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complex problems must be solved to form the dynamical image of environmental
sub-system under the conditions of incomplete information. Basic
flaw of the GIS
technology is that it is not oriented to the multifaceted forecast of the state of the
monitoring object. In this regard, Krapivin and Varotsos (2007, 2008) proposed the
GIS modi
fl
cation by means of a combined use of GIS and modeling technologies.
The result of this attempt is the development of a new GIMS technology based on
the formula GIMS = GIS
∪
IMTEM. This GIMS technology eliminates many of the
above-said GIS
flaws and allows the establishment of monitoring systems that
display prognosis functions. The GIMS architecture is shown in Fig.
1.1
.
In principle, the required information may be obtained by using in-situ and
remote sensing measurements and by getting access to a-prior knowledge-based
data in the GIS databases. However, the following problems must be solved:
fl
what kind of instruments should be used to conduct the so-called ground-truth
and remote sensing measurements?
what is the cost to be paid for the in-situ and remote sensing information?
what kind of balance should be taken into account for the information content of
the in-situ and remote sensing observations along with their corresponding cost?
and
Fig. 1.1 Conceptual diagram showing the de
nition of the GIMS technology structure
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