Environmental Engineering Reference
In-Depth Information
suitable for the quality of floodmodelling current-
ly being undertaken. Smith et al. (2006) point out
that recently in the UK it is largely the LIDAR and
swath bathymetry data collected by the EA, and
the InSAR data collected by InterMap that have
been used to produceDTMs for floodmodelling. In
parts of the world where LIDAR data are not
available, floods are larger than in the UK, or
modelling requirements are less stringent, other
data sources (e.g. SRTM data) have been used
(Wilson et al. 2007). However, the discussion be-
low focuses on DTMs produced using LIDAR.
A substantial amount of these data have and are
being collected by the Environment Agency of
England and Wales (EA). Flights are typically car-
ried out during leaf-off periods with the system set
to record the last returned pulse. The EA provides
quality control by comparing the LIDAR heights
on flat unvegetated surfaces with GPS observa-
tions, and can achieve discrepancies of less than
10 cm (EA2005). However, note that DTMerrors
generally increase in regions of dense vegetation
and/or steep slope, and can be especially signifi-
cant at the boundaries between river channels and
floodplains.
Filtering algorithms for LIDAR data
Sonar bathymetry
Considerable processing is necessary to extract the
DTM from the raw DSM. The basic problem in
LIDAR post-processing is how to separate ground
hits fromhits on surface objects such as vegetation
or buildings. Ground hits can be used to construct
a DTM of the underlying ground surface, while
surface object hits, taken in conjunction with
ground hits, allowobject heights to be determined.
Many schemes have been developed to perform
LIDAR post-processing. Most of these are con-
cernedwith the detection and recognition of build-
ings in urban areas (Maas and Vosselman 1999;
Oude Elberink and Maas 2000), or the measure-
ment of tree heights (Naesset 1997; Magnussen
and Boudewyn 1998). Commercial software is also
available for the removal of surface features.
Gomes Pereira and Wicherson (1999) generated
a DEM from dense LIDAR data for use in hydro-
dynamic modelling, after the removal of surface
features by the data supplier. Another example is
the system developed by the EA, which uses
a combination of edge detection and the commer-
cial TERRASCAN software to convert the DSMto
a DTM (A. Duncan, personal communication).
The system has been designed with flood model-
ling in mind, and, as well as the DTM, also pro-
duces other datasets for use in the subsequent
modelling process, including buildings, taller
vegetation (trees, hedges) and embankments. An
example of the EA's hybrid filtering process is
shown in Figure 11.2. False blockages to flow
such as bridges and flyovers are removed from the
Methods of estimating river channel topography
usually involve generating a series of height cross-
sections along the channel using ground surveying
techniques, then interpolating between the cross-
sections. With the advent of more sophisticated
modelling, there is a need for better estimates of
channel topography, and one technique involves
bathymetric measurement using sonar. This uses
a vessel-mounted transducer to emit a pulse of
sound towards the river bed and measure the
elapsed time before the reflection is received. The
depth of water under the vessel can be estimated
knowing the velocity of sound in water. In the
UK, the EA operates a wide-swath sonar bathym-
etry system designed to make it straightforward
to merge bathymetry of the channel with
LIDAR heights on the adjacent floodplain (Horritt
et al. 2006).
Suitability of DTM generation techniques
for flood modelling
The suitability of a DTMgeneration technique for
flood modelling is largely governed by the height-
ing accuracy and level of spatial detail that can be
captured. Table 11.1 gives a summary of the main
merits and limitations of available DTM genera-
tion techniques, and Table 11.2 summarizes the
main characteristics of the DTMs that are gener-
ally available in the UK. Many of the techniques
described inTable 11.1 produceDTMs that are not
