Geography Reference
In-Depth Information
Both methods described in this section are designed to produce visual outputs
for a single location at a specific moment in time. While the development of
animations can partially alleviate this issue and display dynamic processes, these
outputs offer little interactivity to the viewer, as all movement or changes in the
display need to be pre-processed. Each output being a static pre-processed image
also lacks the capacity to link-in additional layers of information (such as
numerical values of some of the variable displayed). The 360 degree panaroma
products offer the most interactivity, and hyperlinks can be further embedded to
assist navigation and exploration as reported in Pettit et al. (
2004
). However, this
functionality has not been implemented for the virtual Demo Dairy visualisation
product.
4.3 Advantages and Limitations of the Approach
The use of locally relevant climate change impact data is likely to assist local
stakeholders relate to possible future scenarios and therefore facilitate discussion
and community engagement. The combination of this localised data with 3D
photo-realistic multivariate visualisation may further assist stakeholders to
understand and explore climate change parameters and their likely impact at the
farm scale. Stakeholders may also obtain a more holistic understanding of the
likely impact and available adaptation options at the landscape scale.
However the use of localised data and 3D photo-realistic visualisation across
multiple regions presents two key challenges. Firstly, the development of 3D
realistic multivariate visualisation products requires significant manual input from
the 3D modelers. Although new tools are progressively becoming available to
automate some of the data import processes, the creation of 3D realistic visual-
isation is still a time consuming process into which computer rendering time needs
to be factored (the higher the level of detail the more time it takes to produce the
visualisation). Research is currently being conducted to automate the generation of
3D realistic visualisation by coupling GIS databases with computer game tech-
nology, see for example the Spatial Information Exploration Visualisation Envi-
ronment (SIEVE) (Stock et al.
2008
). Also the advances in digital globes such as
Google Earth are supporting more automated generation of 3D datasets, in near
real-time for any location and from a range of perspectives.
Secondly, there exists a limited data resource for local climate change impact
and projections. While some climate change models are being downscaled (Pettit
et al.
2010
) to provide high resolution outputs and more climate change impact
models are making use of those data, the amount of locally relevant data available
is still largely insufficient to provide an overall perspective of climate change
impact across all variables affecting farming systems. Consequently 3D photo-
realistic visualisation developed from this incomplete knowledge of projected
system changes may become subjective and potentially convey intentional or
unintentional bias from the developer. Therefore as reported by Sheppard (
2001
)