1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Flood Inundation Modelling Using LiDAR and SAR Data 87
difficult to disentangle. Certain components of the hydraulic resistance are, however, more
tractable. In particular, skin friction for in-channel flows is a strong function of bed material
grain size, and a number of relationships exist which express the resistance coefficient in
terms of the bed material median grain size, D 50 (e.g. Hey, 1979). Equally, on floodplain
sections, form drag due to vegetation is likely to dominate the resistance term (Kouwen,
2000). Determining the drag coefficient of vegetation is, however, rather complex, as the
frictional losses result from an interaction between plant biophysical properties and the
flow. For example, at high flows the vegetation momentum absorbing area will reduce
due to plant bending and flattening. To account for such effects Kouwen and Li (1980)
and Kouwen (1988) calculated the Darcy-Weisbach friction factor f for short vegetation,
such as floodplain grasses and crops, by treating such vegetation as flexible, and assuming
that it may be submerged or non-submerged. f is dependent on water depth and velocity,
vegetation height and a product MEI, where M is plant flexural rigidity in bending, E is
the stem modulus of elasticity and I is the stem area’s second moment of inertia. Whilst
MEI often cannot be measured directly, it has been shown to correlate well with vegetation
height (Temple, 1987).
Whilst bed material size is not amenable to analysis using remote sensing, a number of
technologies are capable of determining vegetation properties at a variety of scales. As this
is the principal component of frictional resistance on floodplains, the extent to which the
data derived from such techniques can be converted into hydraulic friction coefficients is a
key question. Optical remote sensing techniques are a standard methodology for vegetation
classification and a large body of research has built up in this area. Additionally, recent
research developments allow extraction of specific biophysical parameters from remotely
sensed data. Both may allow spatially variable hydraulic resistance to be automatically
mapped and are thus discussed below.
Vegetation Classification. A large number of satellite (AVHRR, Landsat MSS and TM,
SPOT) and airborne (CASI) sensors measure light reflected from a target in various
bands within the visible part of the electromagnetic spectrum. These sensors vary in
terms of their spatial resolution (∼1 km for AVHRR, ∼3 m for CASI) and the number
and width of the spectral bands resolved. A common property, however, is that
such data can be used to discriminate vegetation of different type and hence produce
a classified map of vegetation at various scales. The technique relies on the fact that
different earth surface features (e.g. bare ground, vegetation, etc.) differentially absorb
and reflect certain wavelengths of light and can thus be discriminated. This is now a
relatively well-established technique and for the UK and Europe a number of operational
products exist. In the UK, the Institute of Terrestrial Ecology (ITE, now Centre
for Ecology and Hydrology, Monks Wood) has produced a national land cover map at
25 m resolution from Landsat TM data (http://www.ceh.ac.uk/data/lcm/). This consists
of 25 vegetation classes whose spatial distribution was determined based on winter and
summer images. An updated version of the dataset was released in October 2001. This
dataset formed the UK contribution to the Europe-wide CORINNE land cover database
(http://www.ceh.ac.uk/subsites/corine/backgr.html; http://etc.satellus.se/) which amalgamated
pixels in the ITE data to a slightly coarser resolution (1:100 000) and disaggregated
them into slightly more classes (44) in conjunction with further contextual datasets. Whilst