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algorithm, and would be monitored by satellite UV 
sensors. The rules would allow the system to be scaled 
for local or regional applications, and might then be 
integrated into global models. 
The approach outlined in Table 1 conceptualizes how the 
internal complexities of natural genomes might be 
integrated with observable environmental attributes to 
simulate dynamical systems. It represents a logically 
structured "bit map" for coding and storing complex, 
interactive knowledge about an ecosystem as strings of 
binary data that could then be machine-processed in a 
virtual reality environment to monitor adaptive reactions. 
As new, or better knowledge became available, the 
cybergenes could be changed or lengthened; or, whole 
new cyberchromes could be added as data from different 
scientific areas became available. Perhaps the most 
important feature of such a genetic algorithm is that the 
rules and neural networks governing self-adaptive 
behavior would reduce the level of human bias now 
common in ecosystem models. 
Current ecosystem models are based largely on an 
assumption of linearity that accounts for neither 
environmental perturbations nor identifiable periodicities. 
Instead, they project evolutionary trajectories based on the 
straight-line portion of time functions representing 
"today", and which are assumed will continue far into the 
future. Indeed, there are many practicing professionals 
who are convinced that resource “management” equates 
to keeping everything as it is. 
In short, current models are not designed to self-adapt. 
They do not ride the fuzzy edge of Order and Chaos to 
maintain high levels of structure and function without 
sacrificing flexibility (Kauffman, 1993). Evolutionary 
trajectories of species and ecosystems are not random, but 
neither are they linear. Evolution is normally such a slow 
process that adaptive reactions are only observable after 
the fact. Some critical questions therefore are..."how can 
the science community gather and process data fast enough 
to drive a genetic algorithm?"; "how can fuzzy rules and 
artificial neural networks be incorporated to simulate an 
ecosystem's internal "learning" process?"; and, "how can 
routinely collected satellite environmental data be used in 
a time-dimension to visualize dynamic processes?" 
This last question is especially interesting for the remote 
sensing and GIS communities because modelers will have 
neither the variety nor the spatial continuity of in-situ data 
necessary to drive dynamical models over large areas. As 
an alternative to discontinuous, in-situ measurements, 
scientists can search remotely acquired sensor data for 
ecological and genetic information, and thus approach the 
exercise from satellite to ground. Pixel digital numbers 
(DNs) from time-series data would become "windows" 
through which genetic and ecologic systems are linked.. 
ROLE OF REMOTE SENSING AND GIS 
Genome maps and genetic networks provide the 
foundation for understanding how species respond and 
adapt to environmental stimuli. Another part of natural 
complexity involves measuring the attributes that 
constitute those stimuli, and understanding how they vary 
In time and space. Remote sensing is perhaps the best 
means for obtaining many of these data because satellite 
507 
sensors can collect uniform global data that are applicable 
at local scales and can thereby provide time series 
comparability. Similarly, airborne sensors collect fine 
resolution data at the local level but can be flown over 
analogous sites around the world to obtain comparable 
geographic data. For developing self-adaptive models, 
however, the greatest asset of satellite measurements is 
that they represent repetitive, synoptic, calibrated data 
sets that satisfy the criterion for "continuity" in rule-based 
models. 
Remotely acquired data come in both raster (image) and 
vector (sounder) formats depending upon sensor design 
and measurement technique. Both kinds are directly 
employable in Geographic Information Systems (GIS), 
which have proven their value as a means for managing 
and displaying relationships between data sets. 
Traditional data collected in the field will not be replaced 
by remotely acquired data because these, too, are directly 
employable as point, line, and polygon attributes. The 
combination of remote and ground-based data types 
constitute the universe of data that could reside in a 
relational database sandwiched between the object 
databases. All of these, in turn, would be employed to 
form a genetic algorithm that would describe an 
ecosystem. Figure 2 illustrates this design. 
  
Cultural & Physical Relational 
Milieu Databases 
  
Figure 2. Object and Relational Databases 
A critical requirement of any relational GIS is that all data 
sets be accurately co-registered. In past, this has been 
accomplished by geocorrecting images using ground 
control points and by improving the pointing accuracy and 
pixel-to-pixel registration of sensors. Another technique 
is to use Global Positioning Systems (GPS) capable of 
geolocating raster, point, line, and polygon features with 
accuracies in the centimeter to meter range. GPS is the 
most recent addition to the spatial data tool kit. It 
represents the enabling technology for merging data sets 
into spatially coherent systems, and offers the hope that at 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996