INTRODUCTION
Categorizing fluvial habitat is an essential step for supporting river
habitat management and conservation programs. Indeed, the physical
habitat composition in a river or stream and the corresponding hydraulic
parameters are considered to be basic elements to river health
assessment (Maddock 1999). A range of approaches with associated
terminology – river landform , morphological unit ,mesohabitat type , hydromorphological unit (HMU) ,physical/hydraulic biotope , ecotope, channel geomorphic
unit (CGU) (see Belletti et al. 2017) – have been developed for
categorizing fluvial habitat, reflecting differences in research focus,
rationales, and the scale at which the research is conducted. The term
“mesohabitat” (e.g. Tickner et al. 2000) applies to a locally
contiguous area (typically less than several 100 m in length) consisting
of similar hydromorphological conditions, such as flow velocity,
turbulence and depth (see Wegscheider et al. 2020). Pardo and Armitage
(1997) characterize mesohabitats as being “visually distinct units of
habitat within the stream, recognizable from the bank and with an
apparent physical uniformity”. Mesohabitats are often associated with
particular depth-velocity conditions (Kemp et al. 1999) and it is these
interactions between flow and physical habitat characteristics that
create the variety of mesohabitats. In particular, surface flow type is
considered a major descriptor of physical habitats. The composition and
particular assemblage of mesohabitats in a river is an indicator of
conditions for fish, macroinvertebrates, and other river biota. Fausch
et al. (2002) advocated for the pertinence of mesohabitat
characterization for the study of fish ecology, arguing that features
relevant to fish movement and behavior, as well as barriers and
obstacles, were best assessed at this scale. Mesohabitats are usually
defined as distinct classes, such as riffle, glide, pool or run, to cite
a few (see for example Parasiewicz 2007). There is some inconsistency in
how mesohabitat classes are defined (Newson & Newson 2000), but
typically, these classes
compartmentalize a continuum of overall hydromorphological conditions,
from slower and more laminar flows in low gradient areas (e.g. glides),
to faster and more turbulent flows in higher gradient areas (e.g.
rapids). The term hydromorphological unit is often used as a
synonym for mesohabitat (Alcaraz-Hernandez et al. 2011, Suska &
Parasiewicz 2020), but in the current study we define this as a broad
class, based on the flow features and gradient of the water surface.
A range of methods for classifying fluvial hydromorphology and
mesohabitats exist (see Harby et al. 2004 for a first summary and
overview), ranging from simple field-based qualitative assessment
[e.g. the River Habitat Survey method (Newson et al. 1998); theNorwegian Mesohabitat Classification method, (Borsányi et al.
2004)] to numerical modelling approaches [e.g. MesoCASiMir(Eisner et al. 2005, 2007), MesoHabsim (Parasiewicz 2007)].
Field-based mesohabitat classification may simply involve observing
features such as surface flow type from the riverbank and inferring the
habitat types from these. For example, in Norway, Borsányi (2004)
developed a system based on surface flow features or types, surface
gradient, flow velocity and depth, all of which can be visually assessed
in the field. Such a system requires no specialized instrumentation and
can be done using bankside observations, possibly supplemented with
qualitative interpretation of aerial photographs. The diversity of
methods for identification of mesohabitats in the field has four major
difficulties in common: 1. They require some experience to provide
consistent and robust results; 2. Researcher variability may lead to the
same mesohabitat being characterized differently depending on the
surveyor (e.g. riffle vs shallow run); 3. The same mesohabitat type may
be identified differently depending on the method used, and similar
terms are used by different methods to identify different features; 4.
They can be time-consuming depending on the method used and length of
river to be surveyed. Numerical modelling approaches, for instance
hydraulic modeling, may remove some of the subjectivity but can be very
time-consuming, both in terms of obtaining data for model validation and
for setting-up the model.
Remote sensing, typically based on airborne or satellite true color or
multispectral imagery and/or LiDAR data, offers the advantage of
providing synoptic coverage of the river at a range of spatial scales
pertinent to the mesohabitat and over larger distances. Remote sensing
is particularly useful as a source of empirical data for numerical
models. For instance, channel bathymetry may be derived from a range of
methods (Sundt et al. 2022), and such data can then be used to derive
flow properties and other habitat metrics, either by empirical-based
hydraulic rules (Hugue et al. 2016) or by hydraulic models (Hauer et al.
2009, Sundt et al. 2022). For example, Hauer et al. (2009) used a
rule-based system to classify a watercourse into distinct mesohabitat
types (riffles, fast runs, runs, pools, shallows, backwaters) based on
predictions from a hydraulic model calibrated using LiDAR data.
Drawbacks and limitations to this approach are that establishing
hydraulic properties is time-consuming and requires specific expertise.
It may also not always be possible to use remote sensing to obtain depth
estimates, for instance if the river bottom is not visible in aerial
images and/or LiDAR data are unavailable.
Remote sensing may be used to directly determine hydromorphology and
mesohabitat because properties of the river’s water surface may provide
information on its hydromorphology. For example, turbulent flows, shown
by broken or unbroken standing waves on the surface, may indicate the
presence of steep channels with coarse bed material (associated with
rapids or cascade mesohabitats), whereas a smooth or rippled water
surface is more indicative of laminar flows (associated with glide or
run mesohabitats). While surface water patterns have been incorporated
into mesohabitat classification schemes (e.g. Borsányi et al. 2004),
they have not been fully exploited in remote sensing-based mesohabitat
mapping. The advantage of deriving these properties directly from remote
sensing is that it is possible to retain the primary advantage of remote
sensing (synoptic coverage) without the requirement to estimate
bathymetry and/or introduce subsequent modelling (e.g. hydrodynamic
modelling). Ideally, the remote sensing approach should be automated to
reduce subjectivity and effort.
Automated extraction of information on river habitats from remote
sensing imagery is difficult due to a large number of confounding
factors (Hedger et al. 2022). Artificial intelligence techniques offer
the potential to deal with the complexity existing in imagery of river
habitats, and have been used to classify surface cover type (Carbonneau
et al. 2020), river sediment sizes (Takechi et al. 2021),
hydromorphological features (Casado et al. 2015), and salmon redds
(Harrison et al. 2020). There is potential for such an approach to be
used to classify features of the water surface (e.g. identifying whether
the surface is smooth or whether the surface has standing waves). Flow
features identified in such a manner can then be combined with
information on surface gradient (readily obtainable from digital terrain
model, DTM, or digital surface model, DSM, data) for a refined
hydromorphological classification. Convolutional neural networks (CNNs)
are particularly appropriate for this type of work. These are deep
learning algorithms that can be trained on images to assign importance
(learnable weights and biases) to features of the image within a network
linking the images to a predefined classification. Such a network can
then be used to predict class types on new images.
Here, we evaluate the potential for automated river classification into
hydromorphological units (HMUs) based on airborne remote sensing data.
We use a classification system that requires information on only (1)
surface patterns, identified by applying a CNN to aerial photographs,
and (2) surface gradient, identified from NIR (topographic)
LiDAR-derived DTMs, to classify areas into one of four broad HMU
classes. Following Borsányi’s rule system, we begin with a simple
surface pattern classification (smooth or rippled versusstanding waves ), but we also investigate a more detailed surface
pattern classification that allows the distinguishment between standing
waves (generated locally) and air bubbles/foam (generated upstream and
being advected downstream).