Geography Reference
In-Depth Information
Mobility sediment reloaded
Water depth (cm)
0
Area reload
−
210
Variation (cm)
> 50
0 to 50
0 to
−
50
<
−
50
Artefact
N
WE
S
0
50
100 Meters
Topographic variation
between 2005 / 2006
Bathymetric maps (from models)
Aerial images
Methodology progression
(c)
Figure 8.19
(
continued
).
(i) non-uniform illumination conditions on the image
database used and (ii) the presence of different sub-
strate types on the monitoring site (submerged aquatic
vegetation, limestone outcrops and different structures
of grain size inside the water channel) which led to
errors in bathymetric models. The results clearly show
significant morphological change. We observed a migra-
tion of sediment from the reintroduction area and an
accumulation on a downstream reach mobilised by sev-
eral annual floods between the two surveys. A sediment
wave 380 meters long was detected on the downstream
reach from the reintroduction site. The resulting sediment
budget presents an excess of sediment of 7007 m
3
(Lejot
et al., 2011). Such an approach therefore provides detailed
quantitative information to river managers in order to
demonstrate the success of a restoration strategy. In this
case, rarely accessible data on sediment local transport
as a result of gravel addition has been made available.
Furthermore the hyperspatial data has proved invaluable
for fish ecologists who are surveying the effects of such
measures on fish distribution and fish habitat.
studied. In this chapter, we have attempted to deliver
a very pragmatic overview of the new data acquisition
options that are now available to river scientists and man-
agers. We hope to have shown that hyperspatial image
acquisition methods are now well within reach of most
projects. With the proliferation of image platforms and
affordable cameras, even low-budget endeavours should
be able to acquire hyperspatial imagery over small reaches
whilst at moderate budgets, catchment scale hyperspa-
tial data acquisition is now well within the reach of
river scientists and managers. Furthermore, many newly
available UAV and ULAV platforms allow for a 'do-it-
yourself' approach which is ideally suited to repeat image
collection. This potential improvement in the temporal
resolution of hyperspatial images is likely to be a key factor
in their expanding usage since it allows for monitoring
at monthly or even daily timescales. Such monitoring
methods with both high temporal and spatial resolutions
are crucially needed in river sciences and monitoring.
However, it should be noted that whilst hyperspatial
imagery can now allow for automated measurements of
many important habitat parameters such as grain size,
depth and riparian vegetation types, the need for field-
based sampling persists. Indeed, the analysis methods
cited in this chapter all have their limitations and readers
are greatly cautioned from treating this new data source as
8.5 Conclusion
Hyperspatial imagery has the potential to dramatically
change the way in which small rivers are managed and
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