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Drones and digital photogrammetry: from classifications to continuums for monitoring river habitat and hydromorphology
Open accessAbstract
Recently, we have gained the opportunity to obtain very high‐resolution imagery and topographic data of rivers using drones and novel digital photogrammetric processing techniques. The high‐resolution outputs from this method are unprecedented, and provide the opportunity to move beyond river habitat classification systems, and work directly with spatially explicit continuums of data. Traditionally, classification systems have formed the backbone of physical river habitat monitoring for their ease of use, rapidity, cost efficiency, and direct comparability. Yet such classifications fail to characterize the detailed heterogeneity of habitat, especially those features which are small or marginal. Drones and digital photogrammetry now provide an alternative approach for monitoring river habitat and hydromorphology, which we review here using two case studies. First, we demonstrate the classification of river habitat using drone imagery acquired in 2012 of a 120 m section of the San Pedro River in Chile, which was at the technological limits of what could be achieved at that time. Second, we review how continuums of data can be acquired, using drone imagery acquired in 2016 from the River Teme in Herefordshire, England. We investigate the precision and accuracy of these data continuums, highlight key current challenges, and review current best practices of data collection, processing, and management. We encourage further quantitative testing and field applications. If current difficulties can be overcome, these continuums of geomorphic and hydraulic information hold great potential for providing new opportunities for understanding river systems to the benefit of both river science and management. WIREs Water 2017, 4:e1222. doi: 10.1002/wat2.1222 This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Water and Life > Methods
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R., & Cushing, C. E. (1980) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/f80-017 · Cited by 9,984
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
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Cited by
Drones for Conservation in Protected Areas: Present and Future
Jiménez López, J., & Mulero-Pázmány, M. (2019) · Drones · doi:10.3390/drones3010010 · Cited by 300
Metadata
- Authors
- Woodget, A. S., Austrums, R., Maddock, I. P., & Habit, E.
- Venue
- WIREs Water
- Publisher
- Wiley
- Published
- 2017-04-26
- Volume
- 4
- Issue
- 4
- ISSN
- 2049-1948 (ISSN-L)
- OpenAlex
- W2607557515
- Cited by
- 180
- References
- 3
- FWCI
- 14.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
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