Geography Reference
In-Depth Information
At the smallest scale depicted in Figure 14.1, LiDAR
data has been acquired and processed at densities suffi-
cient to represent the surface at the grain scale (gravels
range between 2mm and 64mm). The data point con-
centration at this scale has been estimated at between
4000 and 10 000 points m 2 byworkerssuchasLane
et al. (1994). Studies at the sub-bar scale, identified as
a significantly under-researched area by Charlton et al.
(2003), have quantified morphological change over rela-
tively short timescales showing the advantages such rapid
data acquisition canprovide in terms ofmonitoring short-
term system alteration at this spatial scale. Monitoring
of morphologic change at the reach scale and beyond
requires the merging of separate scans and several studies
employing differing methodologies are reported in this
chapter to show the range of approaches that can be
effectively used in river systems. In all cases we emphasise
that, despite the obvious advantages of terrestrial laser
scanning, great care needs to be exercised during data
collection and processing in order to ensure that data
accuracy is maximised. Table 14.1 provides a summary of
the typical errors that are likely at various scanning scales
and these should be considered carefully when designing
a scan campaign. This chapter concludes with advice on
survey protocols required in the light of a review of work
by the authors and other workers that will help to min-
imise the potential errors listed in Table 14.1. Despite the
seemingly endless potential of terrestrial LiDAR a word
of caution is issued with regard to the indiscriminate use
of laser-based scanning in fluvial systems.
Table 14.1 Potential error linked to terrestrial laser
scanning.
Scale
Error
Sub-grain
Short distances - pulsed scanners
unable to differentiate signal returns
at very close range.
Grain/micro-
topographic
Mixed pixel - issues with the size of the
laser footprint and unrealistic areal
averaging
Bar
Registration - error in tie-point
recognition and relative positioning
can affect point cloud registration.
Scan angle - affects spread of laser
footprint linked to unrealistic areal
averaging
Reach
Reflectivity - more reflective surfaces
can saturate the instrument sensor
reducing the estimation of distance.
Atmospheric conditions - aerosols will
impact on scanner range.
Registration - error in tie-point
recognition and relative positioning
can affect point cloud registration.
Scan angle - affects spread of laser
footprint linked to unrealistic areal
averaging
Landscape
More suited at present to airborne
systems but may suffer the same
errors as for reach scale, particularly
registration.
14.2 Scales of application in studies
of river systems
selective transport during low transport rates. Elsewhere,
Michael and Gerhard (2006) have used a laser scanning
system to examine benthic-relief to study the interaction
of organisms with the surrounding flow regime, while
Cui et al. (2008) have used lasers to investigate and model
sediment transport in a flume with forced pool-riffle
morphology.
14.2.1 Thesub-grainscale
Madej et al. (2009) have used flume-based lasers with
millimetric accuracy to model channel responses to vary-
ing sediment input. They concluded that such use of laser
scanning in flume conditions can help confirm inter-
pretations of observations that are limited by challenges
of field conditions. It can also facilitate exploration of
in-channel processes in more detail than would be the
case in the real-life situation. Madej et al. (2009) observed
channel response to varying sediment loads and flume
patterns which included (i) overall channel form and
degree of armouring; (ii) bed-surface fining and bed-
form smoothing during aggradation, and coarsening and
bedform roughening during degradation and (iii) greater
14.2.2 Thegrainscale
At the grain scale, water flow level in river channels is
moderated by the interaction with the roughness of the
surface over which it flows. Milan et al. (2010) note that
this interaction is highly complex and remains poorly
understood due primarily to sample size and sample
bias issues over extremely heterogeneous surfaces. They
demonstrated the utility of oblique laser scan data in
 
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