1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Spatial Modelling of the Terrestrial Environment: Outlook 265
erosion and Burke et al. show how vegetation cover not only affects soil moisture status
but also must be accounted for when soil moisture retrievals are conducted. For estimates
of snow pack properties (such as snow depth), Kelly et al. note that forest cover influences
the accuracy of retrievals from passive microwave instruments. In all of these examples,
small errors in vegetation parameter estimation lead to significant errors in the retrieved or
the modelled variable.
Vegetation characterization can be achieved in many ways. The most usual remote sensing
tool is the multispectral scanning instrument such as Landsat or the Advanced Very High
Resolution Radiometer (AVHRR). Enhanced instruments have more recently been used for
vegetation mapping and potentially enable the improved characterization of different vegetation
parameters. For example, NASA’s Moderate Resolution Imaging Spectroradiometer
on board the Terra and Aqua platforms has at least twice the fineness of spatial resolution
of the AVHRR with a near daily repeat and 32 (mostly narrow) spectral bands in the visible
and infra-red part of the electromagnetic spectrum. This is ideal for classical vegetation
classification and leaf area index mapping. With better specified instruments for particular
science applications, and with the increased understanding of instrument synergisms
for particular applications (for example, combining optical reflectance signals with radar
canopy geometry models to produce vegetation canopy type condition and physical geometry),
more diverse and accurate vegetation parameters will undoubtedly be available for
modellers.
13.2.3 Spatial Resolution: Scales of Variation and Size of Support
Spatial resolution is a cross-cutting issue running throughout many of the book contributions
(especially in the chapters by Okin and Gillette, Kelly et al., Drake et al., Wooster
et al., Barr and Barnsley). It is a multi-dimensional issue that is important for several
different reasons. Spatial resolution refers to the spatial resolving power of the desired
activity. In the case of remote sensing it usually refers to the size of instantaneous field of
view of the sensor (which generally translates to a pixel) but in spatial modelling it could
easily refer to the cell size used for the modelling framework. Both definitions should be
determined for the modeller by the scale(s) of variation of the environmental variable under
consideration. However, spatial resolution of remote sensing instruments is technologically
determined and until recently, spatial resolution of the cell grid in numerical modelling has
been determined by the computational hardware available, i.e. processor speed, memory
and storage. With advancements in both computer and remote sensing technologies, the
spatial resolution issue is becoming controlled more by limitations in our understanding
of the scale of variation of an environmental variable than by technological limitations.
This is an important development because the scale of variation of an environmental variable
should control the way that modellers discretize space. In the past, the technological
constraints have determined the size of support (or spatial framework) used by the model
and have resulted in sometimes very inappropriate spatial models. We are now at a stage
of technological development when scientists can often define (at least theoretically) levels
of spatial resolution that can reflect the natural scale of variation of an environmental
variable. However, this statement assumes that scientists can accurately define the spatial
scale of variation of a desired variable but in many cases this is only possible with a high
degree of uncertainty. For example, in the chapter by Kelly et al., the scales of variation