Rights statement: Accepted for publication in Earth Surface Processes and Landforms. Copyright 2019 American Geophysical Union. Further reproduction or electronic distribution is not permitted.
Accepted author manuscript, 205 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 1/08/2019 |
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<mark>Journal</mark> | Earth Surface Processes and Landforms |
Issue number | 10 |
Volume | 44 |
Number of pages | 4 |
Pages (from-to) | 2081-2084 |
Publication Status | Published |
Early online date | 17/04/19 |
<mark>Original language</mark> | English |
As a topographic modelling technique, structure-from-motion (SfM) photogrammetry combines the utility of digital photogrammetry with a flexibility and ease of use derived from multi-view computer vision methods. In conjunction with the rapidly increasing availability of imagery, particularly from unmanned aerial vehicles, SfM photogrammetry represents a powerful tool for geomorphological research. However, to fully realize this potential, its application must be carefully underpinned by photogrammetric considerations, surveys should be reported in sufficient detail to be repeatable (if practical) and results appropriately assessed to understand fully the potential errors involved. To deliver these goals, robust survey and reporting must be supported through (i) using appropriate survey design, (ii) applying suitable statistics to identify systematic error (bias) and to estimate precision within results, and (iii) propagating uncertainty estimates into the final data products.