DISCUSSION
This study has shown that an automated HMU classification system, based
on a CNN classification of surface patterns evident in aerial
orthophotos, and a rule-based classification of surface gradient based
on topographic LiDAR-derived DSMs, can be used to successfully classify
river stretches into sections of distinct hydromorphology. For the three
river stretches examined, classifications broadly coincided with those
previously identified (Borsányi 2006, Hindar et al. 2019). However,
further refinement of the CNN with respect to classification of surface
patterns may lead to a better characterization of hydromorphological
conditions, particularly with regard to distinguishing between
turbulence generated locally (standing waves) and upstream (advective
diffusion of air bubbles/foam). In the following sections, we discuss
the HMU classifications for our study rivers, discuss the sensitivity of
the classification to the data used for training and validating the CNN
and for making subsequent predictions, and examine how the approach
outlined here can be further developed.
Hydromorphological unit classifications in the study
rivers
HMU classifications produced by the automated classification system were
generally consistent with previously made manual classifications. The
automated classification was derived from the best currently available
aerial orthophotos for comparison with previously made manual
classifications. Differences between automated and manual
classifications may partly be attributed to differences in flow
conditions at the time of surveying, which are known to affect
mesohabitat classifications (Hauer et al. 2009).
No large contiguous area of steep-smooth HMU, associated with the
run mesohabitat, was found. Conditions of a smooth or rippled surface
pattern alongside a steep gradient are rare in Norway because steep
gradients are often associated with standing wave surface patterns; in
16 Norwegian rivers for which manual mesohabitat classifications are
available (Alta, Aurland, Eidselva, Enningdalselvam, Flomselvi,
Halselva, Imsa, Jolstra, Laerdal, Laerdal, Laukhelle, Nausta, Orkla,
Stjorda, Stryn, Suldalslågen; see Hindar et al. 2019), no locations have
been classified as corresponding to steep-smooth conditions.
Cases where isolated cells were classified as steep-smooth could
be argued to be misclassifications, suggesting that it is necessary to
set a minimum size for an HMU and/or that the classification should take
into account the HMU classifications of the neighboring cells when
defining an HMU in any given cell.
The HMU classification was dependent on the surface gradient of the
river. We used the 4% threshold of Borsányi (2006), but expert opinion
has suggested that this threshold may be too high for a river reach
close to the estuary, such as the Nidelva (Borsányi 2006). The HMU
classification did not differentiate between turbulent conditions that
were generated locally (standing waves) and the advective diffusion of
air bubbles/foam generated upstream. This resulted in inaccurate
predictions of hydromorphology in the Orkla in locations downstream of
cascades or rapids. These were typically pool habitats, a mesohabitat
noted for low turbulence (Stone & Hotchkiss 2007), which were
incorrectly assigned a mild-turbulent HMU. A refined surface
pattern classification based on three classes correctly classified these
areas as diffusing foam , suggesting that the HMU classification
needs further refinement.
Training and validation of the
CNN
Key to ensuring an effective hydromorphological classification is using
a suitable dataset for training and validating the CNN. The HMU
classification system used two very dissimilar classes – smooth
or rippled versus standing waves – so it was easy to train the
CNN such that it could distinguish between these patterns for predicting
on new datasets. Additionally, it was easy to create training classes to
differentiate between standing waves and diffusing foam .
Differentiating between further unsmoothed conditions (e.g. between
broken and unbroken standing waves) requires careful selection of sample
classes. This may be difficult because the optical properties of the
phenomena may overlap, depending on both local flow conditions and how
the imagery is acquired. Training and validation therefore require
selection of class examples over a sufficiently broad range of
conditions for effective prediction in other river stretches.
The CNN-based approach for identifying flow features requires
high-resolution aerial photographs of the water surface that are not
adversely influenced by sub-optimal light conditions, both for training
and validating the CNN, and predicting on new datasets. Firstly, the
approach may be less effective for identifying mesohabitats when shadows
fall across the water surface, making surface patterns less visually
identifiable (darker, “noisier” imagery). The approach will therefore
be less effective for narrow watercourse headwaters in steep, tree-lined
valleys where the entire channel may be obscured by shadow (see Hedger
et al. 2022). Secondly, the optical properties of areas in direct
sunlight depend on the interaction between the water surface and the
solar position (Mount 2005, Zeng et al. 2017). Surface undulations may
be more apparent under direct rather than diffuse sunlight, and specular
reflection from unbroken stranding waves in imagery taken under direct
sunlight could appear more like the white water inherent in broken
standing waves. Therefore, the relative frequency of the classification
type will depend on the light environment. Thirdly, in shallow areas, it
is possible that the texture on the riverbed from coarse substrates
could be confused with surface patterns.
Further development
The method proposed here provides a system by which river surface
pattern recognition may be applied to remotely sensed imagery within a
decision system to obtain an automated map of river hydromorphology.
Improved application of the remote sensing data may provide a more
refined classification: specifically, better characterization of flow
features via a refined surface pattern classification, and the use of
additional hydromorphological information to refine the habitat
classification.
Refining the surface pattern classification. The HMU
classification system only categorized surface patterns into two
classes: (1) smooth or rippled and (2) standing waves . We
have shown here that there is potential to distinguish between standing
waves and the advective diffusion of air bubbles/foam. There is,
however, also potential to differentiate between unbroken and broken
standing waves. Unbroken standing waves might have alternating bright
pixels, associated with reflections on the side of the wave facing the
sun, and darker pixels facing away from the sun. Broken standing waves
will have more white water distributed randomly in the cell. The channel
characteristics influence hydraulics, so more detailed information on
the forms of surface flows might be used in further defining the
hydromorphology. For example, unbroken standing waves are more
associated with riffles whereas broken standing waves are more
associated with rapids and cascades (Newson & Newson 2000).
Refining HMU classes. The full classification system on which
the current work is based (Borsányi et al. 2004) uses depth and velocity
to further refine the mesohabitat class: for example, cascades are
defined by shallower depths than rapids. Our HMU classification system
used class divisions that were only based on surface patterns and
gradient, so only provided four broad HMU types. However, extraction of
depth via remote sensing is possible, using bathymetric LiDAR (Hugue et
al. 2016, Puig-Mengual et al. 2021, Sundt et al. 2022), or analysis of
image spectra (Legleiter & Harrison 2019, Sundt et al. 2021), or
Structure from Motion applied to UAV imagery (Dietrich 2017). Velocity
may be determined using large-scale particle image velocimetry, applied
to UAV imagery (Detert & Weitbrecht 2015). There may also be potential
for extraction of depth and velocity using a CNN approach applied to the
imagery of the type used in this analysis, based on direct properties
within the imagery (e.g. the darkness of an area indicating depth, or
foam patterns indicating flow speed). Refining the HMU classes with
information obtained directly from the imagery will give a more detailed
classification of hydromorphology.