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this expert system simply reports the results and 
does not try to automatically fix the misregistra 
tions. For our forestry application, the forest map 
is read into LDIAS and converted to grid format. 
The selected elements are converted to a symbolic 
location file. The remote sensing image is segmen 
ted and the segments are classified. These are used 
to produce a corresponding image symbolic location 
file and statistics file. The simplified MICE 
agenda from this point on is given in Table 7. 
Table 7. Simplified mice agenda 
1) Select map segment 
2) Find all image segments in 
map segment window (focus) 
3) Compare class value 
4) Compare segment sizes 
5) Compare segment shapes 
6) Compare segment locations 
7) Output results 
8) If not done, loop to (1). 
Experiments with MSS imagery, federal maps, and 
provincial maps indicate that shape and class values 
are the most important heuristics for correctly 
identifying areas of misregistration. The knowledge 
of how to fix these areas has yet to be developed. 
One might think that the map data should be tied to 
the geocoded remote sensing image. For the resource 
managers, this may be acceptable as a long-term 
goal. However, they have a customer base familiar 
with and using the existing resource maps. There 
fore, rules will need to be developed for the major 
types of misregistrations. A second problem with 
the MICE expert system is that it is very slow. The 
analysis of the 1:20,000 hydrography level of a 
BCMOFL map and a MSS image requires approximately 3 
hours on the AI VAXstation. Further experiments are 
now in progress. 
6 KEY ISSUES FOR THE FUTURE 
Expert systems are required to integrate remote 
sensing and geographic information systems for 
resource management with automatic methods. The 
complexities of the image analysis and geographic 
information systems are such that one should use 
cooperating, distributed expert systems. Much 
research and experimentation remain to be done. 
Many resource managers in Canada have been 
reluctant in the past to use satellite remote sens 
ing for resource management because of two issues. 
Firstly, their need to be assured that there will be 
a continuity of data. This need has been satisfied 
by CCRS arranging to receive data from the LANDSAT 
and SPOT satellites. Secondly, they desire that the 
same resource information can be derived from each 
data source. Their concerns include cost, accuracy, 
and timeliness. Expert systems can aid in the pro 
duction of the required information, but such sys 
tems cannot compensate for the physical limitations 
of the sensors. 
For environmental monitoring, it is necessary to 
access resource information stored usually in dif 
ferent organizations. Artificial intelligence 
methods are essential to achieve distributed, coop 
erating systems which do not require large numbers 
of highly skilled individuals. 
In the future, the users will not need as much 
computer expertise to achieve their resource manage 
ment goals. Their systems, though, will be more 
complex and the scientists and engineers which 
develop them will be more specialized. We have 
found that it takes approximately two years to make 
a knowledge engineer. 
Two other issues directly related to expert 
systems technology are: who owns the knowledge? and 
what if the expert systems are wrong? Experts are 
going to be reluctant to provide their knowledge if 
they think that their organization will eliminate 
their job as a result. They will require commit 
ments from their organization that this will not 
happen. If expert systems succeed in reproducing 
human performance in some of these limited domains, 
then such systems will likely make errors at times. 
The question of liability for such errors and their 
subsequent results must also be addressed by organ 
izations employing this technology. 
ACKNOWLEDGEMENTS 
The author is grateful for the cooperation of Mr. 
Frank Hegyi of the British Columbia Ministry of 
Forests and Lands. Messrs. John Zelek, Mike Robson, 
and Syd Dubrofsky aided in the preparation of the 
figures for this publication. 
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