Publication details for Dr Patrice CarbonneauWoodget, A.S., Visser, F., Maddock, I.P. & Carbonneau, P.E. (2016). The accuracy and reliability of traditional surface flow type mapping: is it time for a new method of characterizing physical river habitat? River Research and Applications 32(9): 1902-1914.
- Publication type: Journal Article
- ISSN/ISBN: 1535-1459, 1535-1467
- DOI: 10.1002/rra.3047
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
Surface flow types (SFTs) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterize rapidly the physical habitat of rivers. SFT mapping is simple, non-invasive and cost-efficient. However, it is also qualitative, subjective and plagued by difficulties in recording accurately the spatial extent of SFT units. Quantitative validation of the underlying physical habitat parameters is often lacking and does not consistently differentiate between SFTs. Here, we investigate explicitly the accuracy, reliability and statistical separability of traditionally mapped SFTs as indicators of physical habitat, using independent, hydraulic and topographic data collected during three surveys of a c. 50 m reach of the River Arrow, Warwickshire, England. We also explore the potential of a novel remote sensing approach, comprising a small unmanned aerial system (sUAS) and structure-from-motion photogrammetry (SfM), as an alternative method of physical habitat characterization. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterizing physical habitat for river science and management applications. In contrast, the sUAS–SfM approach provided high resolution, spatially continuous, spatially explicit, quantitative measurements of water depth and point cloud roughness at the microscale (spatial scales ≤1 m). Such data are acquired rapidly, inexpensively and provide new opportunities for examining the heterogeneity of physical habitat over a range of spatial and temporal scales. Whilst continued refinement of the sUAS–SfM approach is required, we propose that this method offers an opportunity to move away from broad, mesoscale classifications of physical habitat (spatial scales 10–100 m) and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions, which actually exists at the microscale.