1 Spatial Modelling of the Terrestrial Environment - Georeferencial

92 Spatial Modelling of the Terrestrial Environment
surveys will provide data in a format suitable for the correction to be applied. Additional
research may also generate the ability to retrieve further biophysical parameters from
remotely sensed data. For example, a combination of LiDAR-derived vegetation height
estimates and vegetation classification from optical systems such as CASI has the potential
to further reduce uncertainty in estimates of vegetation properties, but this has yet to be
investigated.
5.3 Integration of Spatial Data with Hydraulic Models
The above data sources typically require further manipulation before they can be directly
incorporated into hydraulic models. New data sources are also a stimulus for additional
technical developments of both parameterizations and models. These integration studies
and developments are explored in the following section.
5.3.1 High Resolution Topographic Data
LiDAR can be regarded as a largely operational technique that can be integrated with a
variety of standard hydraulic models. Indeed, its use is now relatively standard in the UK
in the creation of indicative floodplain maps. The UK Environment Agency is obliged
by Section 105 of the 1991 Water Resources Act to determine the extent of flooding
resulting from the 1-in-100-year recurrence interval flood. Such studies are typically accomplished
with a one-dimensional code as outlined above, with the model calibrated
and validated against bulk hydrometric data as outlined in section 5.1. Marks and Bates
(2000) have also explored the integration of LiDAR data with two-dimensional finiteelement
models. Whilst LiDAR topographic accuracy has been validated against ground
truth data, no studies have been conducted to examine the impact of LiDAR or other
topographic data sources (and their error structures) on model predictions. Hence, although
we know that LiDAR is subject to r.m.s. errors in the region of 15 cm as opposed
to