, https://doi.org/10.5194/isprs-annals-V-2-2022-391-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-391-2022, 2022
J. Dvořák, M. Potůčková, and V. Treml
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 33–38, https://doi.org/10.5194/isprs-annals-V-3-2022-33-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-33-2022, 2022
K. Anders, L. Winiwarter, H. Mara, R. C. Lindenbergh, S. E. Vos, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 137–144, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-137-2021, 2021
A. B. Voordendag, B. Goger, C. Klug, R. Prinz, M. Rutzinger, and G. Kaser
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, https://doi.org/10.5194/isprs-annals-V-2-2021-153-2021, 2021
A. Kollert, M. Rutzinger, M. Bremer, K. Kaufmann, and T. Bork-Hüffer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2021, 201–208, https://doi.org/10.5194/isprs-annals-V-4-2021-201-2021, https://doi.org/10.5194/isprs-annals-V-4-2021-201-2021, 2021
Veit Ulrich, Jack G. Williams, Vivien Zahs, Katharina Anders, Stefan Hecht, and Bernhard Höfle
Earth Surf. Dynam., 9, 19–28, https://doi.org/10.5194/esurf-9-19-2021, https://doi.org/10.5194/esurf-9-19-2021, 2021
Short summary
Short summary
In this work, we use 3D point clouds to detect topographic changes across the surface of a rock glacier. These changes are presented as the relative contribution of surface change during a 3-week period to the annual surface change. By comparing these different time periods and looking at change in different directions, we provide estimates showing that different directions of surface change are dominant at different times of the year. This demonstrates the benefit of frequent monitoring.
M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020
L. Červená, L. Kupková, M. Potůčková, and J. Lysák
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 371–376, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-371-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-371-2020, 2020
M. Tomková, J. Lysák, and M. Potůčková
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 333–338, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-333-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-333-2020, 2020
A. Mayr, M. Bremer, and M. Rutzinger
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 765–772, https://doi.org/10.5194/isprs-annals-V-2-2020-765-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-765-2020, 2020
L. Winiwarter, K. Anders, D. Wujanz, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 789–796, https://doi.org/10.5194/isprs-annals-V-2-2020-789-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-789-2020, 2020
J. Boehm, M. Rutzinger, B. Yang, M. Weinmann, B. Riveiro, and W. Yao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 915–917, https://doi.org/10.5194/isprs-archives-XLII-2-W13-915-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-915-2019, 2019
M. Bremer, V. Wichmann, M. Rutzinger, T. Zieher, and J. Pfeiffer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 943–950, https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019, 2019
J. Boehm, M. Rutzinger, B. Yang, M. Weinmann, B. Riveiro, and W. Yao
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 313–315, https://doi.org/10.5194/isprs-annals-IV-2-W5-313-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-313-2019, 2019
K. Anders, R. C. Lindenbergh, S. E. Vos, H. Mara, S. de Vries, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 317–324, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, 2019
A. Kumar, K. Anders, L Winiwarter, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 373–380, https://doi.org/10.5194/isprs-annals-IV-2-W5-373-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-373-2019, 2019
A. Mayr, M. Bremer, M. Rutzinger, and C. Geitner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 405–412, https://doi.org/10.5194/isprs-annals-IV-2-W5-405-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-405-2019, 2019
J. Pfeiffer, T. Zieher, M. Rutzinger, M. Bremer, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 421–428, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, 2019
T. Zieher, M. Bremer, M. Rutzinger, J. Pfeiffer, P. Fritzmann, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 461–467, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, 2019
A. Mayr, M. Rutzinger, and C. Geitner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 691–697, https://doi.org/10.5194/isprs-archives-XLII-2-691-2018, https://doi.org/10.5194/isprs-archives-XLII-2-691-2018, 2018
T. Zieher, I. Toschi, F. Remondino, M. Rutzinger, Ch. Kofler, A. Mejia-Aguilar, and R. Schlögel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1243–1250, https://doi.org/10.5194/isprs-archives-XLII-2-1243-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1243-2018, 2018
S. Crommelinck, B. Höfle, M. N. Koeva, M. Y. Yang, and G. Vosselman
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 81–88, https://doi.org/10.5194/isprs-annals-IV-2-81-2018, https://doi.org/10.5194/isprs-annals-IV-2-81-2018, 2018
M. Scaioni, B. Höfle, A. P. Baungarten Kersting, L. Barazzetti, M. Previtali, and D. Wujanz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1503–1510, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, 2018
M. Hämmerle, N. Lukač, K.-C. Chen, Zs. Koma, C.-K. Wang, K. Anders, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 59–65, https://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-59-2017, 2017
Sabrina Marx, Katharina Anders, Sofia Antonova, Inga Beck, Julia Boike, Philip Marsh, Moritz Langer, and Bernhard Höfle
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2017-49, https://doi.org/10.5194/esurf-2017-49, 2017
Revised manuscript has not been submitted
Short summary
Short summary
Global climate warming causes permafrost to warm and thaw, and, consequently, to release the carbon into the atmosphere. Terrestrial laser scanning is evaluated and current methods are extended in the context of monitoring subsidence in Arctic permafrost regions. The extracted information is important to gain a deeper understanding of permafrost-related subsidence processes and provides highly accurate ground-truth data which is necessary for further developing area-wide monitoring methods.
Luisa Griesbaum, Sabrina Marx, and Bernhard Höfle
Nat. Hazards Earth Syst. Sci., 17, 1191–1201, https://doi.org/10.5194/nhess-17-1191-2017, https://doi.org/10.5194/nhess-17-1191-2017, 2017
Short summary
Short summary
This study provides a new method for flood documentation based on user-generated flood images. We demonstrate how flood elevation and building inundation depth can be derived from photographs by means of 3-D reconstruction of the scene. With an accuracy of 0.13 m ± 0.10 m, the derived building inundation depth can be used to facilitate damage assessment.
Thomas Zieher, Martin Rutzinger, Barbara Schneider-Muntau, Frank Perzl, David Leidinger, Herbert Formayer, and Clemens Geitner
Nat. Hazards Earth Syst. Sci., 17, 971–992, https://doi.org/10.5194/nhess-17-971-2017, https://doi.org/10.5194/nhess-17-971-2017, 2017
Short summary
Short summary
At catchment scale, it is challenging to provide the required input parameters for physically based slope stability models. In the present study, the parameterization of such a model is optimized against observed shallow landslides during two triggering rainfall events. With the resulting set of parameters the model reproduces the location and the triggering timing of most observed landslides. Based on that, potential effects of increasing precipitation intensity on slope stability are assessed.
L. Červená, L. Kupková, and R. Suchá
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B6, 211–214, https://doi.org/10.5194/isprs-archives-XLI-B6-211-2016, https://doi.org/10.5194/isprs-archives-XLI-B6-211-2016, 2016
Andreas Mayr, Martin Rutzinger, Magnus Bremer, and Clemens Geitner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 137–144, https://doi.org/10.5194/isprs-annals-III-5-137-2016, https://doi.org/10.5194/isprs-annals-III-5-137-2016, 2016
M. Rutzinger, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, R. Sailer, M. Scaioni, J. Stötter, and D. Wujanz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-6, 15–22, https://doi.org/10.5194/isprs-annals-III-6-15-2016, https://doi.org/10.5194/isprs-annals-III-6-15-2016, 2016
S. Bechtold and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 161–168, https://doi.org/10.5194/isprs-annals-III-3-161-2016, https://doi.org/10.5194/isprs-annals-III-3-161-2016, 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.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org