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in light conditions (as a consequence of the weather, time
of day and time of year), reflections from the water
surface and the presence of shadows, especially when
the sun is close to the horizon. These may hinder an
efficient, unsupervised procedure of object recognition,
thus requiring time consuming work by a skilled operator.
Information obtained from close range photographs
may also be used to complement other field measurements
or to validate/calibrate numerical models. Time-lapse
cameras, for example, can provide valuable information
on flow levels that can then be used to inform boundary
conditions for numerical modelling or to validate flow
reconstruction at different discharge conditions.
appropriate, stratified protocol (e.g. Wolcott and Church,
1991). To date, image-based sampling procedures have
sought to replicate the approach of conventional sampling
procedures, focusing on the generation of conventional
grain-size percentile measures. However, one of the key
benefits of image-based methods is that they facilitate the
assessment of continuously varying trends in grain size at
a variety of spatial scales. They therefore enable exami-
nation of scale-dependent variability in surface grain-size
properties in a way that has not been achievable previously
(Figure 15.6).
The morphometry of particles, which is controlled
by petrography and abrasion, can also provide valuable
information about sediment sources and particle
maturity (a proxy for travel distance). Traditional
methods of acquiring shape information are intensely
time-consuming so it is encouraging that photographic
methods are being developed to capture useful data. It
can be used to evaluate the contribution of tributaries or
adjacent hillslopes to the river bedload transport when
an appropriate sampling scheme is adopted (Kuenen,
1956; Diepenbroek et al., 1992). This parameter is less
sensitive to local hydraulic conditions than grain size
(used for example with the same purpose by Rice, 1998
and Surian, 2002) and can be used in contexts where
grain size is strongly disrupted by human activities.
The value of recent developments by Roussillon et al.
(2009) was illustrated by applying them to bed sedi-
ments along the River Progo in Indonesia, over a reach
of 140 km from the source to the ocean. The different
roundness indicators did not show a simple longitudi-
nal trend (Figure 15.7) but highlighted the location of
the most significant changes in particle morphometry. In
particular, a significant increase in angularity is observed
between km 60 and 80, demonstrating local inputs of
fresh and angular material from tributaries draining a
recently active volcano.
This approach can be accurate for a given lithology
where resistance to abrasion and the relation between
distance travelled and roundness is consistent. In more
complex lithological environments, it is necessary to
distinguish the type of rocks before applying such mor-
phometric measures. A combination of grain size, petro-
graphic and particle morphometric information can then
provide the most robust indication of particle sources
along a river continuum. Some tests have been per-
formed on pebbles collected on the Ouveze River, a left
side tributary of the middle Rh one River, France, char-
acterised by a complex lithological basin with basalt,
limestone, sandstone schist, and granite source areas
15.4 Application of vertical and oblique
close-range imagery to monitor bed
features and fluvial processes at
different spatial and temporal
scales
15.4.1 Vertical ground imagery for characterising
grainsize, clastmorphometryand
petrographyofparticles
The physical properties of the channel substrate provide
a fundamental control on bed stability/mobility, aquatic
and riparian habitat availability and define the skin resis-
tance to flow and the character of the boundary layer. A
variety of substrate properties have been examined using
imaging techniques, although these are inherently limited
to examining the nature of the surface layer. The most
widely studied substrate property is the grain-size distri-
bution of the sediment, although this is often used as a
proxy for roughness. More recently, attempts have been
made to measure roughness directly.
Ground-based imagery has been used to examine spa-
tial variations in bed material grain size at large and
small scales. For example, Rice and Church (1998)
used ground-based photography (alongside conventional
sampling) to examine downstream fining along approx-
imately 230 km of channel in the Pine-Sukunka River
system of British Columbia. At such scales it is neces-
sary to manage local-scale variability; for example, by
consistently sampling the head of bars. At smaller scales,
for example across individual bars, conventional sam-
pling techniques, like Wolman sampling, do not detect
within-site variations in grain size that may be important
at that scale, unless great care is taken to first iden-
tify relevant structures and accommodate them using an
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