1 Spatial Modelling of the Terrestrial Environment - Georeferencial

200 Spatial Modelling of the Terrestrial Environment
10 years), the course level of spatial aggregation often employed, their inability to describe
directly the general physical form and land use function of urban areas, and their poor
coverage or lack of availability in many developing nations (Openshaw, 1995).
Potentially, recent developments in sensor technology within field Earth observation allow
a number of the above-mentioned concerns to be addressed. For example, very high
spatial resolution (2–4 m) optical satellite images (i.e., IKONOS and QuickBird) and high
spatial-density airborne LiDAR (Light Detection and Ranging) data, potentially allow accurate,
consistent and timely information on the physical form and land use organization
of urban areas to be obtained at scales of between 1:10 000 and 1:25 000 (Ridley et al.,
1997). However, if the full potential of Earth-observed images for studying and modelling
urban systems is to be realized, sensor development needs to be matched not only by improved
approaches to the inference of policy-relevant information, but also by an improved
integration of such information into the current computational methodologies employed in
urban system modelling (Donnay et al., 2001).
In this section on urban systems two studies are presented that address the above observation.
In the chapter by Barr and Barnsley (Chapter 10) the spatial topological structure
of urban land cover information, such as one may typically try to derive directly from very
high spatial resolution remotely sensed images, is studied in order to ascertain whether it
provides a relatively simple means by which to infer land use. They show for the urban
area under investigation, that while the spatial topology of land cover allows the broad
land use categories present to be distinguished (e.g. residential versus industrial), it does
not allow a more detailed land use typology to be characterized (e.g. different types of
residential development). The chapter by Devereux et al. (Chapter 11) demonstrates how
Earth-observed information and UK census data can be integrated in order to spatially
model sustainable traffic emission scenarios for the county of Cambridgeshire, UK. They
demonstrate that land cover information derived from a Landsat-TM image can be used to
derive traffic emission coefficients for the parameterization of a spatial interaction model
of traffic emissions. Moreover, the utility of LiDAR data for visualizing the relationship
between modelled traffic emissions and urban density is also demonstrated.
References
Batty, M. and Longley, P.A., 1994, Fractal Cities: A Geometry of Form and Function
(London: Academic Press).
Donnay, J.P., Barnsley, M.J. and Longley, P.A., 2001, Remote sensing and urban analysis, in
J.P. Donnay, M.J. Barnsley, and P.A. Longley (eds), Remote Sensing and Urban Analysis
(London: Taylor and Francis), pp. 3–18.
Harrison, P., and Pearce, F., 2000, AAAS Atlas of Population and Environment (Berkeley,
California: University of California Press).
Openshaw, S., 1995, The future of the census, in S. Openshaw (ed.), Census Users Handbook
(Cambridge: Geoinformation International) pp. 389–411.
Ridley, H., Atkinson, P. M., Aplin, P., Muller, J.-P., and Dowman, I., 1997, Evaluating the
potential of forthcoming commercial U.S. high-resolution satellite sensor imagery at the
Ordnance Survey, Photogrammetric Engineering and Remote Sensing, 63, 997–1005.
Wilson, A.G., 2000, Complex Spatial Systems: The Modelling Foundations of Urban and
Regional Planning (London: Prentice Hall).