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time sequences of these signatures might permit analyses
to link object databases with relational coverages of other
physical parameters.
Sensing Programs
The future of remote sensing data collection is ensured by
needs recognized by Heads-of State and by the fact that
both government and commercial interests in the
technology are driving development costs downward. In
the next two years, NASA plans to inaugurate its Earth
Observing System (EOS). It is designed as a fifteen-year
series of low Earth orbiting satellites collecting
reflectance, emission, and absorption spectra for
atmospheric, hydrospheric and biospheric phenomena.
Similarly, the European Space Agency (ESA) plans to
launch the Environmental Satellite (ENVISAT) and the
Japanese will launch their Advanced Earth Observing
Satellite (ADEOS). All are multinational research and
applications missions aimed at future operational systems.
Many other nations also are contributing smaller satellites
and sensors for more specific data collection programs.
By 2010, there are scheduled to be more than 60 polar
orbiting and geostationary satellites gathering Earth data.
Sadly, there is not yet a broadly scoped program to
integrate these data with ground-based and object data into
holistic models of ecosystem evolution. In the authors’
view it is time to begin discussing and planning these
system integrations.
Data Integration
System and data integration will necessarily involve
reanalysis of the datasets themselves. Even though data
are calibrated for detector sensitivity, sensor, drift, and
other aberrations within a sensor’s lifetime, the entire
collection of data from a suite of like sensors operating
over long time frames need to be systematically calibrated
between sensors.
Another aspect of integration will involve substituting real
data, where possible, for mathematically derived values in
current models. With specific regard to global circulation
models, the new long-term data sets from EOS and
supplementary data sets now being developed that span the
current century could provide critical testbeds for
improving the accuracy of climate models. Progress in
model development and improvement can be accelerated
by comparisons of this kind, and through the continued
intercomparison of the results of different models.
Most important of all in the integration process, however,
IS the need to develop strategies that link remotely
acquired data about the environment to the genetic
networks of individuals and genotypes, and to then
program how species ought to react to changing
environments. For example, measurements of ultraviolet
radiation flux gain their greatest application today as a
predictor of ozone concentrations related to the ozone
hole. Those same measurements, and others like them,
may be useful as stimuli for biological mutations and
Species evolution, especially in high altitude areas.
CONCLUSIONS
Rapid. scientific progress is being made in genome
509
characterization, gene response mechanisms, learning
processes, and the complexity of self-organizing systems.
Remote sensor data sets, employed in raster and vector
GIS and that integrated genome and atmospheric data as
objects, could provide the framework for a genetic
algorithm. As these databases began to assemble and
integrate a sufficiently wide diversity of object and
relational variables, rules could be developed directing
their interactions based on field and laboratory
observations. The complexity of hundreds of plant species
interacting as individuals and as communities will never
be fully understood, but perhaps society can learn enough
about how ecosystems operate to at least emulate how they
will react to human and natural stimuli.
REFERENCES
Carpenter, S.R., S.W. Chisholm, C.J. Krebs, D.W.
Schindler, and R.F. Wright, 1995. Ecosystem
Experiments, Science, 269, pp. 324-327.
Eastman, J.R. and M. Fulk. 1993. Long Sequence Time
Series Evaluation Using Standardized Principal
Components. Photogrammetric Engineering and Remote
Sensing, 58(8), pp. 1307-1312.
Kauffman, S. 1993. The Origins of Order: Self-
Organization and Selection in Evolution. New York:
Oxford University Press. 709pgs.
Morain, S.A. 1993. Emerging technology for Biological
Data Collection and Analysis. Annals, Missouri Botanical
Gardens, 80 (2), pp.309-316.
Morain, S.A. and A.M. Budge. 1996. Earth Observing
Platforms and Sensors. Vol. 1 of Manual of Remote
Sensing (R.A. Ryerson, editor-in-chief). Bethesda, MD:
American Society for Photogrammetry and Remote
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NOAA/NESDIS. 1992. Global Change Database: Volume
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Stolum, H-H. 1996. River Meandering as a Self-
Organization Process. Science, 271, pp. 1710-1713.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996
