Environmental Engineering Reference
In-Depth Information
changes in the dielectric constant, very large areas
of water, for instance, can be detected (Sippel
et al. 1998; Jin 1999) but their uncertainties may
be large (Papa et al. 2006). Imagery from (active)
SAR seems at present to be the only reliable source
of information for monitoring floods on rivers
<
1 km in width. Although the operational use of
SAR images for flood data retrieval is currently
still limited by restricted temporal coverage (up to
35 days for some sensors), recent efforts on satel-
lite constellations (e.g. COSMO-SkyMed) seem
promising and should make space-borne SAR
an indispensable tool for hydrological/hydraulic
studies in the near future.
Many different SAR image-processing techni-
ques exist to derive flood area and/or extent. They
range from simple visual interpretation (e.g. Ober-
stadler et al. 1997) and image histogram threshold
(Otsu 1979) or texture measures, to automatic
classification algorithms (e.g. Hess et al. 1995;
Bonn and Dixon 2005) or multi-temporal change
detection methods (e.g. Calabresi 1995), of which
extensive reviews are provided in Liu et al. (2004)
and Lu et al. (2004). Image statistics-based active
contour models (Mason and Davenport 1996;
Horritt 1999) have been used by some authors to
successfully extract a flood shoreline, for which
Mason et al. (2007b) have proposed an improve-
ment based on LIDAR DEM constraining.
Classification accuracies of flooded areas (most
of the time defined as a ratio of the total area of
interest where classification errors are omitted)
vary considerably and only in rare cases exceed
90%. Interpretation errors (i.e. dry areasmapped as
flooded and vice versa) may arise from a variety
of sources: inappropriate image-processing
algorithm, altered backscatter characteristics,
unsuitable wavelength and/or polarizations,
unsuccessful multiplicative noise (i.e. speckle)
filtering, remaining geometric distortions, and
inaccurate image geocoding. Horritt et al. (2001)
state that wind roughening and the effects of
protruding vegetation, both of whichmay produce
significant pulse returns, complicate the imaging
of the water surface. Moreover, due to the corner
reflection principle (Rees 2001) in conjunction
with its coarse resolution, currently available SAR
Flood extent mapping
Given the very high spatial resolution of the
imagery, flood extent is derived from colour or
panchromatic aerial photography by digitizing the
boundaries at the contrasting land-water inter-
face. The accuracy of the derived shoreline may
vary from 10 to 100m, depending largely on the
skills of the photo interpreter, of which the geor-
ectification error is generally 5m with
10%
chance of exceeding that error (Hughes et al. 2006).
In recent years, however, mapping flood area
and extent fromsatellite images has clearly gained
in popularity, mostly owing to their relatively low
post-launch acquisition cost. Following a survey
of hydrologists, Herschy et al. (1985) determined
that the optimum resolution for floodplain map-
pingwas 20m, while that for flood extentmapping
was 100m (max. 10m, min. 1 km) (Blyth 1997).
Clearly, most currently available optical, thermal
as well as active microwave sensors satisfy these
requirements (Bates et al. 1997; Smith 1997;
Marcus and Fonstad 2008; Schumann et al.
2009). Flood mapping with optical and thermal
imagery has met with some success (Marcus and
Fonstad 2008); however, the systematic applica-
tion of such techniques is hampered by persistent
cloud cover during floods, particularly in small to
medium-sized catchments where floods often
recede before weather conditions improve. Also,
the inability to map flooding beneath vegetation
canopies, as demonstrated by, for example, Hess
et al. (1995, 2003) and Wilson et al. (2007) with
radar imagery, limits the applicability of these
sensors. Given these limitations for acquiring
flood information routinely, flood detection and
monitoring seems realistically only feasible with
microwave (i.e. radar) remote sensing, as micro-
waves penetrate cloud cover and are reflected
away from the sensor by smooth open water
bodies.
The use of passive microwave systems over
land surfaces is difficult given their large spatial
resolutions of 20 to 100 km (Rees 2001). Interpre-
tation of the wide range of materials with many
different emissivities is thus rendered nearly im-
possible. Nevertheless, as the sensor is sensitive to
