Structure from motion cross-scale dataset on agricultural areas in eastern Germany over a period of 3.5 years – plot scale, single slope scale, and catchment scale
Contributing person | Flow and Transport Modeling in the Geosphere | |
Contributing person | TLLLR (Thuringian State Office for Agriculture and Rural Areas) | |
Contributing person | LfULG (Saxon State Office for Environment, Agriculture and Geology, Germany) | |
Documentation of the data | The dataset is organized hierarchically by spatial scale into three main folders: - I_catchment - II_slope - III_plot Each of these scale-specific folders is subdivided into: - 0_raw: containing raw input data as acquired in the field - 1_processed: containing outputs from data processing workflows (e.g., dense point clouds, change detection) Catchment scale (folder: I_catchment) - I_catchment_0_raw contains: • UAV_images: UAV imagery sorted by flight date folder (yyyy-mm-dd) • GCPs: Ground control point data - I_catchment_1_processed contains: • UAV_dense: Dense point clouds from UAV imagery in .ply and .e57 formats Slope scale (folder: II_slope) - Subdivided by slope position: lower_slope, middle_slope, upper_slope, • SLR subdivided by months (yyyy-mm) and further by camera number • Dense point, ptPrecision subdivided by months (named yyyy-mm) • Fieldwork subdivided by days (yyyy-mm-dd) - M3C2 organised according to the reference date (no more subdivision) - UAV-images subdivided by date (yyyy-mm-dd) - II_slope_0_raw contains: • GCPs: Coordinates and positions of ground control points • Protocol_fieldwork: Field metadata (e.g., bulk density, soil moisture, soil cover, rainfall intensity, organic carbon, grain size distribution, discharge timeline) • SLR: Raw image data by slope position and camera ID • UAV_images: UAV imagery sorted by flight date • Weather: Time series from the on-site weather station (2020-09-04 to 2022-10-05) - II_slope_1_processed contains: • Camera_calibration • SfM_timelapse: Dense point clouds and precision maps (filtered/unfiltered), M3C2 (named by reference date and time yyyy-mm-ddThh-mm-ss and compare dataset yyyy-mm-ddThh-mm-ss); sorted by slope position and date, including also summary log- and ptPrecision-file Plot scale (folder: III_plot) - Subdivided by date of rainfall simulation - III_plot_0_raw contains: • GCPs: Coordinates and positions of ground control points • Protocol_fieldwork: Field metadata (e.g., bulk density, soil moisture, soil cover, rainfall intensity, organic carbon, grain size distribution, tillage, crop type and stage, discharge and sediment time series [min]) • SLR: Raw camera data from DSLR cameras - III_plot_1_processed contains: • Camera_calibration: Internal camera parameters and calibration information (format TBD; typically JSON/XML or CSV) • SfM_timelapse: Dense point clouds and precision maps (filtered/unfiltered), sorted by experiment date; includes .txt files for M3C2 change detection outputs (referenced to first time step), including also each a summary log- and ptPrecision-file | |
Additional geographical or spatial references | 51° 11‘ 31‘‘N, 13° 17‘ 37‘‘E | |
Additional geographical or spatial references | 51° 03‘ 25‘‘N, 13° 15‘ 39‘‘E | |
Additional geographical or spatial references | 51° 03‘ 44‘‘N, 13° 15‘ 38‘‘E | |
Additional geographical or spatial references | 51° 03‘ 21‘‘N, 11° 19‘ 26‘‘E | |
Additional geographical or spatial references | 51° 11‘ 48‘‘N, 13° 09‘ 28‘‘E | |
Additional geographical or spatial references | 51° 03‘ 56‘‘N, 11° 20‘ 55‘‘E | |
Additional geographical or spatial references | 51° 05‘ 56‘‘N, 11° 21‘ 35‘‘E | |
Additional geographical or spatial references | 51° 04‘ 52‘‘N, 11° 21‘ 35‘‘E | |
Additional geographical or spatial references | 51° 03‘ 42‘‘N, 13° 15‘ 35‘‘E | |
Countries to which the data refer | GERMANY | |
References to related material | Epple, L., Grothum, O., Bienert, A., and Eltner, A.: Decoding rainfall effects on soil surface changes: Empirical separation of sediment yield in time-lapse SfM photogrammetry measurements, Soil and Till. Res., 248, 106384, https://doi.org/10.1016/j.still.2024.106384, 2025. | |
References to related material | Epple, L., Bienert, A., Grothum, O., Lenz, J. & Eltner, A. (2025) Uncovering fine-scale surface flow dynamics with particle tracking velocimetry: A new benchmark for soil erosion modelling. Earth Surface Processes and Landforms, 50(9), e70127. Available from: https://doi.org/10. 1002/esp.70127 | |
References to related material | Grothum, O., Epple, L., Bienert, A., Blanch, X., and Eltner, A.: Near-continuous observation of soil surface changes at single slopes with high spatial resolution via an automated SfM photogrammetric mapping approach, SOIL, 11, 1007–1028, https://doi.org/10.5194/soil-11-1007-2025, 2025. | |
Description of the data | This dataset was generated to improve the calibration and validation of process-based soil erosion models by applying high-resolution, multi-scale and time-lapse photogrammetric observations. Although soil erosion models are vital for understanding and predicting surface processes, they face challenges due to limited spatio-temporal data resolution, assumptions of parameter stationarity and model equifinality. To address these limitations, a unique, nested, cross-scale dataset was collected using Structure from Motion (SfM) photogrammetry at plot, hillslope and catchment scales. The primary objective of the data collection was to capture changes to the soil surface during erosional processes at varying temporal resolutions and spatial extents in order to support model evaluation and development. The dataset comprises three main components: 1) Plot-scale time-lapse data: High-frequency SfM data (DEM generation at 10–60 second intervals) were captured during artificial rainfall simulations. These datasets enable the detailed monitoring of micro-topographic surface changes, including rill initiation, soil settling and compaction processes. 2) Field-scale data: Daily to sub-daily SfM observations (with DEM intervals as fine as 0.2 mm of rainfall) were recorded under natural rainfall conditions over a nearly four-year monitoring period. This data is also supplemented by UAV (uncrewed aerial vehicle) data. This data represents longer-term erosional dynamics and surface evolution under natural climatic conditions. 3) Catchment scale UAV data: Aerial imagery was captured via UAV platforms and processed into digital elevation models and orthophotos using SfM methods. These data extend the spatial scale of analysis and enable the linkage between plot-level processes and larger-scale sediment transport patterns. All data were acquired using calibrated digital cameras and processed through standardised Structure from Motion (SfM) workflows, employing open-source and commercial photogrammetric software. Ground control points and quality assurance procedures were used to ensure geometric consistency and repeatability across datasets. Additional validation was performed using reference targets and control DEMs from laser scanning in selected experiments. The dataset is organised into individual folders corresponding to different spatial scales and time periods. Each folder contains raw imagery, processed DEMs, orthophotos, metadata and processing logs. The dataset is made available in an open-access, structured zip archive format. Full details of the data processing steps can be found in Grothum et al. (2025) and Eltner et al. (2025). A 'List of Files' document is also provided to help you navigate the folder structure. This comprehensive, high-resolution dataset supports retrospective and real-time analysis of erosion processes, and can be used to validate existing and emerging soil erosion models. It has already been used in studies evaluating models, with a focus on distinguishing between erosional and soil compaction processes (Epple et al., 2025; Epple et al., submitted). The data are intended for reuse by the soil erosion and geomorphology research communities, and can be incorporated into future model development, data assimilation techniques and remote sensing applications. | |
Type of the data | Dataset | |
Total size of the dataset | 3859514459783 | |
Author | Epple, Lea | |
Author | Eltner, Anette | |
Author | Grothum, Oliver | |
Author | Bienert, Anne | |
Upload date | 2026-02-05T09:21:03Z | |
Publication date | 2026-02-05T09:21:03Z | |
Data of data creation | 2024 | |
Publication date | 2026-02-05 | |
Abstract of the dataset | This study presents a unpresented approach to enhance soil erosion modelling through the utilisation of nested high-resolution spatio-temporal data obtained through structure from motion (SfM) photogrammetry. This technique permits comprehensive observation of soil surface elevation changes during precipitation events, encompassing data acquisition at diverse scales, from plot to slope to micro-catchment. The study presents a unique dataset that integrates high-resolution time-lapse photogrammetry, field measurements, and UAV (uncrewed aerial vehicle) photogrammetric data, collected over nearly four years. This dataset is intended to enhance the understanding of soil erosion processes and serve as a valuable resource for model evaluation and calibration. The authors encourage the broader scientific community to utilise and expand this dataset, which is expected to contribute to the development of more accurate soil erosion models, thereby improving predictions and management strategies. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/1895 | |
Public reference to this page | https://doi.org/10.25532/OPARA-1038 | |
Publisher | Technische Universität Dresden | |
Licence | Attribution 4.0 International | en |
URI of the licence text | http://creativecommons.org/licenses/by/4.0/ | |
Specification of the discipline(s) | 3::34::315::315-02 | |
Title of the dataset | Structure from motion cross-scale dataset on agricultural areas in eastern Germany over a period of 3.5 years – plot scale, single slope scale, and catchment scale | |
Research instruments | RIEGL VZ400i terrestrial laser scanning system; DJI Phantom 4 RTK drone; Leica TCRM 1102 total station; DSLR cameras - Canon EOS D2000 models; event-based triggering with Arduino MKR FOX 1200 | |
Software | https://github.com/onlyole/TimeLapseErosion.git (Grothum, 2025) | |
Software | Grothum, O., Epple, L., Bienert, A., Blanch, X., and Eltner, A.: Near-continuous observation of soil surface changes at single slopes with high spatial resolution via an automated SfM photogrammetric mapping approach, SOIL, 11, 1007–1028, https://doi.org/10.5194/soil-11-1007-2025, 2025. | |
Project abstract | Although process-based soil erosion models are valuable tools for predicting and managing soil erosion, their limitations arise from constraints in integrating novel data possibilities, uncertainties in parameterisation, and the difficulties in integrating different process scales and their transitions. This study presents a unpresented approach to enhance soil erosion modelling through the utilisation of nested high-resolution spatio-temporal data obtained through structure from motion (SfM) photogrammetry. This technique permits comprehensive observation of soil surface elevation changes during precipitation events, encompassing data acquisition at diverse scales, from plot to slope to micro-catchment. The study presents a unique dataset that integrates high-resolution time-lapse photogrammetry, field measurements, and UAV (uncrewed aerial vehicle) photogrammetric data, collected over nearly four years. This dataset is intended to enhance the understanding of soil erosion processes and serve as a valuable resource for model evaluation and calibration. The authors encourage the broader scientific community to utilise and expand this dataset, which is expected to contribute to the development of more accurate soil erosion models, thereby improving predictions and management strategies. | |
Funding Acknowledgement | The work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG 405774238) in the project “High resolution photogrammetric methods for nested parameterisation and validation of a physical based soil erosion model”. | |
Project title | Soil surface change data of high spatio-temporal resolution from the plot to the catchment scale |
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