Environmental Engineering Reference
In-Depth Information
Table 11.1
Summary of merits and limitations of available Digital Terrain Modelling (DTM) techniques (after Smith
et al. (2006)
Methods
Merits
Limitations
Cartography
Simple to generate if digital contours are
available
Highly dependent on the scale and quality of the base map
Does not accurately characterize low-lying areas such as
Economical for large areas
oodplains
In
uenced by the skill of operator digitizing the map
Ground surveying
Extremely accurate
Expensive and time consuming to collect for large areas
Total Stations can acquire elevations under
canopy
GPS does not provide reliable heights under canopy
Access required to property for measurement of heights
Provides measurements for
lling in voids in
other datasets
Digital aerial
photogrammetry
Proven and well-understood approach
Delay between acquisition of images and production of DTM
Potential for high accuracies in plan and height
Dependent on scale and quality of imagery
Provides an optical image for interpretation
Limitations in the automatic matching algorithm
Relatively economical for surveys of large areas
Manual measurements require an experienced
observer
Interferometric
Synthetic Aperture
Radar (SAR)
Can 'see' through clouds and operate day or
night
Volume scattering in vegetated areas leads to poor coherence
Performance can degrade in urban areas due to bright targets
and shadows
Artefacts in the DTM due to topography or atmospheric
propagation
Rapidly maps very large areas
LIDAR
Potential for high accuracy
May require a lot of
ying time for extremely large areas
Can generate DTM for surface with little or no
texture
Cannot operate in cloudy, rainy or windy conditions
May require complementary data, such as photo, if interpretation
of points is necessary
Could measure vegetation height when set to
record
rst and last pulse
DTM, digital terrain model; GPS, global positional system; LIDAR, Light Detection and Ranging.
LIDAR data manually using an image processing
package, and the resulting gaps interpolated, prior
to DSM filtering.
Atkinson (2007) estimated friction coefficients
from floodplain land cover classification of Land-
sat Thematic Mapper (TM) imagery, and found
that spatially distributed friction had an effect on
the timing of flood inundation, though less effect
on the predicted inundation extent.
Data from LIDAR may also be used for friction
measurement. Most LIDAR DSM vegetation re-
moval software ignores short vegetation less than
1m or so high. However, even in an urban flood-
plain, a significant proportion of the land surface
may be covered with this type of vegetation, and
for floodplains experiencing relatively shallow
inundation the resistance due to vegetation may
dominate the boundary friction term. Mason
et al. (2003) extended LIDAR vegetation height
measurement to short vegetation using local
Floodplain friction measurement
Remotely sensed data may be used to generate
spatially distributed floodplain friction coeffi-
cients for use in 2-D inundation modelling. A
standard method is to use two separate global
static coefficients, one for the channel and the
other for the floodplain, and to calibrate these by
minimizing the difference between the observed
and predicted flood extents. The remote-sensing
approach has the advantage that it makes
unnecessary the non-physical fitting of a global
floodplain friction coefficient. Wilson and
