The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application
Open accessAbstract
Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer‐grade camera. The approach integrates structure‐from‐motion (SfM) and multiview‐stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM‐MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close‐range photogrammetry. The relative precision ratio achieved by SfM‐MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter‐level precision over measurement distances of 10 s of meters). We apply SfM‐MVS at an intermediate scale, to determine erosion rates along a ∼50‐m‐long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a−1. Sequential erosion maps (at ∼0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM‐MVS produced comparable data and reduced data collection time by ∼80%.
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Cites
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A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
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Snavely, N., Seitz, S. M., & Szeliski, R. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0107-3 · Cited by 2,155
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Cited by
Structure from motion photogrammetry in physical geography
Smith, M. W., Carrivick, J. L., & Quincey, D. J. (2016) · Progress in Physical Geography: Earth and Environment · doi:10.1177/0309133315615805 · Cited by 772
Dandois, J., Olano, M., & Ellis, E. (2015) · Remote Sensing · doi:10.3390/rs71013895 · Cited by 435
Woodget, A. S., Carbonneau, P. E., Visser, F., & Maddock, I. P. (2015) · Earth Surface Processes and Landforms · doi:10.1002/esp.3613 · Cited by 424
Cunliffe, A. M., Brazier, R. E., & Anderson, K. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.05.019 · Cited by 365
Hugenholtz, C. H., Whitehead, K., Brown, O. W., Barchyn, T. E., Moorman, B. J., LeClair, A., Riddell, K., & Hamilton, T. (2013) · Geomorphology · doi:10.1016/j.geomorph.2013.03.023 · Cited by 332
Metadata
- Authors
- James, M. R., & Robson, S.
- Venue
- Journal of Geophysical Research: Earth Surface
- Publisher
- American Geophysical Union (AGU)
- Published
- 2012-08-11
- Volume
- 117
- Issue
- F3
- ISSN
- 0148-0227 (ISSN-L)
- OpenAlex
- W2060599314
- Cited by
- 1,116
- References
- 31
- FWCI
- 67.32
- Type
- journal article
- Language
- en
- Open access
- green
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