Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-1007-2025
© 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/soil-11-1007-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
01 Dec 2025
Original research article |
| 01 Dec 2025
| 01 Dec 2025
Near-continuous observation of soil surface changes at single slopes with high spatial resolution via an automated SfM photogrammetric mapping approach
Oliver Grothum
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Helmholtzstr. 10, 01069 Dresden, Germany
Lea Epple
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Helmholtzstr. 10, 01069 Dresden, Germany
Anne Bienert
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Helmholtzstr. 10, 01069 Dresden, Germany
Xabier Blanch
Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Anette Eltner
CORRESPONDING AUTHOR
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Helmholtzstr. 10, 01069 Dresden, Germany
Related authors
Lea Epple, Oliver Grothum, Anne Bienert, and Anette Eltner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-380, https://doi.org/10.5194/essd-2025-380, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
The present study offers a unique, nested, high-resolution dataset that captures soil surface changes every 20 seconds during rainfall events, as well as over seasonal variations, across a range of spatial scales – plot, slope, and catchment. These data collected over a period of 3.5 years was facilitated by a camera-based approach. This open-access resource assists scientists in evaluating and refining existing models, improving process understanding, and training artificial intelligence.
Anette Eltner, David Favis-Mortlock, Oliver Grothum, Martin Neumann, Tomáš Laburda, and Petr Kavka
SOIL, 11, 413–434, https://doi.org/10.5194/soil-11-413-2025, https://doi.org/10.5194/soil-11-413-2025, 2025
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This study develops a new method to improve the calibration and evaluation of models that predict soil erosion by water. By using advanced imaging techniques, we can capture detailed changes in the soil surface over time. This helps improve models that forecast erosion, especially as climate change creates new and unpredictable conditions. Our findings highlight the need for more precise tools to better model erosion of our land and environment in the future.
O. Grothum, A. Bienert, M. Bluemlein, and A. Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 163–170, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-163-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-163-2023, 2023
Felix Rocco Matzke, Tushar Jayesh Barot, Shreeja Sridharan, Nikolaus Ammann, Christoph Kessler, Hans-Gerd Maas, Frank H. P. Fitzek, and Anette Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W11-2025, 211–218, https://doi.org/10.5194/isprs-archives-XLVIII-2-W11-2025-211-2025, https://doi.org/10.5194/isprs-archives-XLVIII-2-W11-2025-211-2025, 2025
Hanne Hendrickx, Melanie Elias, Xabier Blanch, Reynald Delaloye, and Anette Eltner
Earth Surf. Dynam., 13, 705–721, https://doi.org/10.5194/esurf-13-705-2025, https://doi.org/10.5194/esurf-13-705-2025, 2025
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Short summary
This study presents a novel AI-based method for tracking and analysing the movement of rock glaciers and landslides, key landforms in high mountain regions. By utilising time-lapse images, our approach generates detailed velocity data, uncovering movement patterns often missed by traditional methods. This cost-effective tool enhances geohazard monitoring, providing insights into environmental drivers, improving process understanding, and contributing to better safety in alpine areas.
Lea Epple, Oliver Grothum, Anne Bienert, and Anette Eltner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-380, https://doi.org/10.5194/essd-2025-380, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
The present study offers a unique, nested, high-resolution dataset that captures soil surface changes every 20 seconds during rainfall events, as well as over seasonal variations, across a range of spatial scales – plot, slope, and catchment. These data collected over a period of 3.5 years was facilitated by a camera-based approach. This open-access resource assists scientists in evaluating and refining existing models, improving process understanding, and training artificial intelligence.
Anette Eltner, David Favis-Mortlock, Oliver Grothum, Martin Neumann, Tomáš Laburda, and Petr Kavka
SOIL, 11, 413–434, https://doi.org/10.5194/soil-11-413-2025, https://doi.org/10.5194/soil-11-413-2025, 2025
Short summary
Short summary
This study develops a new method to improve the calibration and evaluation of models that predict soil erosion by water. By using advanced imaging techniques, we can capture detailed changes in the soil surface over time. This helps improve models that forecast erosion, especially as climate change creates new and unpredictable conditions. Our findings highlight the need for more precise tools to better model erosion of our land and environment in the future.
Xabier Blanch, Jens Grundmann, Ralf Hedel, and Anette Eltner
EGUsphere, https://doi.org/10.5194/egusphere-2025-724, https://doi.org/10.5194/egusphere-2025-724, 2025
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This study presents a low-cost, automated system for monitoring river water levels using cameras and AI. By combining AI-based image analysis with photogrammetry, it accurately measures water levels in real-time, even in challenging conditions. Tested over 2.5 years at four sites, it achieved high accuracy (errors of 1.0–2.3 cm) and processed over 219,000 images. Its resilience makes it ideal for flood detection and water management in remote areas.
Robert Krüger, Xabier Blanch, Jens Grundmann, Ghazi Al-Rawas, and Anette Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W8-2024, 243–250, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-243-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-243-2024, 2024
Pedro Alberto Pereira Zamboni, Hanne Hendrickx, Dennis Sprute, Holger Flatt, Muhtasimul Islam Rushdi, Florian Brodrecht, and Anette Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W8-2024, 483–490, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-483-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-483-2024, 2024
Anne Bienert, Katja Richter, Sophia Boehme, and Hans-Gerd Maas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-2024, 33–40, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-33-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-33-2024, 2024
Melanie Elias, Steffen Isfort, Anette Eltner, and Hans-Gerd Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 57–64, https://doi.org/10.5194/isprs-annals-X-2-2024-57-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-57-2024, 2024
Robert Krüger, Pierre Karrasch, and Anette Eltner
Geosci. Instrum. Method. Data Syst., 13, 163–176, https://doi.org/10.5194/gi-13-163-2024, https://doi.org/10.5194/gi-13-163-2024, 2024
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Low-cost sensors could fill gaps in existing observation networks. To ensure data quality, the quality of the factory calibration of a given sensor has to be evaluated if the sensor is used out of the box. Here, the factory calibration of a widely used low-cost rain gauge type has been tested both in the lab (66) and in the field (20). The results of the study suggest that the calibration of this particular type should at least be checked for every sensor before being used.
O. Grothum, A. Bienert, M. Bluemlein, and A. Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 163–170, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-163-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-163-2023, 2023
Xabier Blanch, Marta Guinau, Anette Eltner, and Antonio Abellan
Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023, https://doi.org/10.5194/nhess-23-3285-2023, 2023
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We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.
R. Blaskow and A. Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 45–50, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-45-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-45-2023, 2023
Robert Ljubičić, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, https://doi.org/10.5194/hess-25-5105-2021, 2021
Short summary
Short summary
The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed widespread use of image velocimetry techniques in place of more traditional, usually slower, methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, we compare the performance of different UAS video stabilisation tools and provide guidelines for their use in videos with different flight and ground conditions.
Lea Epple, Andreas Kaiser, Marcus Schindewolf, and Anette Eltner
SOIL Discuss., https://doi.org/10.5194/soil-2021-85, https://doi.org/10.5194/soil-2021-85, 2021
Revised manuscript not accepted
Short summary
Short summary
Intensified extreme weather events due to climate change can result in changes of soil erosion. These unclear developments make an improvement of soil erosion modelling all the more important. Assuming that soil erosion models cannot keep up with the current data, this work gives an overview of 44 models, their strengths and weaknesses and discusses their potential for further development with respect to new and improved soil and soil erosion assessment techniques.
A. Eltner, D. Mader, N. Szopos, B. Nagy, J. Grundmann, and L. Bertalan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 717–722, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-717-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-717-2021, 2021
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Short summary
Soil erosion threatens landscapes worldwide, and understanding how surfaces change over time is key to addressing this issue. We developed a new camera-based system that automatically captures and analyzes daily surface changes on a hillside over several years. Triggered by rain and a clock, the system showed how weather and farming impact the land. Our method offers a powerful way to monitor surface changes and can help improve predictions and solutions for soil erosion.
Soil erosion threatens landscapes worldwide, and understanding how surfaces change over time is...
