Bayesian data fusion in environmental sciences : theory and applications
During the last thirty years, new technologies have contributed to a
drastic increase of the amount of data in environmental sciences.
Monitoring networks, remote sensors, archived maps and large databases are
just few examples of the possible information sources responsible for this
growing amount of information. For obvious reasons, it might be
interesting to account for all these information when dealing with a
space-time prediction/estimation context.
In environmental sciences, measurements are very often sampled scarcely
over space and time. Geostatistics is the field that investigates
variables in a space-time context. It includes a large number of methods
and approaches that all aim at providing space-time predictions (or
interpolations) for variables scarcely known in space and in time by
accounting for space-time dependance between these variables. As a
consequence, geostatistics methods are relevant when dealing with the
processing and the analysis of environmental variables in which space and
time play an important role.
As direct consequence of the increasing amount of data, there is an
important diversity in the information (e.g. different nature, different
uncertainty). These issues have recently motivated the emergence of the
concept of data fusion. Broadly speaking, the main objective of data
fusion methods is to deal with various information sources in such a way
that the final result is a single prediction that accounts for all the
sources at once. This enables thus to conciliate several and potentially
contradictory sources instead of having to select only one of them because
of a lack of appropriate methodology.
For most of existing geostatistics methods, it is quite difficult to
account for a potentially large number of different information sources at
once. As a consequence, one has often to opt for only one information
source among all the available sources. This of course leads to a
dramatic loss of information. In order to avoid such choices, it is thus
relevant to get together the concepts of both data fusion and
geostatistics in the context of environmental sciences.
The objectives of this thesis are (i) to develop the theory of a Bayesian
data fusion (BDF) framework in a space-time prediction context and (ii) to
illustrate how the proposed BDF framework can account for a diversity of
information sources in a space-time context. The method will thus be
applied to a few environmental sciences applications for which (i) crucial
available information sources are typically difficult to account for or
(ii) the number of secondary information sources is a limitation when
using existing methods.
Reproduced by permission of Springer. P. Bogaert and D. Fasbender (2007). Bayesian data fusion in a spatial prediction context: a general formulation. Stoch. Env. Res. Risk. A., vol. 21, 695-709. (Chap. 1).
© 2008 IEEE. Reprinted, with permission, from D. Fasbender, J. Radoux and P. Bogaert (2008). Bayesian data fusion for adaptable image pansharpening. IEEE Trans. Geosci. Rem. Sens., vol. 46, 1847-1857. (Chap. 3).
© 2008 IEEE. Reprinted, with permission, from D. Fasbender, D. Tuia, P. Bogaert and M. Kanevski (2008). Support-based implementation of Bayesian data fusion for spatial enhancement: applications to ASTER thermal images. IEEE Geosci. Rem. Sens. Letters, vol. 6, 598-602. (Chap. 4).
Reproduced by permission of American Geophysical Union. D. Fasbender, L. Peeters, P. Bogaert and A. Dassargues (2008). Bayesian data fusion applied to water table spatial mapping. Accepted for publication in Water Resour. Res. (Chap. 5).
School:Université catholique de Louvain
Source Type:Master's Thesis
Keywords:geostatistics multiple information sources data merging géostatistiques fusion de données multiples d
Date of Publication:11/17/2008