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bounced return signal of the repeat-pass L-HH-band
Shuttle Imaging Radar (SIR-C). L-band penetrates the
vegetation canopy and follows a double bounce path
that includes the water and tree trunk surfaces, with
both amplitude and phase coherence stronger than sur-
rounding non-flooded terrain, permitting determination
of the interferometric phase. Alsdorf (2003) also used
these characteristics and found that decreases in water
levels were correlated with increased flow-path distances
between main channel and floodplain water bodies that
could be modelled in a GIS. This correlation function
allowed changes in water storage to be mapped over time.
Most of the studies on water level changes with InSAR
technology successfully mapped relative changes; however
it is often information on absolute water elevations that
hydrologists and water resources managers need and
so the static nature of InSAR observations reveals only
limited information describing the dynamic nature of
(wetland) water flow. In an attempt to overcome some
of this limitation, Hong et al. (2010) have used highly
coherent interferometric phases obtained with short time
difference between two SAR acquisitions. Their technique
transforms relative wetland InSAR observations to an
absolute frame using calibration to gauge stations and
generates both detailed maps of water levels and water
level time series for 50 m pixels with an RMSE of 6-7 cm
including 3-4 cm InSAR water level detection error. Such
products could prove very useful to understand wetland
surface flow patterns and allow efficient management of
wetlands (Hong et al., 2010).
pose to human lives, the environment and economic
activity. The Directive requires Member States to first
carry out a preliminary assessment by 2011 to identify the
river basins and associated coastal areas at risk of flooding.
For such zones they would then need to draw up flood
risk maps by 2013 and establish flood risk management
plans focused on prevention, protection and preparedness
by 2015.
In addition, a near real-time flood extent could be
used in conjunction with a hydraulic model of river
flood flow to help predict future flood extent. The flood
waterline from the image could be intersected with a
LiDAR DEM to obtain water surface elevations along the
waterline, and these could be assimilated into the model
run, correcting the water surface elevations predicted by
the model where necessary (see for example Neal et al.,
2009). This would help to keep the model 'on track'
so that the model's prediction of future flood extent
could be viewed with more confidence. A near real-
time flood detection algorithm, developed on a blueprint
of those discussed in Section 6.3.1.1, giving a synoptic
overview of the extent of flooding in both urban and
rural areas, and capable of working during night-time
and day-time even if cloud was present, could thus be a
useful tool for operational flood relief management and
flood forecasting.
6.3.1 Mappingriverflood inundationfromspace
Before engaging with any process involving satellite
remote sensing and flood management it is crucial to con-
sider end user requirements and the appropriate timeline
as well as the spatial resolution of the delivered prod-
ucts. Figure 6.3 shows the requirement in terms of spatial
resolution and turnaround time for specific flood man-
agement deliverables. For instance, flood mapping for
emergency management can be done at any spatial res-
olution really but should be made available to the end
user within 48 hours whereas for insurance assessment
quite the opposite situation might apply; spatial resolu-
tions finer than 10 m are generally required but timeliness
might be less important.
6.3 The use of SAR imagery to map
and monitor river flooding
As a response to the summer 2007 floods, the UK Govern-
ment set up the Pitt Commission to consider what lessons
could be learned from those flood events (Pitt, 2008)
Among its many recommendations, the Commission
highlighted the need to have real-time or near real-time
flood visualisation tools available to enable emergency
responders to react to and manage fast-moving events,
and to target their limited resources at the highest-priority
areas. It was felt that a simple GIS that could be effec-
tively updated with timings, level and extent of flooding
during a flood event would be a useful system to keep
the emergency services informed. In a similar context
but outside near real-time management, the European
Floods Directive (2007/60/EC, European Commission,
2007) aims to reduce and manage the risks that floods
6.3.1.1 Operational flood detection
Near-real time flood detection is of course desirable for
many obvious reasons and research efforts are continu-
ously invested in developing algorithms that operate in
near-real time (e.g. the ESA GPOD FAIRE system). By
placing data analysis and decision making capabilities
onboard a spacecraft, the time it takes to detect and react
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