Full text: Proceedings of the Symposium on Global and Environmental Monitoring (Pt. 1)

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. 
Anderson, M.R., 1987a. The Onset of Spring Melt in First-Year Ice 
Regions of the Arctic as Determined from SMMR Data for 1979 
and 1980. Journal of Geophysical Research. (In Press for December 
Anderson, M.R., 1987b. Snow Melt on Sea Ice Surfaces as 
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Barber, David G., J. Douglas Dunlop, Joseph M. Piwowar, and 
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