Ground based LiDAR and its Application to the Characterisation of Fluvial Forms - Fluvial Remote Sensing for Science and Management - page 320

D 16 D 50 D 84

− 60

0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

− 80
− 100

− 120

− 140
− 160
− 180
−

Bar head

Chute
channel

200

(a) (b) (c)

Figure 14.6 Point bar sedimentology (D 16 ,D 50 and D 84 ) and sub-bar morphology mapped using terrestrial laser scanner on the
River Coquet, Northumberland. Reproduced from Entwistle NS and Fuller IC (2009) Terrestrial laser scanning to derive the surface
grain size facies character of gravel bars. In: GL Heritage and ARG Large (eds) Laser Scanning for the Environmental Sciences, with
permission fromWiley-Blackwell.

laser energy. The extremely dense sampling of the water
surface using terrestrial laser scanning coupled with the
improved sensitivity of return pulse sensors allows data
to be collected to accurately characterise water surface
roughness (a primary determinant of biotope classifica-
tion in the field).
The study site used by Large andHeritage (2007) on the
South Tyne at Slaggyford is an upland cobble bed channel
characterised by diverse bed morphology. The water
surface data were initially transformed into a regular
grid with 0.02m spacing and this was subsequently used
to determine the local standard deviation of sub areas
across the surface. Figure 14.7 quantifies the biotope
distribution at the site defined using the definition of
Newson and Newson (2000), the most commonly used
typology in the UK. It is clear that, at low flows, biotope
unit variety is high and distribution complex, and it is
argued by the authors that such complexity would be
missed using observational techniques along the lines
of Newson and Newson (2000) or that of the European
Aquatic Monitoring Network (EAMN, 2004) as these
techniques sub-divide reaches into larger components
(at the scale of several square metres), whereas the
laser scanning techniques gets down to the scale of
water surface or bedform roughness - the scale of most
relevance to the communities inhabiting these habitats.
Results obtained with TLS showed overlap between
the riffle and run habitat types. This is to be expected, as
stage rises and fall, these habitat types merge and change
from one type to another. An implication from the point
of view of instream hydraulics may be that these biotopes
are providing very similar habitat to each other. The other
feature apparent from Figure 14.7 is the inclusion of edge

areas in the biotope definition. This is intuitive, but the
advance here is that using terrestrial laser scanning allows
much better quantification of this critical in-channel
component (edges per se have long been recognised
as being of heightened importance from an ecological
point-of-view).

14.2.5 Micro-topographic roughnessunits

A reach-based study of Kingsdale Beck in the Yorkshire
Dales National Park (Entwistle and Fuller, 2009) used
terrestrial laser scanner data to identify and map micro-
topographic roughness element distribution across the
bed of the river. The channel flows across limestone geol-
ogy resulting in prolonged no-flow periods where the
entire bed is exposed for scanning (an unusual situation
nationally, but ideal for TLS studies of benthic environ-
ments!). The bed is composed of gravels and cobbles
(D 50 = 0.14m), and bedform clusters are

typically of the order of 0.3m wide and in excess of 0.5m
long. Due to the excellent exposure, the merged data file
of the 90m survey reach contained in excess of 20 mil-
lion points with little occlusion. Topographic highs were
isolated from the data set and plotted (Figure 14.8). The
study was able to confirm the observations of Brayshaw
(1984) that the location of micro-topographic roughness
elements are strongly linked to the distribution of very
large individual clasts (D 99 and above).

0.09m, D 84 =

14.2.6 Thebarunit scale
A study by Milan et al. (2007) concentrated on a 5881m 2
reach of braided gravel-bed channel, towards the tail of