Geography Reference
In-Depth Information
Since the 1980s the use of remotely sensed optical
imagery for channel change mapping has exploded,
largely because of the widespread availability of Geo-
graphic Information Systems (GIS). GIS made it much
easier to digitise river features from aerial or satellite
imagery, georectify and reproject the features to a com-
mon coordinate system, conduct a wide variety of spatial
and change-over-time analyses, and produce maps from
the results. The techniques for mapping river features
with remote imagery are standard to any GIS or remote
sensing software, and many college students now have the
skills necessary to carry out the work.
The ease of mapping with GIS, however, has led to a
certain degree of complacency regarding the results. Too
often change detection mapped from optical imagery is
considered to be 'real', when in fact the change may be
the result of cumulative errors. Scale distortions in the
original imagery, poor selection of ground control points
used to georeference the image, and the algorithms used
to transform the image to a specific coordinate system
all generate errors. These errors can be substantial and
are compounded when attempting to detect change over
time (Hughes et al., 2006). Simply put, just because it
looks good does not mean that it is good. Analyses of
channel change should include error assessment and use
of stable control points.
Error of this nature can be significantly reduced using
orthorectification techniques, which correct for planform
distortion caused by topography and sensor geometry.
The orthorectification techniques, however, are more
costly and complex. They require additional data layers
on elevation, use more expensive software, need more
time to set up, and call for more specialised personnel.
In addition to GIS, an exciting advance in optical
remote sensing of rivers is the three dimensional map-
ping of channels and channel change (this can also be
done with active sensors, as is discussed in elsewhere in
this volume). Previously, mapping elevation changes in
channels has been a challenge because it requires high
accuracy to capture channel bed elevation changes that
are often subtle (Brasington et al., 2003). It is also requires
measuring elevations of both the submerged and emer-
gent parts of the channel throughout the entire active
channel.
Several approaches have been used to solve these
problems with optical imagery. Westaway et al. (2000,
2001) modified classical photogrammetric techniques to
account for the effects of refraction. In addition, West-
away et al. (2003) developed an alternative approach
that uses classical photogrammetry to derive topographic
information for exposed surfaces and a correlation-based
method of estimating water depth within the wetted
channel from the image data.
More recently, Lane et al. (2010) extended these tech-
niques so that they can be used with historical imagery.
In their procedure, overlapping images and photogram-
metric principles are used to derive local elevations inside
and outside the channel. A filter then identifies which
photogrammetrically derived water elevations are likely
to be valid based on the certainty with which correspond-
ing points can be identified on the two photos. The valid
water depths are used to develop a relation between image
brightness and depth that is applied to the rest of the chan-
nel to map depths. These depths in turn can be subtracted
from the elevation at the dry surface of the channel mar-
gin to determine bed elevation. Lane et al. found that the
method provided accurate depth estimates and could be
used to detect vertical changes of 0.40 m or more with
a 67% confidence of change (Table 2.1). Although more
complex than this brief description would suggest, the
Lane et al. methodology should ultimately be applicable
anywhere: (1) tie points can be identified on historical
imagery that can be surveyed in the present day; (2) the
image scale is sufficiently fine to detect depth changes;
and (3) the imagery of the submerged areas displays a
range of brightness that can be correlated with depth.
Marcus (2012) provides more in-depth summaries of the
techniques developed by Westaway et al. and Lane et al.
As with channel change in the horizontal dimension,
it is critical to consider error when estimating changes in
the vertical dimension. For example, the 67% confidence
of correctly detecting a vertical change of 0.40 m in the
system examined by Lane et al. (2010) is reassuring at
one level, but also a sobering reminder that vertical
changes typical of many small streams will be missed
or inaccurately portrayed using the photogrammetric
approach. Brasington et al. (2003) and Lane et al. (2003)
provide guidelines on how to evaluate vertical error and
its implications for detection of vertical change. Likewise,
one can determine the potential range of depths that can
be mapped and the precision of bathymetric mapping
techniques by modeling the optical physics under varying
stream conditions (e.g. Legleiter et al., 2009).
2.6 Turbidity and suspended sediment
Turbidity, in its formal optical definition, refers to the
amount of attenuation and back scattering of light due to
suspended solids and dissolved load (Davies-Colley and
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