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Proceedings of the Symposium on Global and Environmental Monitoring

We are developing a data integration module which allows us to
transparently exchange spatial data between several different systems
(Piwowar, Joyce and LeDrew, 1990a and 1990b). This program
differs from the conversion programs found on existing systems in
two ways. First, it is internal to the processing system; not part of
a data exchange module. This means that its functions would not
be controlled by the user, but by the system itself as it assembles
the data necessary to satisfy an analysis request. Secondly, it would
not need to create new files: data which are converted would be
immediately made available to the analysis procedure, as if they
originated in the system's own database.
Our data integration module must also be able to resolve scale and
resolution differences between its files. Clearly, while there may be
some question to the validity of combining SSM/I data of 30 km
resolution with 10 m resolution of SAR imagery, the system must
be capable of making these types of scale changes. Dahlberg
(1986), Fung and LeDrew (1987), and De Cola (1989) provide a
variety of perspectives on multi-scale processing. The inherent
accuracy of data sets is an issue which is frequently avoided when
using a spatial processing system. Smith, et al. (1987),
Goodenough (1988), and Stayaert (1989) identify some of the
accuracy issues which must be examined when combining
databases. The SIIS data integration module will incorporate many
of these ideas.
4.2 Multi-Temporal Data Sets
To address the issues of "change", SIIS must have access to
historical, as well as current, information. While much of the sea
ice data collected in the past decade has been retained in a digital
form, earlier data sets can be found only in paper maps or tables.
Of particular interest to this study are the composite ice charts
which have been manually drafted by the Ice Centre of Environment
Canada for over thirty years. These maps show the dominant ice
types, concentrations and thicknesses on a weekly basis for much of
Canada's coastal waters. Searching for trends in ice conditions in
the paper charts is necessarily problematic. We are presently
developing an ice chart digitizing system which will capture the
information from the charts. Our innovative design uses a semi-
automated heads-up digitizing scheme on a scanned map.
The role of temporal modelling is a relatively new topic in spatial
data processing. Initial work done by Langran and Chrisman
(1988), Langran (1989a) and (1989b), and Price (1989) indicate that
slicing through temporal data layers can yield as new information as
slicing through thematic layers. This "4th dimension" modelling
will be incorporated within SIIS as a tool for detecting and
monitoring climatic variability.
4.3 Flexibility
One of the key design principles of SIIS is that it will be an
extremely flexible and versatile tool for the research scientist. This
requires that its user interface be as intuitive as possible and that its
functionality be easily modified. The first requirement is satisfied
by employing a menu-driven graphical user interface. This type of
environment has a fast learning curve and, if well designed, can
speed up the work of experienced users (Barber, et al., 1989).
Flexible functionality within SIIS will be handled by the design
philosophy to make as much use of existing technology as
possible. We note that many of the routine data analysis functions
of information systems are readily available from a variety of
sources. If a required function for SIIS already exists as part of
another program, then (transparent) links will be built to that
function rather than redeveloping it within SIIS. This approach
will encourage a modular design strategy and enable function
substitution and/or replacement if better programs are developed.
We have provided an overview of the Sea Ice Monitoring Site
(SIMS) in Lancaster Sound. This surface validation site will
provide us with information required to link Sea Ice and climate
variables and to develop the required methodologies to extract these
data from multi-sensor remote measurements. The science issues
being addressed with SIMS were presented. The three-phase
measurement concept of: SAR for surface structure; visual
wavelength for reflected radiative components; and thermal IR for
emitted components was introduced.
We reviewed the requirements of a spatial analysis system for the
integration of data from the SIMS project with in situ observations.
We also described how this same system could be implemented to
allow integration of remote sensing imagery into climate modelling
simulation studies and how integration with historical ice
information could provide a means of measuring climate change
using Sea Ice as an indicator.
The real task in development of SIIS is in development of a spatial
analysis system which can improve our capabilities to conduct
research with these data. A requirement of ‘change’ studies is that
quantitative information be utilized so that differences between
‘variation’ and ‘change’ can be precisely measured. Dealing with
the spatial variables, inherent in remote sensing imagery of the ice
surface, will require extensive spatial statistical analysis capabilities
and efficient data integration technology.
These problems are by no means new but their relevance to remote
sensing and functional development of SIIS are particularly acute.
We are in an era of information overload. A particularly relevant
statement was, not so recently, made by a geologist who works in
the field of spatial analysis (Davis 1973).
"In turn, geologists must learn to quantify and systematize
their natural recognition skills so that machines can be
taught to assume some of the burden for them. If this is
not done, we will be literally buried under the reams of
charts, maps, and photographs returned from the resources
survey satellites, orbiting geophysical platforms, and other
exotic tools of the future".
The exotic tools are here; the planet is facing increasing
environmental pressures due to human occupation; and the machines
which can assume some of the data integration and interpretation are
readily available. We are however still a long way from realizing
the capabilities expressed 17 years ago. We hope that through
integration of SIMS and SIIS we can develop a better understanding
of the floating ice regime and more effectively utilize historical,
current and future remote sensing imagery in our determination to
understand the Arctic floating ice regime.
This research was undertaken with the support of a Centre of
Excellence grant from the Province of Ontario to Dr. E. LeDrew
through the Institute for Space and Terrestrial Science (ISTS), and
an NSERC Operating Grant to Dr. E. LeDrew. The support of the
Waterloo Ice Group is greatfully acknowledged.
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