Objective
To develop a knowledge based decision support system which brings
to bear ground and spaceborne multisensor data, together with
expert knowledge about desertification processes, on the problem
of desertification risk assessment after forest fires, with
particular relevance to the Mediterranean countries of the
Community.
The basic approch is to develop a system using a modern GIS shell
for the integration of various moduli. Input to the system will
be the major parameters that govern the natural forest
regeneration, and soil erosion, remotely sensed data and field
observations. The output of the system will consist of a set of
inferences and maps showing the degree of risk of
desertification associated with each area.
A rule base will be incorporated into the system, constructed
from modelling the physical processes that contribute to soil
degradation.
This rule database used with the ground and remotely sensed data
will give the probabilities for soil erosion and
desertificaion, and of natural regeneration. The ground data
used will refer to 1 : 50 000 topographic maps and will include
vegetation cover, soil, erosion and land use data. Further, data
from existing GIS will include topographical, geological and
elevation data. A database will be constructed to incorporate the
above mentioned data .
The relevance of the remotely sensed data (ERS--1 SAR, SPOT
panchromatic, Landsat Thematic Mapper)
will be studied by investigating the statistical correlations
between reflectance and backscattering values with specific
states of soil erosion and natural regeneration. A library of
functions will be build for
image segmentation and relevant feature extraction. Segmentation
techniques combining edge and region information will be
investigated. Multiband texture analysis will be used . The
invocation of the appropriate algorithms will be knowledge
controlled, using information about the time of image
acquisition. The image classification
will be done using Bayesian decision theory. Two approaches will
be investigated: either combining all sources of information at
the data level, or interpreting the data from each source first
and combining them at the symbolic level afterwards.
Finally, the performance of the system will be evaluated by
comparing its output risk maps with conventional maps, to assess
positional and labelling accuracy as well as cost effectiveness.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences databases
- natural sciences earth and related environmental sciences physical geography cartography geographic information systems
- natural sciences computer and information sciences artificial intelligence computer vision image recognition
- natural sciences earth and related environmental sciences soil sciences soil management
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Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
11528 ATHENS
Greece
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.