CONCLUSION

Here we present an approach for automated mapping of fluvial hydromorphology from remote sensing imagery. The main advantages of this approach are that it is quick, flexible, and based on readily available imagery, and classifications follow a strict rule-based system, so are unaffected by researcher-subjectivity and associated biases. Firstly, image processing is fast. The approach does not rely on field surveys or hydraulic modelling, both of which are time consuming, and application of an automated decision rule system is potentially faster than qualitatively interpreting images. The method is flexible and can be easily adapted to various criteria. For example, our HMU classification system used a gradient of 4% as a threshold to distinguish betweenmild and steep gradients, but this could be adjusted if it is producing results that appear unrealistic, which may be the case for low-lying, downstream reaches. With the measurement of additional river properties, such as depth or velocity, the classification can be refined to provide a more detailed hydromorphological delineation; for example, depth could be used to distinguish between cascades (shallow) and rapids (deep), and velocity could be used to distinguish between walks (slow) and glides (fast). Imagery of the required types are usually readily abundant. Within a Norwegian context, orthophotos are available for all rivers, and there is a complete high-resolution topographic LiDAR-derived DTM/DSM covering the country. In locations where existing aerial photographs are not available, imagery of a suitable resolution (e.g. 10 cm) can easily be obtained using UAVs. Additionally, a range of national and global data sources exist that can be used for estimating channel surface gradient (e.g. ASTER DEM; see Azizian & Brocca 2020). Finally, using such a system removes researcher-subjectivity, and therefore means that classifications are consistent among different rivers and that the reasons for the classifications are documented.